Deformable image registration as a tool to improve survival prediction after neoadjuvant chemotherapy for breast cancer: results from the ACRIN 6657/I-SPY-1 trial

NASA Astrophysics Data System (ADS)

Jahani, Nariman; Cohen, Eric; Hsieh, Meng-Kang; Weinstein, Susan P.; Pantalone, Lauren; Davatzikos, Christos; Kontos, Despina

2018-02-01

We examined the ability of DCE-MRI longitudinal features to give early prediction of recurrence-free survival (RFS) in women undergoing neoadjuvant chemotherapy for breast cancer, in a retrospective analysis of 106 women from the ISPY 1 cohort. These features were based on the voxel-wise changes seen in registered images taken before treatment and after the first round of chemotherapy. We computed the transformation field using a robust deformable image registration technique to match breast images from these two visits. Using the deformation field, parametric response maps (PRM) — a voxel-based feature analysis of longitudinal changes in images between visits — was computed for maps of four kinetic features (signal enhancement ratio, peak enhancement, and wash-in/wash-out slopes). A two-level discrete wavelet transform was applied to these PRMs to extract heterogeneity information about tumor change between visits. To estimate survival, a Cox proportional hazard model was applied with the C statistic as the measure of success in predicting RFS. The best PRM feature (as determined by C statistic in univariable analysis) was determined for each of the four kinetic features. The baseline model, incorporating functional tumor volume, age, race, and hormone response status, had a C statistic of 0.70 in predicting RFS. The model augmented with the four PRM features had a C statistic of 0.76. Thus, our results suggest that adding information on the texture of voxel-level changes in tumor kinetic response between registered images of first and second visits could improve early RFS prediction in breast cancer after neoadjuvant chemotherapy.

Data analysis in emission tomography using emission-count posteriors

NASA Astrophysics Data System (ADS)

Sitek, Arkadiusz

2012-11-01

A novel approach to the analysis of emission tomography data using the posterior probability of the number of emissions per voxel (emission count) conditioned on acquired tomographic data is explored. The posterior is derived from the prior and the Poisson likelihood of the emission-count data by marginalizing voxel activities. Based on emission-count posteriors, examples of Bayesian analysis including estimation and classification tasks in emission tomography are provided. The application of the method to computer simulations of 2D tomography is demonstrated. In particular, the minimum-mean-square-error point estimator of the emission count is demonstrated. The process of finding this estimator can be considered as a tomographic image reconstruction technique since the estimates of the number of emissions per voxel divided by voxel sensitivities and acquisition time are the estimates of the voxel activities. As an example of a classification task, a hypothesis stating that some region of interest (ROI) emitted at least or at most r-times the number of events in some other ROI is tested. The ROIs are specified by the user. The analysis described in this work provides new quantitative statistical measures that can be used in decision making in diagnostic imaging using emission tomography.

Assessment of Intervertebral Disc Degeneration Based on Quantitative MRI Analysis: an in vivo study

PubMed Central

Grunert, Peter; Hudson, Katherine D.; Macielak, Michael R.; Aronowitz, Eric; Borde, Brandon H.; Alimi, Marjan; Njoku, Innocent; Ballon, Douglas; Tsiouris, Apostolos John; Bonassar, Lawrence J.; Härtl, Roger

2015-01-01

Study design Animal experimental study Objective To evaluate a novel quantitative imaging technique for assessing disc degeneration. Summary of Background Data T2-relaxation time (T2-RT) measurements have been used to quantitatively assess disc degeneration. T2 values correlate with the water content of inter vertebral disc tissue and thereby allow for the indirect measurement of nucleus pulposus (NP) hydration. Methods We developed an algorithm to subtract out MRI voxels not representing NP tissue based on T2-RT values. Filtered NP voxels were used to measure nuclear size by their amount and nuclear hydration by their mean T2-RT. This technique was applied to 24 rat-tail intervertebral discs’ (IVDs), which had been punctured with an 18-gauge needle according to different techniques to induce varying degrees of degeneration. NP voxel count and average T2-RT were used as parameters to assess the degeneration process at 1 and 3 months post puncture. NP voxel counts were evaluated against X-ray disc height measurements and qualitative MRI studies based on the Pfirrmann grading system. Tails were collected for histology to correlate NP voxel counts to histological disc degeneration grades and to NP cross-sectional area measurements. Results NP voxel count measurements showed strong correlations to qualitative MRI analyses (R2=0.79, p<0.0001), histological degeneration grades (R2=0.902, p<0.0001) and histological NP cross-sectional area measurements (R2=0.887, p<0.0001). In contrast to NP voxel counts, the mean T2-RT for each punctured group remained constant between months 1 and 3. The mean T2-RTs for the punctured groups did not show a statistically significant difference from those of healthy IVDs (63.55ms ±5.88ms month 1 and 62.61ms ±5.02ms) at either time point. Conclusion The NP voxel count proved to be a valid parameter to quantitatively assess disc degeneration in a needle puncture model. The mean NP T2-RT does not change significantly in needle-puncture induced degenerated IVDs. IVDs can be segmented into different tissue components according to their innate T2-RT. PMID:24384655

Voxel-based statistical analysis of cerebral blood flow using Tc-99m ECD brain SPECT in patients with traumatic brain injury: group and individual analyses.

PubMed

Shin, Yong Beom; Kim, Seong-Jang; Kim, In-Ju; Kim, Yong-Ki; Kim, Dong-Soo; Park, Jae Heung; Yeom, Seok-Ran

2006-06-01

Statistical parametric mapping (SPM) was applied to brain perfusion single photon emission computed tomography (SPECT) images in patients with traumatic brain injury (TBI) to investigate regional cerebral abnormalities compared to age-matched normal controls. Thirteen patients with TBI underwent brain perfusion SPECT were included in this study (10 males, three females, mean age 39.8 +/- 18.2, range 21 - 74). SPM2 software implemented in MATLAB 5.3 was used for spatial pre-processing and analysis and to determine the quantitative differences between TBI patients and age-matched normal controls. Three large voxel clusters of significantly decreased cerebral blood perfusion were found in patients with TBI. The largest clusters were area including medial frontal gyrus (voxel number 3642, peak Z-value = 4.31, 4.27, p = 0.000) in both hemispheres. The second largest clusters were areas including cingulated gyrus and anterior cingulate gyrus of left hemisphere (voxel number 381, peak Z-value = 3.67, 3.62, p = 0.000). Other clusters were parahippocampal gyrus (voxel number 173, peak Z-value = 3.40, p = 0.000) and hippocampus (voxel number 173, peak Z-value = 3.23, p = 0.001) in the left hemisphere. The false discovery rate (FDR) was less than 0.04. From this study, group and individual analyses of SPM2 could clearly identify the perfusion abnormalities of brain SPECT in patients with TBI. Group analysis of SPM2 showed hypoperfusion pattern in the areas including medial frontal gyrus of both hemispheres, cingulate gyrus, anterior cingulate gyrus, parahippocampal gyrus and hippocampus in the left hemisphere compared to age-matched normal controls. Also, left parahippocampal gyrus and left hippocampus were additional hypoperfusion areas. However, these findings deserve further investigation on a larger number of patients to be performed to allow a better validation of objective SPM analysis in patients with TBI.

Medial prefrontal aberrations in major depressive disorder revealed by cytoarchitectonically informed voxel-based morphometry

PubMed Central

Bludau, Sebastian; Bzdok, Danilo; Gruber, Oliver; Kohn, Nils; Riedl, Valentin; Sorg, Christian; Palomero-Gallagher, Nicola; Müller, Veronika I.; Hoffstaedter, Felix; Amunts, Katrin; Eickhoff, Simon B.

2017-01-01

Objective The heterogeneous human frontal pole has been identified as a node in the dysfunctional network of major depressive disorder. The contribution of the medial (socio-affective) versus lateral (cognitive) frontal pole to major depression pathogenesis is currently unclear. The present study performs morphometric comparison of the microstructurally informed subdivisions of human frontal pole between depressed patients and controls using both uni- and multivariate statistics. Methods Multi-site voxel- and region-based morphometric MRI analysis of 73 depressed patients and 73 matched controls without psychiatric history. Frontal pole volume was first compared between depressed patients and controls by subdivision-wise classical morphometric analysis. In a second approach, frontal pole volume was compared by subdivision-naive multivariate searchlight analysis based on support vector machines. Results Subdivision-wise morphometric analysis found a significantly smaller medial frontal pole in depressed patients with a negative correlation of disease severity and duration. Histologically uninformed multivariate voxel-wise statistics provided converging evidence for structural aberrations specific to the microstructurally defined medial area of the frontal pole in depressed patients. Conclusions Across disparate methods, we demonstrated subregion specificity in the left medial frontal pole volume in depressed patients. Indeed, the frontal pole was shown to structurally and functionally connect to other key regions in major depression pathology like the anterior cingulate cortex and the amygdala via the uncinate fasciculus. Present and previous findings consolidate the left medial portion of the frontal pole as particularly altered in major depression. PMID:26621569

Depressive symptoms and white matter dysfunction in retired NFL players with concussion history.

PubMed

Strain, Jeremy; Didehbani, Nyaz; Cullum, C Munro; Mansinghani, Sethesh; Conover, Heather; Kraut, Michael A; Hart, John; Womack, Kyle B

2013-07-02

To determine whether correlates of white matter integrity can provide general as well as specific insight into the chronic effects of head injury coupled with depression symptom expression in professional football players. We studied 26 retired National Football League (NFL) athletes who underwent diffusion tensor imaging (DTI) scanning. Depressive symptom severity was measured using the Beck Depression Inventory II (BDI-II) including affective, cognitive, and somatic subfactor scores (Buckley 3-factor model). Fractional anisotropy (FA) maps were processed using tract-based spatial statistics from FSL. Correlations between FA and BDI-II scores were assessed using both voxel-wise and region of interest (ROI) techniques, with ROIs that corresponded to white matter tracts. Tracts demonstrating significant correlations were further evaluated using a receiver operating characteristic curve that utilized the mean FA to distinguish depressed from nondepressed subjects. Voxel-wise analysis identified widely distributed voxels that negatively correlated with total BDI-II and cognitive and somatic subfactors, with voxels correlating with the affective component (p < 0.05 corrected) localized to frontal regions. Four tract ROIs negatively correlated (p < 0.01) with total BDI-II: forceps minor, right frontal aslant tract, right uncinate fasciculus, and left superior longitudinal fasciculus. FA of the forceps minor differentiated depressed from nondepressed athletes with 100% sensitivity and 95% specificity. Depressive symptoms in retired NFL athletes correlate negatively with FA using either an unbiased voxel-wise or an ROI-based, tract-wise approach. DTI is a promising biomarker for depression in this population.

An Effective Post-Filtering Framework for 3-D PET Image Denoising Based on Noise and Sensitivity Characteristics

NASA Astrophysics Data System (ADS)

Kim, Ji Hye; Ahn, Il Jun; Nam, Woo Hyun; Ra, Jong Beom

2015-02-01

Positron emission tomography (PET) images usually suffer from a noticeable amount of statistical noise. In order to reduce this noise, a post-filtering process is usually adopted. However, the performance of this approach is limited because the denoising process is mostly performed on the basis of the Gaussian random noise. It has been reported that in a PET image reconstructed by the expectation-maximization (EM), the noise variance of each voxel depends on its mean value, unlike in the case of Gaussian noise. In addition, we observe that the variance also varies with the spatial sensitivity distribution in a PET system, which reflects both the solid angle determined by a given scanner geometry and the attenuation information of a scanned object. Thus, if a post-filtering process based on the Gaussian random noise is applied to PET images without consideration of the noise characteristics along with the spatial sensitivity distribution, the spatially variant non-Gaussian noise cannot be reduced effectively. In the proposed framework, to effectively reduce the noise in PET images reconstructed by the 3-D ordinary Poisson ordered subset EM (3-D OP-OSEM), we first denormalize an image according to the sensitivity of each voxel so that the voxel mean value can represent its statistical properties reliably. Based on our observation that each noisy denormalized voxel has a linear relationship between the mean and variance, we try to convert this non-Gaussian noise image to a Gaussian noise image. We then apply a block matching 4-D algorithm that is optimized for noise reduction of the Gaussian noise image, and reconvert and renormalize the result to obtain a final denoised image. Using simulated phantom data and clinical patient data, we demonstrate that the proposed framework can effectively suppress the noise over the whole region of a PET image while minimizing degradation of the image resolution.

Magnetic resonance imaging for monitoring therapeutic response in a transgenic mouse model of Alzheimer's disease using voxel-based analysis of amyloid plaques.

PubMed

Kim, Jae-Hun; Ha, Tae Lin; Im, Geun Ho; Yang, Jehoon; Seo, Sang Won; Chung, Julius Juhyun; Chae, Sun Young; Lee, In Su; Lee, Jung Hee

2014-03-05

In this study, we have shown the potential of a voxel-based analysis for imaging amyloid plaques and its utility in monitoring therapeutic response in Alzheimer's disease (AD) mice using manganese oxide nanoparticles conjugated with an antibody of Aβ1-40 peptide (HMON-abAβ40). T1-weighted MR brain images of a drug-treated AD group (n=7), a nontreated AD group (n=7), and a wild-type group (n=7) were acquired using a 7.0 T MRI system before (D-1), 24-h (D+1) after, and 72-h (D+3) after injection with an HMON-abAβ40 contrast agent. For the treatment of AD mice, DAPT was injected intramuscularly into AD transgenic mice (50 mg/kg of body weight). For voxel-based analysis, the skull-stripped mouse brain images were spatially normalized, and these voxels' intensities were corrected to reduce voxel intensity differences across scans in different mice. Statistical analysis showed higher normalized MR signal intensity in the frontal cortex and hippocampus of AD mice over wild-type mice on D+1 and D+3 (P<0.01, uncorrected for multiple comparisons). After the treatment of AD mice, the normalized MR signal intensity in the frontal cortex and hippocampus decreased significantly in comparison with nontreated AD mice on D+1 and D+3 (P<0.01, uncorrected for multiple comparisons). These results were confirmed by histological analysis using a thioflavin staining. This unique strategy allows us to detect brain regions that are subjected to amyloid plaque deposition and has the potential for human applications in monitoring therapeutic response for drug development in AD.

Selective activation around the left occipito-temporal sulcus for words relative to pictures: individual variability or false positives?

PubMed

Wright, Nicholas D; Mechelli, Andrea; Noppeney, Uta; Veltman, Dick J; Rombouts, Serge A R B; Glensman, Janice; Haynes, John-Dylan; Price, Cathy J

2008-08-01

We used high-resolution fMRI to investigate claims that learning to read results in greater left occipito-temporal (OT) activation for written words relative to pictures of objects. In the first experiment, 9/16 subjects performing a one-back task showed activation in > or =1 left OT voxel for words relative to pictures (P < 0.05 uncorrected). In a second experiment, another 9/15 subjects performing a semantic decision task activated > or =1 left OT voxel for words relative to pictures. However, at this low statistical threshold false positives need to be excluded. The semantic decision paradigm was therefore repeated, within subject, in two different scanners (1.5 and 3 T). Both scanners consistently localised left OT activation for words relative to fixation and pictures relative to words, but there were no consistent effects for words relative to pictures. Finally, in a third experiment, we minimised the voxel size (1.5 x 1.5 x 1.5 mm(3)) and demonstrated a striking concordance between the voxels activated for words and pictures, irrespective of task (naming vs. one-back) or script (English vs. Hebrew). In summary, although we detected differential activation for words relative to pictures, these effects: (i) do not withstand statistical rigour; (ii) do not replicate within or between subjects; and (iii) are observed in voxels that also respond to pictures of objects. Our findings have implications for the role of left OT activation during reading. More generally, they show that studies using low statistical thresholds in single subject analyses should correct the statistical threshold for the number of comparisons made or replicate effects within subject. (c) 2007 Wiley-Liss, Inc.

Selective Activation Around the Left Occipito-Temporal Sulcus for Words Relative to Pictures: Individual Variability or False Positives?

PubMed Central

Wright, Nicholas D; Mechelli, Andrea; Noppeney, Uta; Veltman, Dick J; Rombouts, Serge ARB; Glensman, Janice; Haynes, John-Dylan; Price, Cathy J

2008-01-01

We used high-resolution fMRI to investigate claims that learning to read results in greater left occipito-temporal (OT) activation for written words relative to pictures of objects. In the first experiment, 9/16 subjects performing a one-back task showed activation in ≥1 left OT voxel for words relative to pictures (P < 0.05 uncorrected). In a second experiment, another 9/15 subjects performing a semantic decision task activated ≥1 left OT voxel for words relative to pictures. However, at this low statistical threshold false positives need to be excluded. The semantic decision paradigm was therefore repeated, within subject, in two different scanners (1.5 and 3 T). Both scanners consistently localised left OT activation for words relative to fixation and pictures relative to words, but there were no consistent effects for words relative to pictures. Finally, in a third experiment, we minimised the voxel size (1.5 × 1.5 × 1.5 mm3) and demonstrated a striking concordance between the voxels activated for words and pictures, irrespective of task (naming vs. one-back) or script (English vs. Hebrew). In summary, although we detected differential activation for words relative to pictures, these effects: (i) do not withstand statistical rigour; (ii) do not replicate within or between subjects; and (iii) are observed in voxels that also respond to pictures of objects. Our findings have implications for the role of left OT activation during reading. More generally, they show that studies using low statistical thresholds in single subject analyses should correct the statistical threshold for the number of comparisons made or replicate effects within subject. Hum Brain Mapp 2008. © 2007 Wiley-Liss, Inc. PMID:17712786

White matter volume loss in amyotrophic lateral sclerosis: A meta-analysis of voxel-based morphometry studies.

PubMed

Chen, Guangxiang; Zhou, Baiwan; Zhu, Hongyan; Kuang, Weihong; Bi, Feng; Ai, Hua; Gu, Zhongwei; Huang, Xiaoqi; Lui, Su; Gong, Qiyong

2018-04-20

Structural neuroimaging studies of white matter (WM) volume in amyotrophic lateral sclerosis (ALS) using voxel-based morphometry (VBM) have yielded inconsistent findings. This study aimed to perform a quantitative voxel-based meta-analysis using effect-size signed differential mapping (ES-SDM) to establish a statistical consensus between published studies for WM volume alterations in ALS. The pooled meta-analysis revealed significant WM volume losses in the bilateral supplementary motor areas (SMAs), bilateral precentral gyri (PGs), left middle cerebellar peduncle and right cerebellum in patients with ALS, involving the corticospinal tract (CST), interhemispheric fibers, subcortical arcuate fibers, projection fibers to the striatum and cortico-ponto-cerebellar tract. The meta-regression showed that the ALS functional rating scale-revised (ALSFRS-R) was positively correlated with decreased WM volume in the bilateral SMAs, whereas illness duration was negatively correlated with WM volume reduction in the right SMA. This study provides a thorough profile of WM volume loss in ALS and robust evidence that ALS is a multisystem neurodegenerative disease that involves a variety of subcortical WM tracts extending beyond motor cortex involvement. Copyright © 2018 Elsevier Inc. All rights reserved.

How distributed processing produces false negatives in voxel-based lesion-deficit analyses.

PubMed

Gajardo-Vidal, Andrea; Lorca-Puls, Diego L; Crinion, Jennifer T; White, Jitrachote; Seghier, Mohamed L; Leff, Alex P; Hope, Thomas M H; Ludersdorfer, Philipp; Green, David W; Bowman, Howard; Price, Cathy J

2018-07-01

In this study, we hypothesized that if the same deficit can be caused by damage to one or another part of a distributed neural system, then voxel-based analyses might miss critical lesion sites because preservation of each site will not be consistently associated with preserved function. The first part of our investigation used voxel-based multiple regression analyses of data from 359 right-handed stroke survivors to identify brain regions where lesion load is associated with picture naming abilities after factoring out variance related to object recognition, semantics and speech articulation so as to focus on deficits arising at the word retrieval level. A highly significant lesion-deficit relationship was identified in left temporal and frontal/premotor regions. Post-hoc analyses showed that damage to either of these sites caused the deficit of interest in less than half the affected patients (76/162 = 47%). After excluding all patients with damage to one or both of the identified regions, our second analysis revealed a new region, in the anterior part of the left putamen, which had not been previously detected because many patients had the deficit of interest after temporal or frontal damage that preserved the left putamen. The results illustrate how (i) false negative results arise when the same deficit can be caused by different lesion sites; (ii) some of the missed effects can be unveiled by adopting an iterative approach that systematically excludes patients with lesions to the areas identified in previous analyses, (iii) statistically significant voxel-based lesion-deficit mappings can be driven by a subset of patients; (iv) focal lesions to the identified regions are needed to determine whether the deficit of interest is the consequence of focal damage or much more extensive damage that includes the identified region; and, finally, (v) univariate voxel-based lesion-deficit mappings cannot, in isolation, be used to predict outcome in other patients. Copyright © 2018 The Authors. Published by Elsevier Ltd.. All rights reserved.

Two efficient label-equivalence-based connected-component labeling algorithms for 3-D binary images.

PubMed

He, Lifeng; Chao, Yuyan; Suzuki, Kenji

2011-08-01

Whenever one wants to distinguish, recognize, and/or measure objects (connected components) in binary images, labeling is required. This paper presents two efficient label-equivalence-based connected-component labeling algorithms for 3-D binary images. One is voxel based and the other is run based. For the voxel-based one, we present an efficient method of deciding the order for checking voxels in the mask. For the run-based one, instead of assigning each foreground voxel, we assign each run a provisional label. Moreover, we use run data to label foreground voxels without scanning any background voxel in the second scan. Experimental results have demonstrated that our voxel-based algorithm is efficient for 3-D binary images with complicated connected components, that our run-based one is efficient for those with simple connected components, and that both are much more efficient than conventional 3-D labeling algorithms.

The SENSE-Isomorphism Theoretical Image Voxel Estimation (SENSE-ITIVE) Model for Reconstruction and Observing Statistical Properties of Reconstruction Operators

PubMed Central

Bruce, Iain P.; Karaman, M. Muge; Rowe, Daniel B.

2012-01-01

The acquisition of sub-sampled data from an array of receiver coils has become a common means of reducing data acquisition time in MRI. Of the various techniques used in parallel MRI, SENSitivity Encoding (SENSE) is one of the most common, making use of a complex-valued weighted least squares estimation to unfold the aliased images. It was recently shown in Bruce et al. [Magn. Reson. Imag. 29(2011):1267–1287] that when the SENSE model is represented in terms of a real-valued isomorphism, it assumes a skew-symmetric covariance between receiver coils, as well as an identity covariance structure between voxels. In this manuscript, we show that not only is the skew-symmetric coil covariance unlike that of real data, but the estimated covariance structure between voxels over a time series of experimental data is not an identity matrix. As such, a new model, entitled SENSE-ITIVE, is described with both revised coil and voxel covariance structures. Both the SENSE and SENSE-ITIVE models are represented in terms of real-valued isomorphisms, allowing for a statistical analysis of reconstructed voxel means, variances, and correlations resulting from the use of different coil and voxel covariance structures used in the reconstruction processes to be conducted. It is shown through both theoretical and experimental illustrations that the miss-specification of the coil and voxel covariance structures in the SENSE model results in a lower standard deviation in each voxel of the reconstructed images, and thus an artificial increase in SNR, compared to the standard deviation and SNR of the SENSE-ITIVE model where both the coil and voxel covariances are appropriately accounted for. It is also shown that there are differences in the correlations induced by the reconstruction operations of both models, and consequently there are differences in the correlations estimated throughout the course of reconstructed time series. These differences in correlations could result in meaningful differences in interpretation of results. PMID:22617147

Association between lesion location and language function in adult glioma using voxel-based lesion-symptom mapping.

PubMed

Banerjee, Pia; Leu, Kevin; Harris, Robert J; Cloughesy, Timothy F; Lai, Albert; Nghiemphu, Phioanh L; Pope, Whitney B; Bookheimer, Susan Y; Ellingson, Benjamin M

2015-01-01

Management of language difficulties is an important aspect of clinical care for glioma patients, and accurately identifying the possible language deficits in patients based on lesion location would be beneficial to clinicians. To that end, we examined the relationship between lesion presence and language performance on tests of receptive language and expressive language using a highly specific voxel-based lesion-symptom mapping (VLSM) approach in glioma patients. 98 adults with primary glioma, who were pre-surgical candidates, were administered seven neurocognitive tests within the domains of receptive language and expressive language. The association between language performance and lesion presence was examined using VLSM. Statistical parametric maps were created for each test, and composite maps for both receptive language and expressive language were created to display the significant voxels common to all tests within these language domains. We identified clusters of voxels with a significant relationship between lesion presence and language performance. All tasks were associated with several white matter pathways. The receptive language tasks were additionally all associated with regions primarily within the lateral temporal lobe and medial temporal lobe. In contrast, the expressive language tasks shared little overlap, despite each task being independently associated with large anatomic areas. Our findings identify the key anatomic structures involved in language functioning in adult glioma patients using an innovative lesion analysis technique and suggest that expressive language abilities may be more task-dependent and distributed than receptive language abilities.

Voxel-based morphometric brain comparison between healthy subjects and major depressive disorder patients in Japanese with the s/s genotype of 5-HTTLPR.

PubMed

Igata, Natsuki; Kakeda, Shingo; Watanabe, Keita; Ide, Satoru; Kishi, Taro; Abe, Osamu; Igata, Ryouhei; Katsuki, Asuka; Iwata, Nakao; Yoshimura, Reiji; Korogi, Yukunori

2017-06-21

Individuals with s/s genotype of serotonin transporter gene-linked promotor region (5-HTTLPR), which appear with a high frequency in Japanese, exhibit more diagnosable depression in relation to stressful life events than those with the s/l or l/l genotype. We prospectively investigated the brain volume changes in first-episode and medication naïve major depression disorder patients (MDD) with the s/s genotype in Japanese. We assessed the differences between 27 MDD with the s/s genotype and 44 healthy subjects (HS) with the same genotype using a whole-brain voxel-by-voxel statistical analysis of MRI. Gray matter volume in a brain region with significant clusters obtained via voxel-based morphometry analysis were measured and, as an exploratory analysis, evaluated for relationships to the subcategory scores (core, sleep, activity, psychic, somatic anxiety, delusion) of the Hamilton Depression Rating Scale (HAM-D) and the Social Readjustment Rating Scale (SRRS). The brain volume in the left insula lobe was significantly smaller in the MDD than in the HS. The left insula lobe volume correlated negatively with the "psychic" score of HAM-D and the SRRS. In a Japanese population with the s/s genotype, we found an atrophy of the insula in the MDD, which might be associated with "psychic" symptom and stress events.

Multiple imputation of missing fMRI data in whole brain analysis

PubMed Central

Vaden, Kenneth I.; Gebregziabher, Mulugeta; Kuchinsky, Stefanie E.; Eckert, Mark A.

2012-01-01

Whole brain fMRI analyses rarely include the entire brain because of missing data that result from data acquisition limits and susceptibility artifact, in particular. This missing data problem is typically addressed by omitting voxels from analysis, which may exclude brain regions that are of theoretical interest and increase the potential for Type II error at cortical boundaries or Type I error when spatial thresholds are used to establish significance. Imputation could significantly expand statistical map coverage, increase power, and enhance interpretations of fMRI results. We examined multiple imputation for group level analyses of missing fMRI data using methods that leverage the spatial information in fMRI datasets for both real and simulated data. Available case analysis, neighbor replacement, and regression based imputation approaches were compared in a general linear model framework to determine the extent to which these methods quantitatively (effect size) and qualitatively (spatial coverage) increased the sensitivity of group analyses. In both real and simulated data analysis, multiple imputation provided 1) variance that was most similar to estimates for voxels with no missing data, 2) fewer false positive errors in comparison to mean replacement, and 3) fewer false negative errors in comparison to available case analysis. Compared to the standard analysis approach of omitting voxels with missing data, imputation methods increased brain coverage in this study by 35% (from 33,323 to 45,071 voxels). In addition, multiple imputation increased the size of significant clusters by 58% and number of significant clusters across statistical thresholds, compared to the standard voxel omission approach. While neighbor replacement produced similar results, we recommend multiple imputation because it uses an informed sampling distribution to deal with missing data across subjects that can include neighbor values and other predictors. Multiple imputation is anticipated to be particularly useful for 1) large fMRI data sets with inconsistent missing voxels across subjects and 2) addressing the problem of increased artifact at ultra-high field, which significantly limit the extent of whole brain coverage and interpretations of results. PMID:22500925

Visual Exploration of Genetic Association with Voxel-based Imaging Phenotypes in an MCI/AD Study

PubMed Central

Kim, Sungeun; Shen, Li; Saykin, Andrew J.; West, John D.

2010-01-01

Neuroimaging genomics is a new transdisciplinary research field, which aims to examine genetic effects on brain via integrated analyses of high throughput neuroimaging and genomic data. We report our recent work on (1) developing an imaging genomic browsing system that allows for whole genome and entire brain analyses based on visual exploration and (2) applying the system to the imaging genomic analysis of an existing MCI/AD cohort. Voxel-based morphometry is used to define imaging phenotypes. ANCOVA is employed to evaluate the effect of the interaction of genotypes and diagnosis in relation to imaging phenotypes while controlling for relevant covariates. Encouraging experimental results suggest that the proposed system has substantial potential for enabling discovery of imaging genomic associations through visual evaluation and for localizing candidate imaging regions and genomic regions for refined statistical modeling. PMID:19963597

SU-D-202-03: Statistical Segmentation On Quantitative CT for Assessing Spatial Tumor Response During Radiation Therapy Delivery

DOE Office of Scientific and Technical Information (OSTI.GOV)

Schott, D; Chen, X; Klawikowski, S

2016-06-15

Purpose: Develop a method to segment regions of interest (ROIs) in tumor with statistically similar Hounsfield unit (HU) values and/or HU changes during chemoradiation therapy (CRT) delivery, to assess spatial tumor treatment response based on daily CTs during CRT delivery. Methods: Generate a three region map of ROIs with differential HUs, by sampling neighboring voxels around a selected voxel and comparing to the mean of the entire ROI using a t-test. The cumulative distribution function, P, is calculated from the t-test. The P value is assigned to be the value at the selected voxel, and this is repeated over allmore » voxels in the initial ROI. Three regions are defined as: (1-P) < 0.00001 (mid region), and 0.00001 < (1-P) (mean greater than baseline and mean lower than baseline). The test is then expanded to compare daily CT sets acquired during routine CT-guided RT delivery using a CT-on-rails. The first fraction CT is used as the baseline for comparison. We tested 15 pancreatic head tumor cases undergoing CRT, to identify the ROIs and changes corresponding to normal, fibrotic, and tumor tissue. The obtained ROIs were compared with MRI-ADC maps acquired pre- and post-CRT. Results: The ROIs in 13 out of 15 patients’ first fraction CTs and pre-CRT MRIs matched the general region and slices covered, as well as in 6 out of the 9 patients with post-CRT MRIs. The high HU region designated by the t-test was seen to correlate with the tumor region in MR, and these ROIs are positioned within the same region over the course of treatment. In patients with poorly delineated tumors in MR, the t-test was inconclusive. Conclusion: The proposed statistical segmentation technique shows the potential to identify regions in tumor with differential HUs and HU changes during CRT delivery for patients with pancreas head cancer.« less

Material Identification and Quantification in Spectral X-ray Micro-CT

NASA Astrophysics Data System (ADS)

Holmes, Thomas Wesley

The identification and quantification of all the voxels within a reconstructed microCT image was possible through making comparisons of the attenuation profile from an unknown voxel with precalculated signatures of known materials. This was accomplished through simulations with the MCNP6 general-purpose radiation-transport package that modeled a CdTe detector array consisting of 200 elements which were able to differentiate between 100 separate energy bins over the entire range of the emitted 110 kVp tungsten x-ray spectra. The information from each of the separate energy bins was then used to create a single reconstructed image that was then grouped back together to produce a final image where each voxel had a corresponding attenuation pro le. A library of known attenuation profiles was created for each of the materials expected to be within an object with otherwise unknown parameters. A least squares analysis was performed, and comparisons were then made for each voxel's attenuation profile in the unknown object and combinations of each possible library combination of attenuation profiles. Based on predetermined thresholds that the results must meet, some of the combinations were then removed. Of the remaining combinations, a voting system based on statistical evaluations of the fits was designed to select the most appropriate material combination to the input unknown voxel. This was performed over all of the voxels in the reconstructed image and a final resulting material map was produced. These material locations were then quantified by creating an equation of the response from several different densities of the same material and recording the response of the base library. This entire process was called the All Combinations Library Least Squares (ACLLS)analysis and was used to test several Different models. These models investigated a range of densities for the x-ray contrast agents of gold and gadolinium that can be used in many medical applications, as well as a range of densities of bone to test the ACLLS ability to be used with bone density estimation. A final test used a model with five different materials present within the object and consisted of two separate features with mixtures of three materials as gold, iodine and water, and another feature with gadolinium, iodine and water. The remaining four features were all mixtures of water with bone, gold, gadolinium, and iodine. All of the various material mixtures were successfully identified and quantified using the ACLLS analysis package within an acceptable statistical range. The ACLLS method has proven itself as a viable analysis tool for determining both the physical locations and the amount of all the materials present within a given object. This tool could be implemented in the future so as to further assist a team of medical practitioners in diagnosing a subject through reducing ambiguities in an image and providing a quantifiable solution to all of the voxels.

Probabilistic multiple sclerosis lesion classification based on modeling regional intensity variability and local neighborhood information.

PubMed

Harmouche, Rola; Subbanna, Nagesh K; Collins, D Louis; Arnold, Douglas L; Arbel, Tal

2015-05-01

In this paper, a fully automatic probabilistic method for multiple sclerosis (MS) lesion classification is presented, whereby the posterior probability density function over healthy tissues and two types of lesions (T1-hypointense and T2-hyperintense) is generated at every voxel. During training, the system explicitly models the spatial variability of the intensity distributions throughout the brain by first segmenting it into distinct anatomical regions and then building regional likelihood distributions for each tissue class based on multimodal magnetic resonance image (MRI) intensities. Local class smoothness is ensured by incorporating neighboring voxel information in the prior probability through Markov random fields. The system is tested on two datasets from real multisite clinical trials consisting of multimodal MRIs from a total of 100 patients with MS. Lesion classification results based on the framework are compared with and without the regional information, as well as with other state-of-the-art methods against the labels from expert manual raters. The metrics for comparison include Dice overlap, sensitivity, and positive predictive rates for both voxel and lesion classifications. Statistically significant improvements in Dice values ( ), for voxel-based and lesion-based sensitivity values ( ), and positive predictive rates ( and respectively) are shown when the proposed method is compared to the method without regional information, and to a widely used method [1]. This holds particularly true in the posterior fossa, an area where classification is very challenging. The proposed method allows us to provide clinicians with accurate tissue labels for T1-hypointense and T2-hyperintense lesions, two types of lesions that differ in appearance and clinical ramifications, and with a confidence level in the classification, which helps clinicians assess the classification results.

Structural covariance in the hallucinating brain: a voxel-based morphometry study

PubMed Central

Modinos, Gemma; Vercammen, Ans; Mechelli, Andrea; Knegtering, Henderikus; McGuire, Philip K.; Aleman, André

2009-01-01

Background Neuroimaging studies have indicated that a number of cortical regions express altered patterns of structural covariance in schizophrenia. The relation between these alterations and specific psychotic symptoms is yet to be investigated. We used voxel-based morphometry to examine regional grey matter volumes and structural covariance associated with severity of auditory verbal hallucinations. Methods We applied optimized voxel-based morphometry to volumetric magnetic resonance imaging data from 26 patients with medication-resistant auditory verbal hallucinations (AVHs); statistical inferences were made at p < 0.05 after correction for multiple comparisons. Results Grey matter volume in the left inferior frontal gyrus was positively correlated with severity of AVHs. Hallucination severity influenced the pattern of structural covariance between this region and the left superior/middle temporal gyri, the right inferior frontal gyrus and hippocampus, and the insula bilaterally. Limitations The results are based on self-reported severity of auditory hallucinations. Complementing with a clinician-based instrument could have made the findings more compelling. Future studies would benefit from including a measure to control for other symptoms that may covary with AVHs and for the effects of antipsychotic medication. Conclusion The results revealed that overall severity of AVHs modulated cortical intercorrelations between frontotemporal regions involved in language production and verbal monitoring, supporting the critical role of this network in the pathophysiology of hallucinations. PMID:19949723

Brain volumes in healthy adults aged 40 years and over: a voxel-based morphometry study.

PubMed

Riello, Roberta; Sabattoli, Francesca; Beltramello, Alberto; Bonetti, Matteo; Bono, Giorgio; Falini, Andrea; Magnani, Giuseppe; Minonzio, Giorgio; Piovan, Enrico; Alaimo, Giuseppina; Ettori, Monica; Galluzzi, Samantha; Locatelli, Enrico; Noiszewska, Malgorzata; Testa, Cristina; Frisoni, Giovanni B

2005-08-01

Gender and age effect on brain morphology have been extensively investigated. However, the great variety in methods applied to morphology partly explain the conflicting results of linear patterns of tissue changes and lateral asymmetry in men and women. The aim of the present study was to assess the effect of age, gender and laterality on the volumes of gray matter (GM) and white matter (WM) in a large group of healthy adults by means of voxel-based morphometry. This technique, based on observer-independent algorithms, automatically segments the 3 types of tissue and computes the amount of tissue in each single voxel. Subjects were 229 healthy subjects of 40 years of age or older, who underwent magnetic resonance (MR) for reasons other than cognitive impairment. MR images were reoriented following the AC-PC line and, after removing the voxels below the cerebellum, were processed by Statistical Parametric Mapping (SPM99). GM and WM volumes were normalized for intracranial volume. Women had more fractional GM and WM volumes than men. Age was negatively correlated with both fractional GM and WM, and a gender x age interaction effect was found for WM, men having greater WM loss with advancing age. Pairwise differences between left and right GM were negative (greater GM in right hemisphere) in men, and positive (greater GM in left hemisphere) in women (-0.56+/-4.2 vs 0.99+/-4.8; p=0.019). These results support side-specific accelerated WM loss in men, and may help our better understanding of changes in regional brain structures associated with pathological aging.

Methodological improvements in voxel-based analysis of diffusion tensor images: applications to study the impact of apolipoprotein E on white matter integrity.

PubMed

Newlander, Shawn M; Chu, Alan; Sinha, Usha S; Lu, Po H; Bartzokis, George

2014-02-01

To identify regional differences in apparent diffusion coefficient (ADC) and fractional anisotropy (FA) using customized preprocessing before voxel-based analysis (VBA) in 14 normal subjects with the specific genes that decrease (apolipoprotein [APO] E ε2) and that increase (APOE ε4) the risk of Alzheimer's disease. Diffusion tensor images (DTI) acquired at 1.5 Tesla were denoised with a total variation tensor regularization algorithm before affine and nonlinear registration to generate a common reference frame for the image volumes of all subjects. Anisotropic and isotropic smoothing with varying kernel sizes was applied to the aligned data before VBA to determine regional differences between cohorts segregated by allele status. VBA on the denoised tensor data identified regions of reduced FA in APOE ε4 compared with the APOE ε2 healthy older carriers. The most consistent results were obtained using the denoised tensor and anisotropic smoothing before statistical testing. In contrast, isotropic smoothing identified regional differences for small filter sizes alone, emphasizing that this method introduces bias in FA values for higher kernel sizes. Voxel-based DTI analysis can be performed on low signal to noise ratio images to detect subtle regional differences in cohorts using the proposed preprocessing techniques. Copyright © 2013 Wiley Periodicals, Inc.

Functional quantitative susceptibility mapping (fQSM).

PubMed

Balla, Dávid Z; Sanchez-Panchuelo, Rosa M; Wharton, Samuel J; Hagberg, Gisela E; Scheffler, Klaus; Francis, Susan T; Bowtell, Richard

2014-10-15

Blood oxygenation level dependent (BOLD) functional magnetic resonance imaging (fMRI) is a powerful technique, typically based on the statistical analysis of the magnitude component of the complex time-series. Here, we additionally interrogated the phase data of the fMRI time-series and used quantitative susceptibility mapping (QSM) in order to investigate the potential of functional QSM (fQSM) relative to standard magnitude BOLD fMRI. High spatial resolution data (1mm isotropic) were acquired every 3 seconds using zoomed multi-slice gradient-echo EPI collected at 7 T in single orientation (SO) and multiple orientation (MO) experiments, the latter involving 4 repetitions with the subject's head rotated relative to B0. Statistical parametric maps (SPM) were reconstructed for magnitude, phase and QSM time-series and each was subjected to detailed analysis. Several fQSM pipelines were evaluated and compared based on the relative number of voxels that were coincidentally found to be significant in QSM and magnitude SPMs (common voxels). We found that sensitivity and spatial reliability of fQSM relative to the magnitude data depended strongly on the arbitrary significance threshold defining "activated" voxels in SPMs, and on the efficiency of spatio-temporal filtering of the phase time-series. Sensitivity and spatial reliability depended slightly on whether MO or SO fQSM was performed and on the QSM calculation approach used for SO data. Our results present the potential of fQSM as a quantitative method of mapping BOLD changes. We also critically discuss the technical challenges and issues linked to this intriguing new technique. Copyright © 2014 Elsevier Inc. All rights reserved.

Visual feature extraction from voxel-weighted averaging of stimulus images in 2 fMRI studies.

PubMed

Hart, Corey B; Rose, William J

2013-11-01

Multiple studies have provided evidence for distributed object representation in the brain, with several recent experiments leveraging basis function estimates for partial image reconstruction from fMRI data. Using a novel combination of statistical decomposition, generalized linear models, and stimulus averaging on previously examined image sets and Bayesian regression of recorded fMRI activity during presentation of these data sets, we identify a subset of relevant voxels that appear to code for covarying object features. Using a technique we term "voxel-weighted averaging," we isolate image filters that these voxels appear to implement. The results, though very cursory, appear to have significant implications for hierarchical and deep-learning-type approaches toward the understanding of neural coding and representation.

Impact of correction factors in human brain lesion-behavior inference.

PubMed

Sperber, Christoph; Karnath, Hans-Otto

2017-03-01

Statistical voxel-based lesion-behavior mapping (VLBM) in neurological patients with brain lesions is frequently used to examine the relationship between structure and function of the healthy human brain. Only recently, two simulation studies noted reduced anatomical validity of this method, observing the results of VLBM to be systematically misplaced by about 16 mm. However, both simulation studies differed from VLBM analyses of real data in that they lacked the proper use of two correction factors: lesion size and "sufficient lesion affection." In simulation experiments on a sample of 274 real stroke patients, we found that the use of these two correction factors reduced misplacement markedly compared to uncorrected VLBM. Apparently, the misplacement is due to physiological effects of brain lesion anatomy. Voxel-wise topographies of collateral damage in the real data were generated and used to compute a metric for the inter-voxel relation of brain damage. "Anatomical bias" vectors that were solely calculated from these inter-voxel relations in the patients' real anatomical data, successfully predicted the VLBM misplacement. The latter has the potential to help in the development of new VLBM methods that provide even higher anatomical validity than currently available by the proper use of correction factors. Hum Brain Mapp 38:1692-1701, 2017. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

Adaptive smoothing based on Gaussian processes regression increases the sensitivity and specificity of fMRI data.

PubMed

Strappini, Francesca; Gilboa, Elad; Pitzalis, Sabrina; Kay, Kendrick; McAvoy, Mark; Nehorai, Arye; Snyder, Abraham Z

2017-03-01

Temporal and spatial filtering of fMRI data is often used to improve statistical power. However, conventional methods, such as smoothing with fixed-width Gaussian filters, remove fine-scale structure in the data, necessitating a tradeoff between sensitivity and specificity. Specifically, smoothing may increase sensitivity (reduce noise and increase statistical power) but at the cost loss of specificity in that fine-scale structure in neural activity patterns is lost. Here, we propose an alternative smoothing method based on Gaussian processes (GP) regression for single subjects fMRI experiments. This method adapts the level of smoothing on a voxel by voxel basis according to the characteristics of the local neural activity patterns. GP-based fMRI analysis has been heretofore impractical owing to computational demands. Here, we demonstrate a new implementation of GP that makes it possible to handle the massive data dimensionality of the typical fMRI experiment. We demonstrate how GP can be used as a drop-in replacement to conventional preprocessing steps for temporal and spatial smoothing in a standard fMRI pipeline. We present simulated and experimental results that show the increased sensitivity and specificity compared to conventional smoothing strategies. Hum Brain Mapp 38:1438-1459, 2017. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

Schizophrenia classification using functional network features

NASA Astrophysics Data System (ADS)

Rish, Irina; Cecchi, Guillermo A.; Heuton, Kyle

2012-03-01

This paper focuses on discovering statistical biomarkers (features) that are predictive of schizophrenia, with a particular focus on topological properties of fMRI functional networks. We consider several network properties, such as node (voxel) strength, clustering coefficients, local efficiency, as well as just a subset of pairwise correlations. While all types of features demonstrate highly significant statistical differences in several brain areas, and close to 80% classification accuracy, the most remarkable results of 93% accuracy are achieved by using a small subset of only a dozen of most-informative (lowest p-value) correlation features. Our results suggest that voxel-level correlations and functional network features derived from them are highly informative about schizophrenia and can be used as statistical biomarkers for the disease.

Tropospheric wet refractivity tomography using multiplicative algebraic reconstruction technique

NASA Astrophysics Data System (ADS)

Xiaoying, Wang; Ziqiang, Dai; Enhong, Zhang; Fuyang, K. E.; Yunchang, Cao; Lianchun, Song

2014-01-01

Algebraic reconstruction techniques (ART) have been successfully used to reconstruct the total electron content (TEC) of the ionosphere and in recent years be tentatively used in tropospheric wet refractivity and water vapor tomography in the ground-based GNSS technology. The previous research on ART used in tropospheric water vapor tomography focused on the convergence and relaxation parameters for various algebraic reconstruction techniques and rarely discussed the impact of Gaussian constraints and initial field on the iteration results. The existing accuracy evaluation parameters calculated from slant wet delay can only evaluate the resultant precision of the voxels penetrated by slant paths and cannot evaluate that of the voxels not penetrated by any slant path. The paper proposes two new statistical parameters Bias and RMS, calculated from wet refractivity of the total voxels, to improve the deficiencies of existing evaluation parameters and then discusses the effect of the Gaussian constraints and initial field on the convergence and tomography results in multiplicative algebraic reconstruction technique (MART) to reconstruct the 4D tropospheric wet refractivity field using simulation method.

Atlasing the frontal lobe connections and their variability due to age and education: a spherical deconvolution tractography study.

PubMed

Rojkova, K; Volle, E; Urbanski, M; Humbert, F; Dell'Acqua, F; Thiebaut de Schotten, M

2016-04-01

In neuroscience, there is a growing consensus that higher cognitive functions may be supported by distributed networks involving different cerebral regions, rather than by single brain areas. Communication within these networks is mediated by white matter tracts and is particularly prominent in the frontal lobes for the control and integration of information. However, the detailed mapping of frontal connections remains incomplete, albeit crucial to an increased understanding of these cognitive functions. Based on 47 high-resolution diffusion-weighted imaging datasets (age range 22-71 years), we built a statistical normative atlas of the frontal lobe connections in stereotaxic space, using state-of-the-art spherical deconvolution tractography. We dissected 55 tracts including U-shaped fibers. We further characterized these tracts by measuring their correlation with age and education level. We reported age-related differences in the microstructural organization of several, specific frontal fiber tracts, but found no correlation with education level. Future voxel-based analyses, such as voxel-based morphometry or tract-based spatial statistics studies, may benefit from our atlas by identifying the tracts and networks involved in frontal functions. Our atlas will also build the capacity of clinicians to further understand the mechanisms involved in brain recovery and plasticity, as well as assist clinicians in the diagnosis of disconnection or abnormality within specific tracts of individual patients with various brain diseases.

Reliability evaluation of I-123 ADAM SPECT imaging using SPM software and AAL ROI methods

NASA Astrophysics Data System (ADS)

Yang, Bang-Hung; Tsai, Sung-Yi; Wang, Shyh-Jen; Su, Tung-Ping; Chou, Yuan-Hwa; Chen, Chia-Chieh; Chen, Jyh-Cheng

2011-08-01

The level of serotonin was regulated by serotonin transporter (SERT), which is a decisive protein in regulation of serotonin neurotransmission system. Many psychiatric disorders and therapies were also related to concentration of cerebral serotonin. I-123 ADAM was the novel radiopharmaceutical to image SERT in brain. The aim of this study was to measure reliability of SERT densities of healthy volunteers by automated anatomical labeling (AAL) method. Furthermore, we also used statistic parametric mapping (SPM) on a voxel by voxel analysis to find difference of cortex between test and retest of I-123 ADAM single photon emission computed tomography (SPECT) images.Twenty-one healthy volunteers were scanned twice with SPECT at 4 h after intravenous administration of 185 MBq of 123I-ADAM. The image matrix size was 128×128 and pixel size was 3.9 mm. All images were obtained through filtered back-projection (FBP) reconstruction algorithm. Region of interest (ROI) definition was performed based on the AAL brain template in PMOD version 2.95 software package. ROI demarcations were placed on midbrain, pons, striatum, and cerebellum. All images were spatially normalized to the SPECT MNI (Montreal Neurological Institute) templates supplied with SPM2. And each image was transformed into standard stereotactic space, which was matched to the Talairach and Tournoux atlas. Then differences across scans were statistically estimated on a voxel by voxel analysis using paired t-test (population main effect: 2 cond's, 1 scan/cond.), which was applied to compare concentration of SERT between the test and retest cerebral scans.The average of specific uptake ratio (SUR: target/cerebellum-1) of 123I-ADAM binding to SERT in midbrain was 1.78±0.27, pons was 1.21±0.53, and striatum was 0.79±0.13. The cronbach's α of intra-class correlation coefficient (ICC) was 0.92. Besides, there was also no significant statistical finding in cerebral area using SPM2 analysis. This finding might help us to understand reliability of I-123 ADAM SPECT imaging and further develop new strategy for the treatment of psychiatric disorders.

a Voxel-Based Filtering Algorithm for Mobile LIDAR Data

NASA Astrophysics Data System (ADS)

Qin, H.; Guan, G.; Yu, Y.; Zhong, L.

2018-04-01

This paper presents a stepwise voxel-based filtering algorithm for mobile LiDAR data. In the first step, to improve computational efficiency, mobile LiDAR points, in xy-plane, are first partitioned into a set of two-dimensional (2-D) blocks with a given block size, in each of which all laser points are further organized into an octree partition structure with a set of three-dimensional (3-D) voxels. Then, a voxel-based upward growing processing is performed to roughly separate terrain from non-terrain points with global and local terrain thresholds. In the second step, the extracted terrain points are refined by computing voxel curvatures. This voxel-based filtering algorithm is comprehensively discussed in the analyses of parameter sensitivity and overall performance. An experimental study performed on multiple point cloud samples, collected by different commercial mobile LiDAR systems, showed that the proposed algorithm provides a promising solution to terrain point extraction from mobile point clouds.

Detection of small human cerebral cortical lesions with MRI under different levels of Gaussian smoothing: applications in epilepsy

NASA Astrophysics Data System (ADS)

Cantor-Rivera, Diego; Goubran, Maged; Kraguljac, Alan; Bartha, Robert; Peters, Terry

2010-03-01

The main objective of this study was to assess the effect of smoothing filter selection in Voxel-Based Morphometry studies on structural T1-weighted magnetic resonance images. Gaussian filters of 4 mm, 8 mm or 10 mm Full Width at High Maximum are commonly used, based on the assumption that the filter size should be at least twice the voxel size to obtain robust statistical results. The hypothesis of the presented work was that the selection of the smoothing filter influenced the detectability of small lesions in the brain. Mesial Temporal Sclerosis associated to Epilepsy was used as the case to demonstrate this effect. Twenty T1-weighted MRIs from the BrainWeb database were selected. A small phantom lesion was placed in the amygdala, hippocampus, or parahippocampal gyrus of ten of the images. Subsequently the images were registered to the ICBM/MNI space. After grey matter segmentation, a T-test was carried out to compare each image containing a phantom lesion with the rest of the images in the set. For each lesion the T-test was repeated with different Gaussian filter sizes. Voxel-Based Morphometry detected some of the phantom lesions. Of the three parameters considered: location,size, and intensity; it was shown that location is the dominant factor for the detection of the lesions.

Investigating structural brain changes of dehydration using voxel-based morphometry.

PubMed

Streitbürger, Daniel-Paolo; Möller, Harald E; Tittgemeyer, Marc; Hund-Georgiadis, Margret; Schroeter, Matthias L; Mueller, Karsten

2012-01-01

Dehydration can affect the volume of brain structures, which might imply a confound in volumetric and morphometric studies of normal or diseased brain. Six young, healthy volunteers were repeatedly investigated using three-dimensional T(1)-weighted magnetic resonance imaging during states of normal hydration, hyperhydration, and dehydration to assess volume changes in gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF). The datasets were analyzed using voxel-based morphometry (VBM), a widely used voxel-wise statistical analysis tool, FreeSurfer, a fully automated volumetric segmentation measure, and SIENAr a longitudinal brain-change detection algorithm. A significant decrease of GM and WM volume associated with dehydration was found in various brain regions, most prominently, in temporal and sub-gyral parietal areas, in the left inferior orbito-frontal region, and in the extra-nuclear region. Moreover, we found consistent increases in CSF, that is, an expansion of the ventricular system affecting both lateral ventricles, the third, and the fourth ventricle. Similar degrees of shrinkage in WM volume and increase of the ventricular system have been reported in studies of mild cognitive impairment or Alzheimer's disease during disease progression. Based on these findings, a potential confound in GM and WM or ventricular volume studies due to the subjects' hydration state cannot be excluded and should be appropriately addressed in morphometric studies of the brain.

Investigating Structural Brain Changes of Dehydration Using Voxel-Based Morphometry

PubMed Central

Streitbürger, Daniel-Paolo; Möller, Harald E.; Tittgemeyer, Marc; Hund-Georgiadis, Margret; Schroeter, Matthias L.; Mueller, Karsten

2012-01-01

Dehydration can affect the volume of brain structures, which might imply a confound in volumetric and morphometric studies of normal or diseased brain. Six young, healthy volunteers were repeatedly investigated using three-dimensional T 1-weighted magnetic resonance imaging during states of normal hydration, hyperhydration, and dehydration to assess volume changes in gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF). The datasets were analyzed using voxel-based morphometry (VBM), a widely used voxel-wise statistical analysis tool, FreeSurfer, a fully automated volumetric segmentation measure, and SIENAr a longitudinal brain-change detection algorithm. A significant decrease of GM and WM volume associated with dehydration was found in various brain regions, most prominently, in temporal and sub-gyral parietal areas, in the left inferior orbito-frontal region, and in the extra-nuclear region. Moreover, we found consistent increases in CSF, that is, an expansion of the ventricular system affecting both lateral ventricles, the third, and the fourth ventricle. Similar degrees of shrinkage in WM volume and increase of the ventricular system have been reported in studies of mild cognitive impairment or Alzheime s disease during disease progression. Based on these findings, a potential confound in GM and WM or ventricular volume studies due to the subjects’ hydration state cannot be excluded and should be appropriately addressed in morphometric studies of the brain. PMID:22952926

Multistep Lattice-Voxel method utilizing lattice function for Monte-Carlo treatment planning with pixel based voxel model.

PubMed

Kumada, H; Saito, K; Nakamura, T; Sakae, T; Sakurai, H; Matsumura, A; Ono, K

2011-12-01

Treatment planning for boron neutron capture therapy generally utilizes Monte-Carlo methods for calculation of the dose distribution. The new treatment planning system JCDS-FX employs the multi-purpose Monte-Carlo code PHITS to calculate the dose distribution. JCDS-FX allows to build a precise voxel model consisting of pixel based voxel cells in the scale of 0.4×0.4×2.0 mm(3) voxel in order to perform high-accuracy dose estimation, e.g. for the purpose of calculating the dose distribution in a human body. However, the miniaturization of the voxel size increases calculation time considerably. The aim of this study is to investigate sophisticated modeling methods which can perform Monte-Carlo calculations for human geometry efficiently. Thus, we devised a new voxel modeling method "Multistep Lattice-Voxel method," which can configure a voxel model that combines different voxel sizes by utilizing the lattice function over and over. To verify the performance of the calculation with the modeling method, several calculations for human geometry were carried out. The results demonstrated that the Multistep Lattice-Voxel method enabled the precise voxel model to reduce calculation time substantially while keeping the high-accuracy of dose estimation. Copyright © 2011 Elsevier Ltd. All rights reserved.

Diffusion Tensor Image Registration Using Hybrid Connectivity and Tensor Features

PubMed Central

Wang, Qian; Yap, Pew-Thian; Wu, Guorong; Shen, Dinggang

2014-01-01

Most existing diffusion tensor imaging (DTI) registration methods estimate structural correspondences based on voxelwise matching of tensors. The rich connectivity information that is given by DTI, however, is often neglected. In this article, we propose to integrate complementary information given by connectivity features and tensor features for improved registration accuracy. To utilize connectivity information, we place multiple anchors representing different brain anatomies in the image space, and define the connectivity features for each voxel as the geodesic distances from all anchors to the voxel under consideration. The geodesic distance, which is computed in relation to the tensor field, encapsulates information of brain connectivity. We also extract tensor features for every voxel to reflect the local statistics of tensors in its neighborhood. We then combine both connectivity features and tensor features for registration of tensor images. From the images, landmarks are selected automatically and their correspondences are determined based on their connectivity and tensor feature vectors. The deformation field that deforms one tensor image to the other is iteratively estimated and optimized according to the landmarks and their associated correspondences. Experimental results show that, by using connectivity features and tensor features simultaneously, registration accuracy is increased substantially compared with the cases using either type of features alone. PMID:24293159

Efficient Encoding and Rendering of Time-Varying Volume Data

NASA Technical Reports Server (NTRS)

Ma, Kwan-Liu; Smith, Diann; Shih, Ming-Yun; Shen, Han-Wei

1998-01-01

Visualization of time-varying volumetric data sets, which may be obtained from numerical simulations or sensing instruments, provides scientists insights into the detailed dynamics of the phenomenon under study. This paper describes a coherent solution based on quantization, coupled with octree and difference encoding for visualizing time-varying volumetric data. Quantization is used to attain voxel-level compression and may have a significant influence on the performance of the subsequent encoding and visualization steps. Octree encoding is used for spatial domain compression, and difference encoding for temporal domain compression. In essence, neighboring voxels may be fused into macro voxels if they have similar values, and subtrees at consecutive time steps may be merged if they are identical. The software rendering process is tailored according to the tree structures and the volume visualization process. With the tree representation, selective rendering may be performed very efficiently. Additionally, the I/O costs are reduced. With these combined savings, a higher level of user interactivity is achieved. We have studied a variety of time-varying volume datasets, performed encoding based on data statistics, and optimized the rendering calculations wherever possible. Preliminary tests on workstations have shown in many cases tremendous reduction by as high as 90% in both storage space and inter-frame delay.

Seeking Optimal Region-Of-Interest (ROI) Single-Value Summary Measures for fMRI Studies in Imaging Genetics

PubMed Central

Tong, Yunxia; Chen, Qiang; Nichols, Thomas E.; Rasetti, Roberta; Callicott, Joseph H.; Berman, Karen F.; Weinberger, Daniel R.; Mattay, Venkata S.

2016-01-01

A data-driven hypothesis-free genome-wide association (GWA) approach in imaging genetics studies allows screening the entire genome to discover novel genes that modulate brain structure, chemistry, and function. However, a whole brain voxel-wise analysis approach in such genome-wide based imaging genetic studies can be computationally intense and also likely has low statistical power since a stringent multiple comparisons correction is needed for searching over the entire genome and brain. In imaging genetics with functional magnetic resonance imaging (fMRI) phenotypes, since many experimental paradigms activate focal regions that can be pre-specified based on a priori knowledge, reducing the voxel-wise search to single-value summary measures within a priori ROIs could prove efficient and promising. The goal of this investigation is to evaluate the sensitivity and reliability of different single-value ROI summary measures and provide guidance in future work. Four different fMRI databases were tested and comparisons across different groups (patients with schizophrenia, their siblings, vs. normal control subjects; across genotype groups) were conducted. Our results show that four of these measures, particularly those that represent values from the top most-activated voxels within an ROI are more powerful at reliably detecting group differences and generating greater effect sizes than the others. PMID:26974435

SU-E-T-558: Monte Carlo Photon Transport Simulations On GPU with Quadric Geometry

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chi, Y; Tian, Z; Jiang, S

Purpose: Monte Carlo simulation on GPU has experienced rapid advancements over the past a few years and tremendous accelerations have been achieved. Yet existing packages were developed only in voxelized geometry. In some applications, e.g. radioactive seed modeling, simulations in more complicated geometry are needed. This abstract reports our initial efforts towards developing a quadric geometry module aiming at expanding the application scope of GPU-based MC simulations. Methods: We defined the simulation geometry consisting of a number of homogeneous bodies, each specified by its material composition and limiting surfaces characterized by quadric functions. A tree data structure was utilized tomore » define geometric relationship between different bodies. We modified our GPU-based photon MC transport package to incorporate this geometry. Specifically, geometry parameters were loaded into GPU’s shared memory for fast access. Geometry functions were rewritten to enable the identification of the body that contains the current particle location via a fast searching algorithm based on the tree data structure. Results: We tested our package in an example problem of HDR-brachytherapy dose calculation for shielded cylinder. The dose under the quadric geometry and that under the voxelized geometry agreed in 94.2% of total voxels within 20% isodose line based on a statistical t-test (95% confidence level), where the reference dose was defined to be the one at 0.5cm away from the cylinder surface. It took 243sec to transport 100million source photons under this quadric geometry on an NVidia Titan GPU card. Compared with simulation time of 99.6sec in the voxelized geometry, including quadric geometry reduced efficiency due to the complicated geometry-related computations. Conclusion: Our GPU-based MC package has been extended to support photon transport simulation in quadric geometry. Satisfactory accuracy was observed with a reduced efficiency. Developments for charged particle transport in this geometry are currently in progress.« less

Voxel-Based 3-D Tree Modeling from Lidar Images for Extracting Tree Structual Information

NASA Astrophysics Data System (ADS)

Hosoi, F.

2014-12-01

Recently, lidar (light detection and ranging) has been used to extracting tree structural information. Portable scanning lidar systems can capture the complex shape of individual trees as a 3-D point-cloud image. 3-D tree models reproduced from the lidar-derived 3-D image can be used to estimate tree structural parameters. We have proposed the voxel-based 3-D modeling for extracting tree structural parameters. One of the tree parameters derived from the voxel modeling is leaf area density (LAD). We refer to the method as the voxel-based canopy profiling (VCP) method. In this method, several measurement points surrounding the canopy and optimally inclined laser beams are adopted for full laser beam illumination of whole canopy up to the internal. From obtained lidar image, the 3-D information is reproduced as the voxel attributes in the 3-D voxel array. Based on the voxel attributes, contact frequency of laser beams on leaves is computed and LAD in each horizontal layer is obtained. This method offered accurate LAD estimation for individual trees and woody canopy trees. For more accurate LAD estimation, the voxel model was constructed by combining airborne and portable ground-based lidar data. The profiles obtained by the two types of lidar complemented each other, thus eliminating blind regions and yielding more accurate LAD profiles than could be obtained by using each type of lidar alone. Based on the estimation results, we proposed an index named laser beam coverage index, Ω, which relates to the lidar's laser beam settings and a laser beam attenuation factor. It was shown that this index can be used for adjusting measurement set-up of lidar systems and also used for explaining the LAD estimation error using different types of lidar systems. Moreover, we proposed a method to estimate woody material volume as another application of the voxel tree modeling. In this method, voxel solid model of a target tree was produced from the lidar image, which is composed of consecutive voxels that filled the outer surface and the interior of the stem and large branches. From the model, the woody material volume of any part of the target tree can be directly calculated easily by counting the number of corresponding voxels and multiplying the result by the per-voxel volume.

18F-FDG PET brain images as features for Alzheimer classification

NASA Astrophysics Data System (ADS)

Azmi, M. H.; Saripan, M. I.; Nordin, A. J.; Ahmad Saad, F. F.; Abdul Aziz, S. A.; Wan Adnan, W. A.

2017-08-01

2-Deoxy-2-[fluorine-18] fluoro-D-glucose (18F-FDG) Positron Emission Tomography (PET) imaging offers meaningful information for various types of diseases diagnosis. In Alzheimer's disease (AD), the hypometabolism of glucose which observed on the low intensity voxel in PET image may relate to the onset of the disease. The importance of early detection of AD is inevitable because the resultant brain damage is irreversible. Several statistical analysis and machine learning algorithm have been proposed to investigate the rate and the pattern of the hypometabolism. This study focus on the same aim with further investigation was performed on several hypometabolism pattern. Some pre-processing steps were implemented to standardize the data in order to minimize the effect of resolution and anatomical differences. The features used are the mean voxel intensity within the AD pattern mask, which derived from several z-score and FDR threshold values. The global mean voxel (GMV) and slice-based mean voxel (SbMV) intensity were observed and used as input to the neural network. Several neural network architectures were tested and compared to the nearest neighbour method. The highest accuracy equals to 0.9 and recorded at z-score ≤-1.3 with 1 node neural network architecture (sensitivity=0.81 and specificity=0.95) and at z-score ≤-0.7 with 10 nodes neural network (sensitivity=0.83 and specificity=0.94).

Radiomics-based differentiation of lung disease models generated by polluted air based on X-ray computed tomography data.

PubMed

Szigeti, Krisztián; Szabó, Tibor; Korom, Csaba; Czibak, Ilona; Horváth, Ildikó; Veres, Dániel S; Gyöngyi, Zoltán; Karlinger, Kinga; Bergmann, Ralf; Pócsik, Márta; Budán, Ferenc; Máthé, Domokos

2016-02-11

Lung diseases (resulting from air pollution) require a widely accessible method for risk estimation and early diagnosis to ensure proper and responsive treatment. Radiomics-based fractal dimension analysis of X-ray computed tomography attenuation patterns in chest voxels of mice exposed to different air polluting agents was performed to model early stages of disease and establish differential diagnosis. To model different types of air pollution, BALBc/ByJ mouse groups were exposed to cigarette smoke combined with ozone, sulphur dioxide gas and a control group was established. Two weeks after exposure, the frequency distributions of image voxel attenuation data were evaluated. Specific cut-off ranges were defined to group voxels by attenuation. Cut-off ranges were binarized and their spatial pattern was associated with calculated fractal dimension, then abstracted by the fractal dimension -- cut-off range mathematical function. Nonparametric Kruskal-Wallis (KW) and Mann-Whitney post hoc (MWph) tests were used. Each cut-off range versus fractal dimension function plot was found to contain two distinctive Gaussian curves. The ratios of the Gaussian curve parameters are considerably significant and are statistically distinguishable within the three exposure groups. A new radiomics evaluation method was established based on analysis of the fractal dimension of chest X-ray computed tomography data segments. The specific attenuation patterns calculated utilizing our method may diagnose and monitor certain lung diseases, such as chronic obstructive pulmonary disease (COPD), asthma, tuberculosis or lung carcinomas.

A Fully GPU-Based Ray-Driven Backprojector via a Ray-Culling Scheme with Voxel-Level Parallelization for Cone-Beam CT Reconstruction.

PubMed

Park, Hyeong-Gyu; Shin, Yeong-Gil; Lee, Ho

2015-12-01

A ray-driven backprojector is based on ray-tracing, which computes the length of the intersection between the ray paths and each voxel to be reconstructed. To reduce the computational burden caused by these exhaustive intersection tests, we propose a fully graphics processing unit (GPU)-based ray-driven backprojector in conjunction with a ray-culling scheme that enables straightforward parallelization without compromising the high computing performance of a GPU. The purpose of the ray-culling scheme is to reduce the number of ray-voxel intersection tests by excluding rays irrelevant to a specific voxel computation. This rejection step is based on an axis-aligned bounding box (AABB) enclosing a region of voxel projection, where eight vertices of each voxel are projected onto the detector plane. The range of the rectangular-shaped AABB is determined by min/max operations on the coordinates in the region. Using the indices of pixels inside the AABB, the rays passing through the voxel can be identified and the voxel is weighted as the length of intersection between the voxel and the ray. This procedure makes it possible to reflect voxel-level parallelization, allowing an independent calculation at each voxel, which is feasible for a GPU implementation. To eliminate redundant calculations during ray-culling, a shared-memory optimization is applied to exploit the GPU memory hierarchy. In experimental results using real measurement data with phantoms, the proposed GPU-based ray-culling scheme reconstructed a volume of resolution 28032803176 in 77 seconds from 680 projections of resolution 10243768 , which is 26 times and 7.5 times faster than standard CPU-based and GPU-based ray-driven backprojectors, respectively. Qualitative and quantitative analyses showed that the ray-driven backprojector provides high-quality reconstruction images when compared with those generated by the Feldkamp-Davis-Kress algorithm using a pixel-driven backprojector, with an average of 2.5 times higher contrast-to-noise ratio, 1.04 times higher universal quality index, and 1.39 times higher normalized mutual information. © The Author(s) 2014.

Detection of periimplant fenestration and dehiscence with the use of two scan modes and the smallest voxel sizes of a cone-beam computed tomography device.

PubMed

de-Azevedo-Vaz, Sergio Lins; Vasconcelos, Karla de Faria; Neves, Frederico Sampaio; Melo, Saulo Leonardo Sousa; Campos, Paulo Sérgio Flores; Haiter-Neto, Francisco

2013-01-01

To assess the accuracy of cone-beam computed tomography (CBCT) in periimplant fenestration and dehiscence detection, and to determine the effects of 2 voxel sizes and scan modes. One hundred titanium implants were placed in bovine ribs in which periimplant fenestration and dehiscence were simulated. CBCT images were acquired with the use of 3 protocols of the i-CAT NG unit: A) 0.2 mm voxel size half-scan (180°); B) 0.2 mm voxel size full-scan (360°); and C) 0.12 mm voxel size full scan (360°). Receiver operating characteristic curves and diagnostic values were obtained. The Az values were compared with the use of analysis of variance. The Az value for dehiscence in protocol A was significantly lower than those of B or C (P < .01). They did not statistically differ for fenestration (P > .05). Protocol B yielded the highest values. The voxel sizes did not affect fenestration and dehiscence detection, and for dehiscence full-scan performed better than half-scan. Copyright © 2013 Elsevier Inc. All rights reserved.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Engstroem, K; Casares-Magaz, O; Muren, L

Purpose: Multi-parametric MRI (mp-MRI) is being introduced in radiotherapy (RT) of prostate cancer, including for tumour delineation in focal boosting strategies. We recently developed an image-based tumour control probability model, based on cell density distributions derived from apparent diffusion coefficient (ADC) maps. Beyond tumour volume and cell densities, tumour hypoxia is also an important determinant of RT response. Since tissue perfusion from mp-MRI has been related to hypoxia we have explored the patterns of ADC and perfusion maps, and the relations between them, inside and outside prostate index lesions. Methods: ADC and perfusion maps from 20 prostate cancer patients weremore » used, with the prostate and index lesion delineated by a dedicated uro-radiologist. To reduce noise, the maps were averaged over a 3×3×3 voxel cube. Associations between different ADC and perfusion histogram parameters within the prostate, inside and outside the index lesion, were evaluated with the Pearson’s correlation coefficient. In the voxel-wise analysis, scatter plots of ADC vs perfusion were analysed for voxels in the prostate, inside and outside of the index lesion, again with the associations quantified with the Pearson’s correlation coefficient. Results: Overall ADC was lower inside the index lesion than in the normal prostate as opposed to ktrans that was higher inside the index lesion than outside. In the histogram analysis, the minimum ktrans was significantly correlated with the maximum ADC (Pearson=0.47; p=0.03). At the voxel level, 15 of the 20 cases had a statistically significant inverse correlation between ADC and perfusion inside the index lesion; ten of the cases had a Pearson < −0.4. Conclusion: The minimum value of ktrans across the tumour was correlated to the maximum ADC. However, on the voxel level, the ‘local’ ktrans in the index lesion is inversely (i.e. negatively) correlated to the ‘local’ ADC in most patients. Research agreement with Varian Medical Systems, not related to the work presented in this abstract.« less

Retrospective correction of bias in diffusion tensor imaging arising from coil combination mode.

PubMed

Sakaie, Ken; Lowe, Mark

2017-04-01

To quantify and retrospectively correct for systematic differences in diffusion tensor imaging (DTI) measurements due to differences in coil combination mode. Multi-channel coils are now standard among MRI systems. There are several options for combining signal from multiple coils during image reconstruction, including sum-of-squares (SOS) and adaptive combine (AC). This contribution examines the bias between SOS- and AC-derived measures of tissue microstructure and a strategy for limiting that bias. Five healthy subjects were scanned under an institutional review board-approved protocol. Each set of raw image data was reconstructed twice-once with SOS and once with AC. The diffusion tensor was calculated from SOS- and AC-derived data by two algorithms-standard log-linear least squares and an approach that accounts for the impact of coil combination on signal statistics. Systematic differences between SOS and AC in terms of tissue microstructure (axial diffusivity, radial diffusivity, mean diffusivity and fractional anisotropy) were evaluated on a voxel-by-voxel basis. SOS-based tissue microstructure values are systematically lower than AC-based measures throughout the brain in each subject when using the standard tensor calculation method. The difference between SOS and AC can be virtually eliminated by taking into account the signal statistics associated with coil combination. The impact of coil combination mode on diffusion tensor-based measures of tissue microstructure is statistically significant but can be corrected retrospectively. The ability to do so is expected to facilitate pooling of data among imaging protocols. Copyright © 2016 Elsevier Inc. All rights reserved.

Diffusion Tensor Imaging of Pedophilia.

PubMed

Cantor, James M; Lafaille, Sophie; Soh, Debra W; Moayedi, Massieh; Mikulis, David J; Girard, Todd A

2015-11-01

Pedophilia is a principal motivator of child molestation, incurring great emotional and financial burdens on victims and society. Even among pedophiles who never commit any offense,the condition requires lifelong suppression and control. Previous comparison using voxel-based morphometry (VBM)of MR images from a large sample of pedophiles and controls revealed group differences in white matter. The present study therefore sought to verify and characterize white matter involvement using diffusion tensor imaging (DTI), which better captures the microstructure of white matter than does VBM. Pedophilics ex offenders (n=24) were compared with healthy, age-matched controls with no criminal record and no indication of pedophilia (n=32). White matter microstructure was analyzed with Tract-Based Spatial Statistics, and the trajectories of implicated fiber bundles were identified by probabilistic tractography. Groups showed significant, highly focused differences in DTI parameters which related to participants’ genital responses to sexual depictions of children, but not to measures of psychopathy or to childhood histories of physical abuse, sexual abuse, or neglect. Some previously reported gray matter differences were suggested under highly liberal statistical conditions (p(uncorrected)<.005), but did not survive ordinary statistical correction (whole brain per voxel false discovery rate of 5%). These results confirm that pedophilia is characterized by neuroanatomical differences in white matter microstructure, over and above any neural characteristics attributable to psychopathy and childhood adversity, which show neuroanatomic footprints of their own. Although some gray matter structures were implicated previously, only few have emerged reliably.

Defining ischemic burden after traumatic brain injury using 15O PET imaging of cerebral physiology.

PubMed

Coles, Jonathan P; Fryer, Tim D; Smielewski, Peter; Rice, Kenneth; Clark, John C; Pickard, John D; Menon, David K

2004-02-01

Whereas postmortem ischemic damage is common in head injury, antemortem demonstration of ischemia has proven to be elusive. Although 15O positron emission tomography may be useful in this area, the technique has traditionally analyzed data within regions of interest (ROIs) to improve statistical accuracy. In head injury, such techniques are limited because of the lack of a priori knowledge regarding the location of ischemia, coexistence of hyperaemia, and difficulty in defining ischemic cerebral blood flow (CBF) and cerebral oxygen metabolism (CMRO2) levels. We report a novel method for defining disease pathophysiology following head injury. Voxel-based approaches are used to define the distribution of oxygen extraction fraction (OEF) across the entire brain; the standard deviation of this distribution provides a measure of the variability of OEF. These data are also used to integrate voxels above a threshold OEF value to produce an ROI based upon coherent physiology rather than spatial contiguity (the ischemic brain volume; IBV). However, such approaches may suffer from poor statistical accuracy, particularly in regions with low blood flow. The magnitude of these errors has been assessed in modeling experiments using the Hoffman brain phantom and modified control datasets. We conclude that this technique is a valid and useful tool for quantifying ischemic burden after traumatic brain injury.

A Large Scale (N=400) Investigation of Gray Matter Differences in Schizophrenia Using Optimized Voxel-based Morphometry

PubMed Central

Meda, Shashwath A.; Giuliani, Nicole R.; Calhoun, Vince D.; Jagannathan, Kanchana; Schretlen, David J.; Pulver, Anne; Cascella, Nicola; Keshavan, Matcheri; Kates, Wendy; Buchanan, Robert; Sharma, Tonmoy; Pearlson, Godfrey D.

2008-01-01

Background Many studies have employed voxel-based morphometry (VBM) of MRI images as an automated method of investigating cortical gray matter differences in schizophrenia. However, results from these studies vary widely, likely due to different methodological or statistical approaches. Objective To use VBM to investigate gray matter differences in schizophrenia in a sample significantly larger than any published to date, and to increase statistical power sufficiently to reveal differences missed in smaller analyses. Methods Magnetic resonance whole brain images were acquired from four geographic sites, all using the same model 1.5T scanner and software version, and combined to form a sample of 200 patients with both first episode and chronic schizophrenia and 200 healthy controls, matched for age, gender and scanner location. Gray matter concentration was assessed and compared using optimized VBM. Results Compared to the healthy controls, schizophrenia patients showed significantly less gray matter concentration in multiple cortical and subcortical regions, some previously unreported. Overall, we found lower concentrations of gray matter in regions identified in prior studies, most of which reported only subsets of the affected areas. Conclusions Gray matter differences in schizophrenia are most comprehensively elucidated using a large, diverse and representative sample. PMID:18378428

A Corner-Point-Grid-Based Voxelization Method for Complex Geological Structure Model with Folds

NASA Astrophysics Data System (ADS)

Chen, Qiyu; Mariethoz, Gregoire; Liu, Gang

2017-04-01

3D voxelization is the foundation of geological property modeling, and is also an effective approach to realize the 3D visualization of the heterogeneous attributes in geological structures. The corner-point grid is a representative data model among all voxel models, and is a structured grid type that is widely applied at present. When carrying out subdivision for complex geological structure model with folds, we should fully consider its structural morphology and bedding features to make the generated voxels keep its original morphology. And on the basis of which, they can depict the detailed bedding features and the spatial heterogeneity of the internal attributes. In order to solve the shortage of the existing technologies, this work puts forward a corner-point-grid-based voxelization method for complex geological structure model with folds. We have realized the fast conversion from the 3D geological structure model to the fine voxel model according to the rule of isocline in Ramsay's fold classification. In addition, the voxel model conforms to the spatial features of folds, pinch-out and other complex geological structures, and the voxels of the laminas inside a fold accords with the result of geological sedimentation and tectonic movement. This will provide a carrier and model foundation for the subsequent attribute assignment as well as the quantitative analysis and evaluation based on the spatial voxels. Ultimately, we use examples and the contrastive analysis between the examples and the Ramsay's description of isoclines to discuss the effectiveness and advantages of the method proposed in this work when dealing with the voxelization of 3D geologic structural model with folds based on corner-point grids.

Effect of Experimental Thyrotoxicosis on Brain Gray Matter: A Voxel-Based Morphometry Study.

PubMed

Göbel, Anna; Heldmann, Marcus; Göttlich, Martin; Dirk, Anna-Luise; Brabant, Georg; Münte, Thomas F

2015-09-01

Hyper-as well hypothyroidism have an effect on behavior and brain function. Moreover, during development thyroid hormones influence brain structure. This study aimed to demonstrate an effect of experimentally induced hyperthyroidism on brain gray matter in healthy adult humans. High-resolution 3D T1-weighted images were acquired in 29 healthy young subjects prior to as well as after receiving 250 µg of T4 per day for 8 weeks. Voxel-based morphometry analysis was performed using Statistical Parametric Mapping 8 (SPM8). Laboratory testing confirmed the induction of hyperthyroidism. In the hyperthyroid condition, gray matter volumes were increased in the right posterior cerebellum (lobule VI) and decreased in the bilateral visual cortex and anterior cerebellum (lobules I-IV) compared to the euthyroid condition. Our study provides evidence that short periods of hyperthyroidism induce distinct alterations in brain structures of cerebellar regions that have been associated with sensorimotor functions as well as working memory in the literature.

Multi-Scale Voxel Segmentation for Terrestrial Lidar Data within Marshes

NASA Astrophysics Data System (ADS)

Nguyen, C. T.; Starek, M. J.; Tissot, P.; Gibeaut, J. C.

2016-12-01

The resilience of marshes to a rising sea is dependent on their elevation response. Terrestrial laser scanning (TLS) is a detailed topographic approach for accurate, dense surface measurement with high potential for monitoring of marsh surface elevation response. The dense point cloud provides a 3D representation of the surface, which includes both terrain and non-terrain objects. Extraction of topographic information requires filtering of the data into like-groups or classes, therefore, methods must be incorporated to identify structure in the data prior to creation of an end product. A voxel representation of three-dimensional space provides quantitative visualization and analysis for pattern recognition. The objectives of this study are threefold: 1) apply a multi-scale voxel approach to effectively extract geometric features from the TLS point cloud data, 2) investigate the utility of K-means and Self Organizing Map (SOM) clustering algorithms for segmentation, and 3) utilize a variety of validity indices to measure the quality of the result. TLS data were collected at a marsh site along the central Texas Gulf Coast using a Riegl VZ 400 TLS. The site consists of both exposed and vegetated surface regions. To characterize structure of the point cloud, octree segmentation is applied to create a tree data structure of voxels containing the points. The flexibility of voxels in size and point density makes this algorithm a promising candidate to locally extract statistical and geometric features of the terrain including surface normal and curvature. The characteristics of the voxel itself such as the volume and point density are also computed and assigned to each point as are laser pulse characteristics. The features extracted from the voxelization are then used as input for clustering of the points using the K-means and SOM clustering algorithms. Optimal number of clusters are then determined based on evaluation of cluster separability criterions. Results for different combinations of the feature space vector and differences between K-means and SOM clustering will be presented. The developed method provides a novel approach for compressing TLS scene complexity in marshes, such as for vegetation biomass studies or erosion monitoring.

Collaborative voxel-based surgical virtual environments.

PubMed

Acosta, Eric; Muniz, Gilbert; Armonda, Rocco; Bowyer, Mark; Liu, Alan

2008-01-01

Virtual Reality-based surgical simulators can utilize Collaborative Virtual Environments (C-VEs) to provide team-based training. To support real-time interactions, C-VEs are typically replicated on each user's local computer and a synchronization method helps keep all local copies consistent. This approach does not work well for voxel-based C-VEs since large and frequent volumetric updates make synchronization difficult. This paper describes a method that allows multiple users to interact within a voxel-based C-VE for a craniotomy simulator being developed. Our C-VE method requires smaller update sizes and provides faster synchronization update rates than volumetric-based methods. Additionally, we address network bandwidth/latency issues to simulate networked haptic and bone drilling tool interactions with a voxel-based skull C-VE.

Voxel-wise grey matter asymmetry analysis in left- and right-handers.

PubMed

Ocklenburg, Sebastian; Friedrich, Patrick; Güntürkün, Onur; Genç, Erhan

2016-10-28

Handedness is thought to originate in the brain, but identifying its structural correlates in the cortex has yielded surprisingly incoherent results. One idea proclaimed by several authors is that structural grey matter asymmetries might underlie handedness. While some authors have found significant associations with handedness in different brain areas (e.g. in the central sulcus and precentral sulcus), others have failed to identify such associations. One method used by many researchers to determine structural grey matter asymmetries is voxel based morphometry (VBM). However, it has recently been suggested that the standard VBM protocol might not be ideal to assess structural grey matter asymmetries, as it establishes accurate voxel-wise correspondence across individuals but not across both hemispheres. This could potentially lead to biased and incoherent results. Recently, a new toolbox specifically geared at assessing structural asymmetries and involving accurate voxel-wise correspondence across hemispheres has been published [F. Kurth, C. Gaser, E. Luders. A 12-step user guide for analyzing voxel-wise gray matter asymmetries in statistical parametric mapping (SPM), Nat Protoc 10 (2015), 293-304]. Here, we used this new toolbox to re-assess grey matter asymmetry differences in left- vs. right-handers and linked them to quantitative measures of hand preference and hand skill. While we identified several significant left-right asymmetries in the overall sample, no difference between left- and right-handers reached significance after correction for multiple comparisons. These findings indicate that the structural brain correlates of handedness are unlikely to be rooted in macroscopic grey matter area differences that can be assessed with VBM. Future studies should focus on other potential structural correlates of handedness, e.g. structural white matter asymmetries. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Whole-brain voxel-based morphometry in Kallmann syndrome associated with mirror movements.

PubMed

Koenigkam-Santos, M; Santos, A C; Borduqui, T; Versiani, B R; Hallak, J E C; Crippa, J A S; Castro, M

2008-10-01

There are 2 main hypotheses concerning the cause of mirror movements (MM) in Kallmann syndrome (KS): abnormal development of the primary motor system, involving the ipsilateral corticospinal tract; and lack of contralateral motor cortex inhibitory mechanisms, mainly through the corpus callosum. The purpose of our study was to determine white and gray matter volume changes in a KS population by using optimized voxel-based morphometry (VBM) and to investigate the relationship between the abnormalities and the presence of MM, addressing the 2 mentioned hypotheses. T1-weighted volumetric images from 21 patients with KS and 16 matched control subjects were analyzed with optimized VBM. Images were segmented and spatially normalized, and these deformation parameters were then applied to the original images before the second segmentation. Patients were divided into groups with and without MM, and a t test statistic was then applied on a voxel-by-voxel basis between the groups and controls to evaluate significant differences. When considering our hypothesis a priori, we found that 2 areas of increased gray matter volume, in the left primary motor and sensorimotor cortex, were demonstrated only in patients with MM, when compared with healthy controls. Regarding white matter alterations, no areas of altered volume involving the corpus callosum or the projection of the corticospinal tract were demonstrated. The VBM study did not show significant white matter changes in patients with KS but showed gray matter alterations in keeping with a hypertrophic response to a deficient pyramidal decussation in patients with MM. In addition, gray matter alterations were observed in patients without MM, which can represent more complex mechanisms determining the presence or absence of this symptom.

Individual classification of Alzheimer's disease with diffusion magnetic resonance imaging.

PubMed

Schouten, Tijn M; Koini, Marisa; Vos, Frank de; Seiler, Stephan; Rooij, Mark de; Lechner, Anita; Schmidt, Reinhold; Heuvel, Martijn van den; Grond, Jeroen van der; Rombouts, Serge A R B

2017-05-15

Diffusion magnetic resonance imaging (MRI) is a powerful non-invasive method to study white matter integrity, and is sensitive to detect differences in Alzheimer's disease (AD) patients. Diffusion MRI may be able to contribute towards reliable diagnosis of AD. We used diffusion MRI to classify AD patients (N=77), and controls (N=173). We use different methods to extract information from the diffusion MRI data. First, we use the voxel-wise diffusion tensor measures that have been skeletonised using tract based spatial statistics. Second, we clustered the voxel-wise diffusion measures with independent component analysis (ICA), and extracted the mixing weights. Third, we determined structural connectivity between Harvard Oxford atlas regions with probabilistic tractography, as well as graph measures based on these structural connectivity graphs. Classification performance for voxel-wise measures ranged between an AUC of 0.888, and 0.902. The ICA-clustered measures ranged between an AUC of 0.893, and 0.920. The AUC for the structural connectivity graph was 0.900, while graph measures based upon this graph ranged between an AUC of 0.531, and 0.840. All measures combined with a sparse group lasso resulted in an AUC of 0.896. Overall, fractional anisotropy clustered into ICA components was the best performing measure. These findings may be useful for future incorporation of diffusion MRI into protocols for AD classification, or as a starting point for early detection of AD using diffusion MRI. Copyright © 2017 Elsevier Inc. All rights reserved.

Apparent Fibre Density: a novel measure for the analysis of diffusion-weighted magnetic resonance images.

PubMed

Raffelt, David; Tournier, J-Donald; Rose, Stephen; Ridgway, Gerard R; Henderson, Robert; Crozier, Stuart; Salvado, Olivier; Connelly, Alan

2012-02-15

This article proposes a new measure called Apparent Fibre Density (AFD) for the analysis of high angular resolution diffusion-weighted images using higher-order information provided by fibre orientation distributions (FODs) computed using spherical deconvolution. AFD has the potential to provide specific information regarding differences between populations by identifying not only the location, but also the orientations along which differences exist. In this work, analytical and numerical Monte-Carlo simulations are used to support the use of the FOD amplitude as a quantitative measure (i.e. AFD) for population and longitudinal analysis. To perform robust voxel-based analysis of AFD, we present and evaluate a novel method to modulate the FOD to account for changes in fibre bundle cross-sectional area that occur during spatial normalisation. We then describe a novel approach for statistical analysis of AFD that uses cluster-based inference of differences extended throughout space and orientation. Finally, we demonstrate the capability of the proposed method by performing voxel-based AFD comparisons between a group of Motor Neurone Disease patients and healthy control subjects. A significant decrease in AFD was detected along voxels and orientations corresponding to both the corticospinal tract and corpus callosal fibres that connect the primary motor cortices. In addition to corroborating previous findings in MND, this study demonstrates the clear advantage of using this type of analysis by identifying differences along single fibre bundles in regions containing multiple fibre populations. Copyright © 2011 Elsevier Inc. All rights reserved.

An Effective Algorithm Research of Scenario Voxelization Organization and Occlusion Culling

NASA Astrophysics Data System (ADS)

Lai, Guangling; Ding, Lu; Qin, Zhiyuan; Tong, Xiaochong

2016-11-01

Compared with the traditional triangulation approaches, the voxelized point cloud data can reduce the sensitivity of scenario and complexity of calculation. While on the base of the point cloud data, implementation scenario organization could be accomplishment by subtle voxel, but it will add more memory consumption. Therefore, an effective voxel representation method is very necessary. At present, the specific study of voxel visualization algorithm is less. This paper improved the ray tracing algorithm by the characteristics of voxel configuration. Firstly, according to the scope of point cloud data, determined the scope of the pixels on the screen. Then, calculated the light vector came from each pixel. Lastly, used the rules of voxel configuration to calculate all the voxel penetrated through by light. The voxels closest to viewpoint were named visible ones, the rest were all obscured ones. This experimental showed that the method could realize voxelization organization and voxel occlusion culling of implementation scenario efficiently, and increased the render efficiency.

Absence of gender effect on children with attention-deficit/hyperactivity disorder as assessed by optimized voxel-based morphometry.

PubMed

Yang, Pinchen; Wang, Pei-Ning; Chuang, Kai-Hsiang; Jong, Yuh-Jyh; Chao, Tzu-Cheng; Wu, Ming-Ting

2008-12-30

Brain abnormalities, as determined by structural magnetic resonance imaging (MRI), have been reported in patients with attention-deficit hyperactivity disorder (ADHD); however, female subjects have been underrepresented in previous reports. In this study, we used optimized voxel-based morphometry to compare the total and regional gray matter volumes between groups of 7- to 17-year-old ADHD and healthy children (total 114 subjects). Fifty-seven children with ADHD (n=57, 35 males and 22 females) and healthy children (n=57) received MRI scans. Segmented brain MRI images were normalized into standardized stereotactic space, modulated to allow volumetric analysis, smoothed and compared at the voxel level with statistical parametric mapping. Global volumetric comparisons between groups revealed that the total brain volumes of ADHD children were smaller than those of the control children. As for the regional brain analysis, the brain volumes of ADHD children were found to be bilaterally smaller in the following regions as compared with normal control values: the caudate nucleus and the cerebellum. There were two clusters of regional decrease in the female brain, left posterior cingulum and right precuneus, as compared with the male brain. Brain regions showing the interaction effect of diagnosis and gender were negligible. These results were consistent with the hypothesized dysfunctional systems in ADHD, and they also suggested that neuroanatomical abnormalities in ADHD were not influenced by gender.

Statistical image quantification toward optimal scan fusion and change quantification

NASA Astrophysics Data System (ADS)

Potesil, Vaclav; Zhou, Xiang Sean

2007-03-01

Recent advance of imaging technology has brought new challenges and opportunities for automatic and quantitative analysis of medical images. With broader accessibility of more imaging modalities for more patients, fusion of modalities/scans from one time point and longitudinal analysis of changes across time points have become the two most critical differentiators to support more informed, more reliable and more reproducible diagnosis and therapy decisions. Unfortunately, scan fusion and longitudinal analysis are both inherently plagued with increased levels of statistical errors. A lack of comprehensive analysis by imaging scientists and a lack of full awareness by physicians pose potential risks in clinical practice. In this paper, we discuss several key error factors affecting imaging quantification, studying their interactions, and introducing a simulation strategy to establish general error bounds for change quantification across time. We quantitatively show that image resolution, voxel anisotropy, lesion size, eccentricity, and orientation are all contributing factors to quantification error; and there is an intricate relationship between voxel anisotropy and lesion shape in affecting quantification error. Specifically, when two or more scans are to be fused at feature level, optimal linear fusion analysis reveals that scans with voxel anisotropy aligned with lesion elongation should receive a higher weight than other scans. As a result of such optimal linear fusion, we will achieve a lower variance than naïve averaging. Simulated experiments are used to validate theoretical predictions. Future work based on the proposed simulation methods may lead to general guidelines and error lower bounds for quantitative image analysis and change detection.

Efficient Blockwise Permutation Tests Preserving Exchangeability

PubMed Central

Zhou, Chunxiao; Zwilling, Chris E.; Calhoun, Vince D.; Wang, Michelle Y.

2014-01-01

In this paper, we present a new blockwise permutation test approach based on the moments of the test statistic. The method is of importance to neuroimaging studies. In order to preserve the exchangeability condition required in permutation tests, we divide the entire set of data into certain exchangeability blocks. In addition, computationally efficient moments-based permutation tests are performed by approximating the permutation distribution of the test statistic with the Pearson distribution series. This involves the calculation of the first four moments of the permutation distribution within each block and then over the entire set of data. The accuracy and efficiency of the proposed method are demonstrated through simulated experiment on the magnetic resonance imaging (MRI) brain data, specifically the multi-site voxel-based morphometry analysis from structural MRI (sMRI). PMID:25289113

Dose fractionation theorem in 3-D reconstruction (tomography)

DOE Office of Scientific and Technical Information (OSTI.GOV)

Glaeser, R.M.

It is commonly assumed that the large number of projections for single-axis tomography precludes its application to most beam-labile specimens. However, Hegerl and Hoppe have pointed out that the total dose required to achieve statistical significance for each voxel of a computed 3-D reconstruction is the same as that required to obtain a single 2-D image of that isolated voxel, at the same level of statistical significance. Thus a statistically significant 3-D image can be computed from statistically insignificant projections, as along as the total dosage that is distributed among these projections is high enough that it would have resultedmore » in a statistically significant projection, if applied to only one image. We have tested this critical theorem by simulating the tomographic reconstruction of a realistic 3-D model created from an electron micrograph. The simulations verify the basic conclusions of high absorption, signal-dependent noise, varying specimen contrast and missing angular range. Furthermore, the simulations demonstrate that individual projections in the series of fractionated-dose images can be aligned by cross-correlation because they contain significant information derived from the summation of features from different depths in the structure. This latter information is generally not useful for structural interpretation prior to 3-D reconstruction, owing to the complexity of most specimens investigated by single-axis tomography. These results, in combination with dose estimates for imaging single voxels and measurements of radiation damage in the electron microscope, demonstrate that it is feasible to use single-axis tomography with soft X-ray microscopy of frozen-hydrated specimens.« less

Non-parametric model selection for subject-specific topological organization of resting-state functional connectivity.

PubMed

Ferrarini, Luca; Veer, Ilya M; van Lew, Baldur; Oei, Nicole Y L; van Buchem, Mark A; Reiber, Johan H C; Rombouts, Serge A R B; Milles, J

2011-06-01

In recent years, graph theory has been successfully applied to study functional and anatomical connectivity networks in the human brain. Most of these networks have shown small-world topological characteristics: high efficiency in long distance communication between nodes, combined with highly interconnected local clusters of nodes. Moreover, functional studies performed at high resolutions have presented convincing evidence that resting-state functional connectivity networks exhibits (exponentially truncated) scale-free behavior. Such evidence, however, was mostly presented qualitatively, in terms of linear regressions of the degree distributions on log-log plots. Even when quantitative measures were given, these were usually limited to the r(2) correlation coefficient. However, the r(2) statistic is not an optimal estimator of explained variance, when dealing with (truncated) power-law models. Recent developments in statistics have introduced new non-parametric approaches, based on the Kolmogorov-Smirnov test, for the problem of model selection. In this work, we have built on this idea to statistically tackle the issue of model selection for the degree distribution of functional connectivity at rest. The analysis, performed at voxel level and in a subject-specific fashion, confirmed the superiority of a truncated power-law model, showing high consistency across subjects. Moreover, the most highly connected voxels were found to be consistently part of the default mode network. Our results provide statistically sound support to the evidence previously presented in literature for a truncated power-law model of resting-state functional connectivity. Copyright © 2010 Elsevier Inc. All rights reserved.

VOXEL-LEVEL MAPPING OF TRACER KINETICS IN PET STUDIES: A STATISTICAL APPROACH EMPHASIZING TISSUE LIFE TABLES.

PubMed

O'Sullivan, Finbarr; Muzi, Mark; Mankoff, David A; Eary, Janet F; Spence, Alexander M; Krohn, Kenneth A

2014-06-01

Most radiotracers used in dynamic positron emission tomography (PET) scanning act in a linear time-invariant fashion so that the measured time-course data are a convolution between the time course of the tracer in the arterial supply and the local tissue impulse response, known as the tissue residue function. In statistical terms the residue is a life table for the transit time of injected radiotracer atoms. The residue provides a description of the tracer kinetic information measurable by a dynamic PET scan. Decomposition of the residue function allows separation of rapid vascular kinetics from slower blood-tissue exchanges and tissue retention. For voxel-level analysis, we propose that residues be modeled by mixtures of nonparametrically derived basis residues obtained by segmentation of the full data volume. Spatial and temporal aspects of diagnostics associated with voxel-level model fitting are emphasized. Illustrative examples, some involving cancer imaging studies, are presented. Data from cerebral PET scanning with 18 F fluoro-deoxyglucose (FDG) and 15 O water (H2O) in normal subjects is used to evaluate the approach. Cross-validation is used to make regional comparisons between residues estimated using adaptive mixture models with more conventional compartmental modeling techniques. Simulations studies are used to theoretically examine mean square error performance and to explore the benefit of voxel-level analysis when the primary interest is a statistical summary of regional kinetics. The work highlights the contribution that multivariate analysis tools and life-table concepts can make in the recovery of local metabolic information from dynamic PET studies, particularly ones in which the assumptions of compartmental-like models, with residues that are sums of exponentials, might not be certain.

TU-C-12A-02: Development of a Multiparametric Statistical Response Map for Quantitative Imaging

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bosca, R; The University of Texas MD Anderson Cancer Center, Houston, TX; Mahajan, A

2014-06-15

Purpose: Quantitative imaging biomarkers (QIB) are becoming increasingly utilized in early phase clinical trials as a means of non-invasively assessing treatment response and associated response heterogeneity. The aim of this study was to develop a flexible multiparametric statistical framework to predict voxel-by-voxel response of several potential MRI QIBs. Methods: Patients with histologically proven glioblastomas (n=11) were treated with chemoradiation (with/without bevacizumab) and underwent one baseline and two mid-treatment (3–4wks) MRIs. Dynamic contrast-enhanced (3D FSPGR, 6.3sec/phase, 0.1 mmol/kg Gd-DTPA), dynamic susceptibility contrast (2D GRE-EPI, 1.5sec/phase, 0.2mmol/kg Gd-DTPA), and diffusion tensor (2D DW-EPI, b=0, 1200 s/mm{sup 2}, 27 directions) imaging acquisitions weremore » obtained during each study. Mid-treatment and pre-treatment images were rigidly aligned, and regions of partial response (PR), stable disease (SD), and progressive disease (PD) were contoured in consensus by two experienced radiation oncologists. Voxels in these categories were used to train ordinal (PR« less

SU-E-CAMPUS-I-02: Estimation of the Dosimetric Error Caused by the Voxelization of Hybrid Computational Phantoms Using Triangle Mesh-Based Monte Carlo Transport

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lee, C; Badal, A

Purpose: Computational voxel phantom provides realistic anatomy but the voxel structure may result in dosimetric error compared to real anatomy composed of perfect surface. We analyzed the dosimetric error caused from the voxel structure in hybrid computational phantoms by comparing the voxel-based doses at different resolutions with triangle mesh-based doses. Methods: We incorporated the existing adult male UF/NCI hybrid phantom in mesh format into a Monte Carlo transport code, penMesh that supports triangle meshes. We calculated energy deposition to selected organs of interest for parallel photon beams with three mono energies (0.1, 1, and 10 MeV) in antero-posterior geometry. Wemore » also calculated organ energy deposition using three voxel phantoms with different voxel resolutions (1, 5, and 10 mm) using MCNPX2.7. Results: Comparison of organ energy deposition between the two methods showed that agreement overall improved for higher voxel resolution, but for many organs the differences were small. Difference in the energy deposition for 1 MeV, for example, decreased from 11.5% to 1.7% in muscle but only from 0.6% to 0.3% in liver as voxel resolution increased from 10 mm to 1 mm. The differences were smaller at higher energies. The number of photon histories processed per second in voxels were 6.4×10{sup 4}, 3.3×10{sup 4}, and 1.3×10{sup 4}, for 10, 5, and 1 mm resolutions at 10 MeV, respectively, while meshes ran at 4.0×10{sup 4} histories/sec. Conclusion: The combination of hybrid mesh phantom and penMesh was proved to be accurate and of similar speed compared to the voxel phantom and MCNPX. The lowest voxel resolution caused a maximum dosimetric error of 12.6% at 0.1 MeV and 6.8% at 10 MeV but the error was insignificant in some organs. We will apply the tool to calculate dose to very thin layer tissues (e.g., radiosensitive layer in gastro intestines) which cannot be modeled by voxel phantoms.« less

Segmentation-free statistical image reconstruction for polyenergetic x-ray computed tomography with experimental validation.

PubMed

Idris A, Elbakri; Fessler, Jeffrey A

2003-08-07

This paper describes a statistical image reconstruction method for x-ray CT that is based on a physical model that accounts for the polyenergetic x-ray source spectrum and the measurement nonlinearities caused by energy-dependent attenuation. Unlike our earlier work, the proposed algorithm does not require pre-segmentation of the object into the various tissue classes (e.g., bone and soft tissue) and allows mixed pixels. The attenuation coefficient of each voxel is modelled as the product of its unknown density and a weighted sum of energy-dependent mass attenuation coefficients. We formulate a penalized-likelihood function for this polyenergetic model and develop an iterative algorithm for estimating the unknown density of each voxel. Applying this method to simulated x-ray CT measurements of objects containing both bone and soft tissue yields images with significantly reduced beam hardening artefacts relative to conventional beam hardening correction methods. We also apply the method to real data acquired from a phantom containing various concentrations of potassium phosphate solution. The algorithm reconstructs an image with accurate density values for the different concentrations, demonstrating its potential for quantitative CT applications.

When the Single Matters more than the Group (II): Addressing the Problem of High False Positive Rates in Single Case Voxel Based Morphometry Using Non-parametric Statistics.

PubMed

Scarpazza, Cristina; Nichols, Thomas E; Seramondi, Donato; Maumet, Camille; Sartori, Giuseppe; Mechelli, Andrea

2016-01-01

In recent years, an increasing number of studies have used Voxel Based Morphometry (VBM) to compare a single patient with a psychiatric or neurological condition of interest against a group of healthy controls. However, the validity of this approach critically relies on the assumption that the single patient is drawn from a hypothetical population with a normal distribution and variance equal to that of the control group. In a previous investigation, we demonstrated that family-wise false positive error rate (i.e., the proportion of statistical comparisons yielding at least one false positive) in single case VBM are much higher than expected (Scarpazza et al., 2013). Here, we examine whether the use of non-parametric statistics, which does not rely on the assumptions of normal distribution and equal variance, would enable the investigation of single subjects with good control of false positive risk. We empirically estimated false positive rates (FPRs) in single case non-parametric VBM, by performing 400 statistical comparisons between a single disease-free individual and a group of 100 disease-free controls. The impact of smoothing (4, 8, and 12 mm) and type of pre-processing (Modulated, Unmodulated) was also examined, as these factors have been found to influence FPRs in previous investigations using parametric statistics. The 400 statistical comparisons were repeated using two independent, freely available data sets in order to maximize the generalizability of the results. We found that the family-wise error rate was 5% for increases and 3.6% for decreases in one data set; and 5.6% for increases and 6.3% for decreases in the other data set (5% nominal). Further, these results were not dependent on the level of smoothing and modulation. Therefore, the present study provides empirical evidence that single case VBM studies with non-parametric statistics are not susceptible to high false positive rates. The critical implication of this finding is that VBM can be used to characterize neuroanatomical alterations in individual subjects as long as non-parametric statistics are employed.

A Voxel-Based Approach to Explore Local Dose Differences Associated With Radiation-Induced Lung Damage

DOE Office of Scientific and Technical Information (OSTI.GOV)

Palma, Giuseppe; Monti, Serena; D'Avino, Vittoria

Purpose: To apply a voxel-based (VB) approach aimed at exploring local dose differences associated with late radiation-induced lung damage (RILD). Methods and Materials: An interinstitutional database of 98 patients who were Hodgkin lymphoma (HL) survivors treated with postchemotherapy supradiaphragmatic radiation therapy was analyzed in the study. Eighteen patients experienced late RILD, classified according to the Radiation Therapy Oncology Group scoring system. Each patient's computed tomographic (CT) scan was normalized to a single reference case anatomy (common coordinate system, CCS) through a log-diffeomorphic approach. The obtained deformation fields were used to map the dose of each patient into the CCS. Themore » coregistration robustness and the dose mapping accuracy were evaluated by geometric and dose scores. Two different statistical mapping schemes for nonparametric multiple permutation inference on dose maps were applied, and the corresponding P<.05 significance lung subregions were generated. A receiver operating characteristic (ROC)-based test was performed on the mean dose extracted from each subregion. Results: The coregistration process resulted in a geometrically robust and accurate dose warping. A significantly higher dose was consistently delivered to RILD patients in voxel clusters near the peripheral medial-basal portion of the lungs. The area under the ROC curves (AUC) from the mean dose of the voxel clusters was higher than the corresponding AUC derived from the total lung mean dose. Conclusions: We implemented a framework including a robust registration process and a VB approach accounting for the multiple comparison problem in dose-response modeling, and applied it to a cohort of HL survivors to explore a local dose–RILD relationship in the lungs. Patients with RILD received a significantly greater dose in parenchymal regions where low doses (∼6 Gy) were delivered. Interestingly, the relation between differences in the high-dose range and RILD seems to lack a clear spatial signature.« less

Imaging predictors of poststroke depression: methodological factors in voxel-based analysis

PubMed Central

Gozzi, Sophia A; Wood, Amanda G; Chen, Jian; Vaddadi, Krishnarao; Phan, Thanh G

2014-01-01

Objective The purpose of this study was to explore the relationship between lesion location and poststroke depression using statistical parametric mapping. Methods First episode patients with stroke were assessed within 12 days and at 1-month poststroke. Patients with an a priori defined cut-off score of 11 on the Hospital Anxiety and Depression Scale (HADS) at follow-up were further assessed using the Mini-International Neuropsychiatric Interview (MINI) to confirm a clinical diagnosis of major or minor depression in accordance with Diagnostic and Statistical Manual-IV (DSM-IV) inclusion criteria. Participants were included if they were aged 18–85 years, proficient in English and eligible for MRI. Patients were excluded if they had a confounding diagnosis such as major depressive disorder at the time of admission, a neurodegenerative disease, epilepsy or an imminently life-threatening comorbid illness, subarachnoid or subdural stroke, a second episode of stroke before follow-up and/or a serious impairment of consciousness or language. Infarcts observed on MRI scans were manually segmented into binary images, linearly registered into a common stereotaxic coordinate space. Using statistical parametric mapping, we compared infarct patterns in patients with stroke with and without depression. Results 27% (15/55 patients) met criteria for depression at follow-up. Mean infarct volume was 19±53 mL and National Institute of Health Stroke Scale (NIHSS) at Time 1 (within 12 days of stroke) was 4±4, indicating a sample of mild strokes. No voxels or clusters were significant after a multiple comparison correction was applied (p>0.05). Examination of infarct maps showed that there was minimal overlap of infarct location between patients, thus invalidating the voxel comparison analysis. Conclusions This study provided inconclusive evidence for the association between infarcts in a specific region and poststroke depression. PMID:25001395

Real-time 3D human pose recognition from reconstructed volume via voxel classifiers

NASA Astrophysics Data System (ADS)

Yoo, ByungIn; Choi, Changkyu; Han, Jae-Joon; Lee, Changkyo; Kim, Wonjun; Suh, Sungjoo; Park, Dusik; Kim, Junmo

2014-03-01

This paper presents a human pose recognition method which simultaneously reconstructs a human volume based on ensemble of voxel classifiers from a single depth image in real-time. The human pose recognition is a difficult task since a single depth camera can capture only visible surfaces of a human body. In order to recognize invisible (self-occluded) surfaces of a human body, the proposed algorithm employs voxel classifiers trained with multi-layered synthetic voxels. Specifically, ray-casting onto a volumetric human model generates a synthetic voxel, where voxel consists of a 3D position and ID corresponding to the body part. The synthesized volumetric data which contain both visible and invisible body voxels are utilized to train the voxel classifiers. As a result, the voxel classifiers not only identify the visible voxels but also reconstruct the 3D positions and the IDs of the invisible voxels. The experimental results show improved performance on estimating the human poses due to the capability of inferring the invisible human body voxels. It is expected that the proposed algorithm can be applied to many fields such as telepresence, gaming, virtual fitting, wellness business, and real 3D contents control on real 3D displays.

A voxel visualization and analysis system based on AutoCAD

NASA Astrophysics Data System (ADS)

Marschallinger, Robert

1996-05-01

A collection of AutoLISP programs is presented which enable the visualization and analysis of voxel models by AutoCAD rel. 12/rel. 13. The programs serve as an interactive, graphical front end for manipulating the results of three-dimensional modeling software producing block estimation data. ASCII data files describing geometry and attributes per estimation block are imported and stored as a voxel array. Each voxel may contain multiple attributes, therefore different parameters may be incorporated in one voxel array. Voxel classification is implemented on a layer basis providing flexible treatment of voxel classes such as recoloring, peeling, or volumetry. A versatile clipping tool enables slicing voxel arrays according to combinations of three perpendicular clipping planes. The programs feature an up-to-date, graphical user interface for user-friendly operation by non AutoCAD specialists.

Reduced structural connectivity within a prefrontal-motor-subcortical network in amyotrophic lateral sclerosis.

PubMed

Buchanan, Colin R; Pettit, Lewis D; Storkey, Amos J; Abrahams, Sharon; Bastin, Mark E

2015-05-01

To investigate white matter structural connectivity changes associated with amyotrophic lateral sclerosis (ALS) using network analysis and compare the results with those obtained using standard voxel-based methods, specifically Tract-based Spatial Statistics (TBSS). MRI data were acquired from 30 patients with ALS and 30 age-matched healthy controls. For each subject, 85 grey matter regions (network nodes) were identified from high resolution structural MRI, and network connections formed from the white matter tracts generated by diffusion MRI and probabilistic tractography. Whole-brain networks were constructed using strong constraints on anatomical plausibility and a weighting reflecting tract-averaged fractional anisotropy (FA). Analysis using Network-based Statistics (NBS), without a priori selected regions, identified an impaired motor-frontal-subcortical subnetwork (10 nodes and 12 bidirectional connections), consistent with upper motor neuron pathology, in the ALS group compared with the controls (P = 0.020). Reduced FA in three of the impaired network connections, which involved fibers of the corticospinal tract, correlated with rate of disease progression (P ≤ 0.024). A novel network-tract comparison revealed that the connections involved in the affected network had a strong correspondence (mean overlap of 86.2%) with white matter tracts identified as having reduced FA compared with the control group using TBSS. These findings suggest that white matter degeneration in ALS is strongly linked to the motor cortex, and that impaired structural networks identified using NBS have a strong correspondence to affected white matter tracts identified using more conventional voxel-based methods. © 2014 Wiley Periodicals, Inc.

High resolution, MRI-based, segmented, computerized head phantom

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zubal, I.G.; Harrell, C.R.; Smith, E.O.

1999-01-01

The authors have created a high-resolution software phantom of the human brain which is applicable to voxel-based radiation transport calculations yielding nuclear medicine simulated images and/or internal dose estimates. A software head phantom was created from 124 transverse MRI images of a healthy normal individual. The transverse T2 slices, recorded in a 256x256 matrix from a GE Signa 2 scanner, have isotropic voxel dimensions of 1.5 mm and were manually segmented by the clinical staff. Each voxel of the phantom contains one of 62 index numbers designating anatomical, neurological, and taxonomical structures. The result is stored as a 256x256x128 bytemore » array. Internal volumes compare favorably to those described in the ICRP Reference Man. The computerized array represents a high resolution model of a typical human brain and serves as a voxel-based anthropomorphic head phantom suitable for computer-based modeling and simulation calculations. It offers an improved realism over previous mathematically described software brain phantoms, and creates a reference standard for comparing results of newly emerging voxel-based computations. Such voxel-based computations lead the way to developing diagnostic and dosimetry calculations which can utilize patient-specific diagnostic images. However, such individualized approaches lack fast, automatic segmentation schemes for routine use; therefore, the high resolution, typical head geometry gives the most realistic patient model currently available.« less

On Voxel based Iso-Tumor Control Probabilty and Iso-Complication Maps for Selective Boosting and Selective Avoidance Intensity Modulated Radiotherapy.

PubMed

Kim, Yusung; Tomé, Wolfgang A

2008-01-01

Voxel based iso-Tumor Control Probability (TCP) maps and iso-Complication maps are proposed as a plan-review tool especially for functional image-guided intensity-modulated radiotherapy (IMRT) strategies such as selective boosting (dose painting) and conformal avoidance IMRT. The maps employ voxel-based phenomenological biological dose-response models for target volumes and normal organs. Two IMRT strategies for prostate cancer, namely conventional uniform IMRT delivering an EUD = 84 Gy (equivalent uniform dose) to the entire PTV and selective boosting delivering an EUD = 82 Gy to the entire PTV, are investigated, to illustrate the advantages of this approach over iso-dose maps. Conventional uniform IMRT did yield a more uniform isodose map to the entire PTV while selective boosting did result in a nonuniform isodose map. However, when employing voxel based iso-TCP maps selective boosting exhibited a more uniform tumor control probability map compared to what could be achieved using conventional uniform IMRT, which showed TCP cold spots in high-risk tumor subvolumes despite delivering a higher EUD to the entire PTV. Voxel based iso-Complication maps are presented for rectum and bladder, and their utilization for selective avoidance IMRT strategies are discussed. We believe as the need for functional image guided treatment planning grows, voxel based iso-TCP and iso-Complication maps will become an important tool to assess the integrity of such treatment plans.

Frontotemporal alterations in pediatric bipolar disorder: results of a voxel-based morphometry study.

PubMed

Dickstein, Daniel P; Milham, Michael P; Nugent, Allison C; Drevets, Wayne C; Charney, Dennis S; Pine, Daniel S; Leibenluft, Ellen

2005-07-01

While numerous magnetic resonance imaging (MRI) studies have evaluated adults with bipolar disorder (BPD), few have examined MRI changes in children with BPD. To determine volume alterations in children with BPD using voxel-based morphometry, an automated MRI analysis method with reduced susceptibility to various biases. A priori regions of interest included amygdala, accumbens, hippocampus, dorsolateral prefrontal cortex (DLPFC), and orbitofrontal cortex. Ongoing study of the pathophysiology of pediatric BPD. Intramural National Institute of Mental Health; approved by the institutional review board. Patients Pediatric subjects with BPD (n = 20) with at least 1 manic or hypomanic episode meeting strict DSM-IV criteria for duration and elevated, expansive mood. Controls (n = 20) and their first-degree relatives lacked psychiatric disorders. Groups were matched for age and sex and did not differ in IQ. With a 1.5-T MRI machine, we collected 1.2-mm axial sections (124 per subject) with an axial 3-dimensional spoiled gradient recalled echo in the steady state sequence. Image analysis was by optimized voxel-based morphometry. Subjects with BPD had reduced gray matter volume in the left DLPFC. With a less conservative statistical threshold, additional gray matter reductions were found in the left accumbens and left amygdala. No difference was found in the hippocampus or orbitofrontal cortex. Our results are consistent with data implicating the prefrontal cortex in emotion regulation, a process that is perturbed in BPD. Reductions in amygdala and accumbens volumes are consistent with neuropsychological data on pediatric BPD. Further study is required to determine the relationship between these findings in children and adults with BPD.

Long-term white matter tract reorganization following prolonged febrile seizures.

PubMed

Pujar, Suresh S; Seunarine, Kiran K; Martinos, Marina M; Neville, Brian G R; Scott, Rod C; Chin, Richard F M; Clark, Chris A

2017-05-01

Diffusion magnetic resonance imaging (MRI) studies have demonstrated acute white matter changes following prolonged febrile seizures (PFS), but their longer-term evolution is unknown. We investigated a population-based cohort to determine white matter diffusion properties 8 years after PFS. We used diffusion tensor imaging (DTI) and applied Tract-Based Spatial Statistics for voxel-wise comparison of white matter microstructure between 26 children with PFS and 27 age-matched healthy controls. Age, gender, handedness, and hippocampal volumes were entered as covariates for voxel-wise analysis. Mean duration between the episode of PFS and follow-up was 8.2 years (range 6.7-9.6). All children were neurologically normal, and had normal conventional neuroimaging. On voxel-wise analysis, compared to controls, the PFS group had (1) increased fractional anisotropy in early maturing central white matter tracts, (2) increased mean and axial diffusivity in several peripheral white matter tracts and late-maturing central white matter tracts, and (3) increased radial diffusivity in peripheral white matter tracts. None of the tracts had reduced fractional anisotropy or diffusivity indices in the PFS group. In this homogeneous, population-based sample, we found increased fractional anisotropy in early maturing central white matter tracts and increased mean and axial diffusivity with/without increased radial diffusivity in several late-maturing peripheral white matter tracts 8 years post-PFS. We propose disruption in white matter maturation secondary to seizure-induced axonal injury, with subsequent neuroplasticity and microstructural reorganization as a plausible explanation. © 2017 The Authors. Epilepsia published by Wiley Periodicals, Inc. on behalf of International League Against Epilepsy.

Mapping the MRI voxel volume in which thermal noise matches physiological noise--implications for fMRI.

PubMed

Bodurka, J; Ye, F; Petridou, N; Murphy, K; Bandettini, P A

2007-01-15

This work addresses the choice of the imaging voxel volume in blood oxygen level dependent (BOLD) functional magnetic resonance imaging (fMRI). Noise of physiological origin that is present in the voxel time course is a prohibitive factor in the detection of small activation-induced BOLD signal changes. If the physiological noise contribution dominates over the temporal fluctuation contribution in the imaging voxel, further increases in the voxel signal-to-noise ratio (SNR) will have diminished corresponding increases in temporal signal-to-noise (TSNR), resulting in reduced corresponding increases in the ability to detect activation induced signal changes. On the other hand, if the thermal and system noise dominate (suggesting a relatively low SNR) further decreases in SNR can prohibit detection of activation-induced signal changes. Here we have proposed and called the "suggested" voxel volume for fMRI the volume where thermal plus system-related and physiological noise variances are equal. Based on this condition we have created maps of fMRI suggested voxel volume from our experimental data at 3T, since this value will spatially vary depending on the contribution of physiologic noise in each voxel. Based on our fast EPI segmentation technique we have found that for gray matter (GM), white matter (WM), and cerebral spinal fluid (CSF) brain compartments the mean suggested cubical voxel volume is: (1.8 mm)3, (2.1 mm)3 and (1.4 mm)3, respectively. Serendipitously, (1.8 mm)3 cubical voxel volume for GM approximately matches the cortical thickness, thus optimizing BOLD contrast by minimizing partial volume averaging. The introduced suggested fMRI voxel volume can be a useful parameter for choice of imaging volume for functional studies.

Learning discriminative functional network features of schizophrenia

NASA Astrophysics Data System (ADS)

Gheiratmand, Mina; Rish, Irina; Cecchi, Guillermo; Brown, Matthew; Greiner, Russell; Bashivan, Pouya; Polosecki, Pablo; Dursun, Serdar

2017-03-01

Associating schizophrenia with disrupted functional connectivity is a central idea in schizophrenia research. However, identifying neuroimaging-based features that can serve as reliable "statistical biomarkers" of the disease remains a challenging open problem. We argue that generalization accuracy and stability of candidate features ("biomarkers") must be used as additional criteria on top of standard significance tests in order to discover more robust biomarkers. Generalization accuracy refers to the utility of biomarkers for making predictions about individuals, for example discriminating between patients and controls, in novel datasets. Feature stability refers to the reproducibility of the candidate features across different datasets. Here, we extracted functional connectivity network features from fMRI data at both high-resolution (voxel-level) and a spatially down-sampled lower-resolution ("supervoxel" level). At the supervoxel level, we used whole-brain network links, while at the voxel level, due to the intractably large number of features, we sampled a subset of them. We compared statistical significance, stability and discriminative utility of both feature types in a multi-site fMRI dataset, composed of schizophrenia patients and healthy controls. For both feature types, a considerable fraction of features showed significant differences between the two groups. Also, both feature types were similarly stable across multiple data subsets. However, the whole-brain supervoxel functional connectivity features showed a higher cross-validation classification accuracy of 78.7% vs. 72.4% for the voxel-level features. Cross-site variability and heterogeneity in the patient samples in the multi-site FBIRN dataset made the task more challenging compared to single-site studies. The use of the above methodology in combination with the fully data-driven approach using the whole brain information have the potential to shed light on "biomarker discovery" in schizophrenia.

Patch-based generation of a pseudo CT from conventional MRI sequences for MRI-only radiotherapy of the brain

DOE Office of Scientific and Technical Information (OSTI.GOV)

Andreasen, Daniel, E-mail: dana@dtu.dk; Van Leemput, Koen; Hansen, Rasmus H.

Purpose: In radiotherapy (RT) based on magnetic resonance imaging (MRI) as the only modality, the information on electron density must be derived from the MRI scan by creating a so-called pseudo computed tomography (pCT). This is a nontrivial task, since the voxel-intensities in an MRI scan are not uniquely related to electron density. To solve the task, voxel-based or atlas-based models have typically been used. The voxel-based models require a specialized dual ultrashort echo time MRI sequence for bone visualization and the atlas-based models require deformable registrations of conventional MRI scans. In this study, we investigate the potential of amore » patch-based method for creating a pCT based on conventional T{sub 1}-weighted MRI scans without using deformable registrations. We compare this method against two state-of-the-art methods within the voxel-based and atlas-based categories. Methods: The data consisted of CT and MRI scans of five cranial RT patients. To compare the performance of the different methods, a nested cross validation was done to find optimal model parameters for all the methods. Voxel-wise and geometric evaluations of the pCTs were done. Furthermore, a radiologic evaluation based on water equivalent path lengths was carried out, comparing the upper hemisphere of the head in the pCT and the real CT. Finally, the dosimetric accuracy was tested and compared for a photon treatment plan. Results: The pCTs produced with the patch-based method had the best voxel-wise, geometric, and radiologic agreement with the real CT, closely followed by the atlas-based method. In terms of the dosimetric accuracy, the patch-based method had average deviations of less than 0.5% in measures related to target coverage. Conclusions: We showed that a patch-based method could generate an accurate pCT based on conventional T{sub 1}-weighted MRI sequences and without deformable registrations. In our evaluations, the method performed better than existing voxel-based and atlas-based methods and showed a promising potential for RT of the brain based only on MRI.« less

Converging evidence for abnormalities of the prefrontal cortex and evaluation of midsagittal structures in pediatric PTSD: an MRI study

PubMed Central

Carrion, Victor G.; Weems, Carl F.; Watson, Christa; Eliez, Stephan; Menon, Vinod; Reiss, Allan L.

2009-01-01

Objective Volumetric imaging research has shown abnormal brain morphology in posttraumatic stress disorder (PTSD) when compared to controls. We present results on a study of brain morphology in the prefrontal cortex (PFC) and midline structures, via indices of gray matter volume and density, in pediatric PTSD. We hypothesized that both methods would demonstrate aberrant morphology in the PFC. Further, we hypothesized aberrant brainstem anatomy and reduced corpus collosum volume in children with PTSD. Methods Twenty-four children (aged 7-14) with history of interpersonal trauma and 24 age, and gender matched controls underwent structural magnetic resonance imaging. Images of the PFC and midline brain structures were first analyzed using volumetric image analysis. The PFC data were then compared with whole-brain voxel-based techniques using statistical parametric mapping (SPM). Results The PTSD group showed significant increased gray matter volume in the right and left inferior and superior quadrants of the prefrontal cortex and smaller gray matter volume in pons, and posterior vermis areas by volumetric image analysis. The voxel-byvoxel group comparisons demonstrated increased gray matter density mostly localized to ventral PFC as compared to the control group. Conclusions Abnormal frontal lobe morphology, as revealed by separate-complementary image analysis methods, and reduced pons and posterior vermis areas are associated with pediatric PTSD. Voxel-based morphometry may help to corroborate and further localize data obtained by volume of interest methods in PTSD. PMID:19349151

Cluster analysis of dynamic contrast enhanced MRI reveals tumor subregions related to locoregional relapse for cervical cancer patients.

PubMed

Torheim, Turid; Groendahl, Aurora R; Andersen, Erlend K F; Lyng, Heidi; Malinen, Eirik; Kvaal, Knut; Futsaether, Cecilia M

2016-11-01

Solid tumors are known to be spatially heterogeneous. Detection of treatment-resistant tumor regions can improve clinical outcome, by enabling implementation of strategies targeting such regions. In this study, K-means clustering was used to group voxels in dynamic contrast enhanced magnetic resonance images (DCE-MRI) of cervical cancers. The aim was to identify clusters reflecting treatment resistance that could be used for targeted radiotherapy with a dose-painting approach. Eighty-one patients with locally advanced cervical cancer underwent DCE-MRI prior to chemoradiotherapy. The resulting image time series were fitted to two pharmacokinetic models, the Tofts model (yielding parameters K trans and ν e ) and the Brix model (A Brix , k ep and k el ). K-means clustering was used to group similar voxels based on either the pharmacokinetic parameter maps or the relative signal increase (RSI) time series. The associations between voxel clusters and treatment outcome (measured as locoregional control) were evaluated using the volume fraction or the spatial distribution of each cluster. One voxel cluster based on the RSI time series was significantly related to locoregional control (adjusted p-value 0.048). This cluster consisted of low-enhancing voxels. We found that tumors with poor prognosis had this RSI-based cluster gathered into few patches, making this cluster a potential candidate for targeted radiotherapy. None of the voxels clusters based on Tofts or Brix parameter maps were significantly related to treatment outcome. We identified one group of tumor voxels significantly associated with locoregional relapse that could potentially be used for dose painting. This tumor voxel cluster was identified using the raw MRI time series rather than the pharmacokinetic maps.

Cluster Analysis of Time-Dependent Crystallographic Data: Direct Identification of Time-Independent Structural Intermediates

PubMed Central

Kostov, Konstantin S.; Moffat, Keith

2011-01-01

The initial output of a time-resolved macromolecular crystallography experiment is a time-dependent series of difference electron density maps that displays the time-dependent changes in underlying structure as a reaction progresses. The goal is to interpret such data in terms of a small number of crystallographically refinable, time-independent structures, each associated with a reaction intermediate; to establish the pathways and rate coefficients by which these intermediates interconvert; and thereby to elucidate a chemical kinetic mechanism. One strategy toward achieving this goal is to use cluster analysis, a statistical method that groups objects based on their similarity. If the difference electron density at a particular voxel in the time-dependent difference electron density (TDED) maps is sensitive to the presence of one and only one intermediate, then its temporal evolution will exactly parallel the concentration profile of that intermediate with time. The rationale is therefore to cluster voxels with respect to the shapes of their TDEDs, so that each group or cluster of voxels corresponds to one structural intermediate. Clusters of voxels whose TDEDs reflect the presence of two or more specific intermediates can also be identified. From such groupings one can then infer the number of intermediates, obtain their time-independent difference density characteristics, and refine the structure of each intermediate. We review the principles of cluster analysis and clustering algorithms in a crystallographic context, and describe the application of the method to simulated and experimental time-resolved crystallographic data for the photocycle of photoactive yellow protein. PMID:21244840

Adjusted scaling of FDG positron emission tomography images for statistical evaluation in patients with suspected Alzheimer's disease.

PubMed

Buchert, Ralph; Wilke, Florian; Chakrabarti, Bhismadev; Martin, Brigitte; Brenner, Winfried; Mester, Janos; Clausen, Malte

2005-10-01

Statistical parametric mapping (SPM) gained increasing acceptance for the voxel-based statistical evaluation of brain positron emission tomography (PET) with the glucose analog 2-[18F]-fluoro-2-deoxy-d-glucose (FDG) in patients with suspected Alzheimer's disease (AD). To increase the sensitivity for detection of local changes, individual differences of total brain FDG uptake are usually compensated for by proportional scaling. However, in cases of extensive hypometabolic areas, proportional scaling overestimates scaled uptake. This may cause significant underestimation of the extent of hypometabolic areas by the statistical test. To detect this problem, the authors tested for hypermetabolism. In patients with no visual evidence of true focal hypermetabolism, significant clusters of hypermetabolism in the presence of extended hypometabolism were interpreted as false-positive findings, indicating relevant overestimation of scaled uptake. In this case, scaled uptake was reduced step by step until there were no more significant clusters of hypermetabolism. In 22 consecutive patients with suspected AD, proportional scaling resulted in relevant overestimation of scaled uptake in 9 patients. Scaled uptake had to be reduced by 11.1% +/- 5.3% in these cases to eliminate the artifacts. Adjusted scaling resulted in extension of existing and appearance of new clusters of hypometabolism. Total volume of the additional voxels with significant hypometabolism depended linearly on the extent of the additional scaling and was 202 +/- 118 mL on average. Adjusted scaling helps to identify characteristic metabolic patterns in patients with suspected AD. It is expected to increase specificity of FDGPET in this group of patients.

A voxel-based technique to estimate the volume of trees from terrestrial laser scanner data

NASA Astrophysics Data System (ADS)

Bienert, A.; Hess, C.; Maas, H.-G.; von Oheimb, G.

2014-06-01

The precise determination of the volume of standing trees is very important for ecological and economical considerations in forestry. If terrestrial laser scanner data are available, a simple approach for volume determination is given by allocating points into a voxel structure and subsequently counting the filled voxels. Generally, this method will overestimate the volume. The paper presents an improved algorithm to estimate the wood volume of trees using a voxel-based method which will correct for the overestimation. After voxel space transformation, each voxel which contains points is reduced to the volume of its surrounding bounding box. In a next step, occluded (inner stem) voxels are identified by a neighbourhood analysis sweeping in the X and Y direction of each filled voxel. Finally, the wood volume of the tree is composed by the sum of the bounding box volumes of the outer voxels and the volume of all occluded inner voxels. Scan data sets from several young Norway maple trees (Acer platanoides) were used to analyse the algorithm. Therefore, the scanned trees as well as their representing point clouds were separated in different components (stem, branches) to make a meaningful comparison. Two reference measurements were performed for validation: A direct wood volume measurement by placing the tree components into a water tank, and a frustum calculation of small trunk segments by measuring the radii along the trunk. Overall, the results show slightly underestimated volumes (-0.3% for a probe of 13 trees) with a RMSE of 11.6% for the individual tree volume calculated with the new approach.

The effect of voxel size on dose distribution in Varian Clinac iX 6 MV photon beam using Monte Carlo simulation

NASA Astrophysics Data System (ADS)

Yani, Sitti; Dirgayussa, I. Gde E.; Rhani, Moh. Fadhillah; Haryanto, Freddy; Arif, Idam

2015-09-01

Recently, Monte Carlo (MC) calculation method has reported as the most accurate method of predicting dose distributions in radiotherapy. The MC code system (especially DOSXYZnrc) has been used to investigate the different voxel (volume elements) sizes effect on the accuracy of dose distributions. To investigate this effect on dosimetry parameters, calculations were made with three different voxel sizes. The effects were investigated with dose distribution calculations for seven voxel sizes: 1 × 1 × 0.1 cm3, 1 × 1 × 0.5 cm3, and 1 × 1 × 0.8 cm3. The 1 × 109 histories were simulated in order to get statistical uncertainties of 2%. This simulation takes about 9-10 hours to complete. Measurements are made with field sizes 10 × 10 cm2 for the 6 MV photon beams with Gaussian intensity distribution FWHM 0.1 cm and SSD 100.1 cm. MC simulated and measured dose distributions in a water phantom. The output of this simulation i.e. the percent depth dose and dose profile in dmax from the three sets of calculations are presented and comparisons are made with the experiment data from TTSH (Tan Tock Seng Hospital, Singapore) in 0-5 cm depth. Dose that scored in voxels is a volume averaged estimate of the dose at the center of a voxel. The results in this study show that the difference between Monte Carlo simulation and experiment data depend on the voxel size both for percent depth dose (PDD) and profile dose. PDD scan on Z axis (depth) of water phantom, the big difference obtain in the voxel size 1 × 1 × 0.8 cm3 about 17%. In this study, the profile dose focused on high gradient dose area. Profile dose scan on Y axis and the big difference get in the voxel size 1 × 1 × 0.1 cm3 about 12%. This study demonstrated that the arrange voxel in Monte Carlo simulation becomes important.

Hybrid computational phantoms of the male and female newborn patient: NURBS-based whole-body models

NASA Astrophysics Data System (ADS)

Lee, Choonsik; Lodwick, Daniel; Hasenauer, Deanna; Williams, Jonathan L.; Lee, Choonik; Bolch, Wesley E.

2007-07-01

Anthropomorphic computational phantoms are computer models of the human body for use in the evaluation of dose distributions resulting from either internal or external radiation sources. Currently, two classes of computational phantoms have been developed and widely utilized for organ dose assessment: (1) stylized phantoms and (2) voxel phantoms which describe the human anatomy via mathematical surface equations or 3D voxel matrices, respectively. Although stylized phantoms based on mathematical equations can be very flexible in regard to making changes in organ position and geometrical shape, they are limited in their ability to fully capture the anatomic complexities of human internal anatomy. In turn, voxel phantoms have been developed through image-based segmentation and correspondingly provide much better anatomical realism in comparison to simpler stylized phantoms. However, they themselves are limited in defining organs presented in low contrast within either magnetic resonance or computed tomography images—the two major sources in voxel phantom construction. By definition, voxel phantoms are typically constructed via segmentation of transaxial images, and thus while fine anatomic features are seen in this viewing plane, slice-to-slice discontinuities become apparent in viewing the anatomy of voxel phantoms in the sagittal or coronal planes. This study introduces the concept of a hybrid computational newborn phantom that takes full advantage of the best features of both its stylized and voxel counterparts: flexibility in phantom alterations and anatomic realism. Non-uniform rational B-spline (NURBS) surfaces, a mathematical modeling tool traditionally applied to graphical animation studies, was adopted to replace the limited mathematical surface equations of stylized phantoms. A previously developed whole-body voxel phantom of the newborn female was utilized as a realistic anatomical framework for hybrid phantom construction. The construction of a hybrid phantom is performed in three steps: polygonization of the voxel phantom, organ modeling via NURBS surfaces and phantom voxelization. Two 3D graphic tools, 3D-DOCTOR™ and Rhinoceros™, were utilized to polygonize the newborn voxel phantom and generate NURBS surfaces, while an in-house MATLAB™ code was used to voxelize the resulting NURBS model into a final computational phantom ready for use in Monte Carlo radiation transport calculations. A total of 126 anatomical organ and tissue models, including 38 skeletal sites and 31 cartilage sites, were described within the hybrid phantom using either NURBS or polygon surfaces. A male hybrid newborn phantom was constructed following the development of the female phantom through the replacement of female-specific organs with male-specific organs. The outer body contour and internal anatomy of the NURBS-based phantoms were adjusted to match anthropometric and reference newborn data reported by the International Commission on Radiological Protection in their Publication 89. The voxelization process was designed to accurately convert NURBS models to a voxel phantom with minimum volumetric change. A sensitivity study was additionally performed to better understand how the meshing tolerance and voxel resolution would affect volumetric changes between the hybrid-NURBS and hybrid-voxel phantoms. The male and female hybrid-NURBS phantoms were constructed in a manner so that all internal organs approached their ICRP reference masses to within 1%, with the exception of the skin (-6.5% relative error) and brain (-15.4% relative error). Both hybrid-voxel phantoms were constructed with an isotropic voxel resolution of 0.663 mm—equivalent to the ICRP 89 reference thickness of the newborn skin (dermis and epidermis). Hybrid-NURBS phantoms used to create their voxel counterpart retain the non-uniform scalability of stylized phantoms, while maintaining the anatomic realism of segmented voxel phantoms with respect to organ shape, depth and inter-organ positioning. This work was supported by the National Cancer Institute.

Whole-body voxel-based personalized dosimetry: Multiple voxel S-value approach for heterogeneous media with non-uniform activity distributions.

PubMed

Lee, Min Sun; Kim, Joong Hyun; Paeng, Jin Chul; Kang, Keon Wook; Jeong, Jae Min; Lee, Dong Soo; Lee, Jae Sung

2017-12-14

Personalized dosimetry with high accuracy is becoming more important because of the growing interests in personalized medicine and targeted radionuclide therapy. Voxel-based dosimetry using dose point kernel or voxel S-value (VSV) convolution is available. However, these approaches do not consider medium heterogeneity. Here, we propose a new method for whole-body voxel-based personalized dosimetry for heterogeneous media with non-uniform activity distributions, which is referred to as the multiple VSV approach. Methods: The multiple numbers (N) of VSVs for media with different densities covering the whole-body density ranges were used instead of using only a single VSV for water. The VSVs were pre-calculated using GATE Monte Carlo simulation; those were convoluted with the time-integrated activity to generate density-specific dose maps. Computed tomography-based segmentation was conducted to generate binary maps for each density region. The final dose map was acquired by the summation of N segmented density-specific dose maps. We tested several sets of VSVs with different densities: N = 1 (single water VSV), 4, 6, 8, 10, and 20. To validate the proposed method, phantom and patient studies were conducted and compared with direct Monte Carlo, which was considered the ground truth. Finally, patient dosimetry (10 subjects) was conducted using the multiple VSV approach and compared with the single VSV and organ-based dosimetry approaches. Errors at the voxel- and organ-levels were reported for eight organs. Results: In the phantom and patient studies, the multiple VSV approach showed significant improvements regarding voxel-level errors, especially for the lung and bone regions. As N increased, voxel-level errors decreased, although some overestimations were observed at lung boundaries. In the case of multiple VSVs ( N = 8), we achieved voxel-level errors of 2.06%. In the dosimetry study, our proposed method showed much improved results compared to the single VSV and organ-based dosimetry. Errors at the organ-level were -6.71%, 2.17%, and 227.46% for the single VSV, multiple VSV, and organ-based dosimetry, respectively. Conclusion: The multiple VSV approach for heterogeneous media with non-uniform activity distributions offers fast personalized dosimetry at whole-body level, yielding results comparable to those of the direct Monte Carlo approach. Copyright © 2017 by the Society of Nuclear Medicine and Molecular Imaging, Inc.

TU-AB-202-11: Tumor Segmentation by Fusion of Multi-Tracer PET Images Using Copula Based Statistical Methods

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lapuyade-Lahorgue, J; Ruan, S; Li, H

Purpose: Multi-tracer PET imaging is getting more attention in radiotherapy by providing additional tumor volume information such as glucose and oxygenation. However, automatic PET-based tumor segmentation is still a very challenging problem. We propose a statistical fusion approach to joint segment the sub-area of tumors from the two tracers FDG and FMISO PET images. Methods: Non-standardized Gamma distributions are convenient to model intensity distributions in PET. As a serious correlation exists in multi-tracer PET images, we proposed a new fusion method based on copula which is capable to represent dependency between different tracers. The Hidden Markov Field (HMF) model ismore » used to represent spatial relationship between PET image voxels and statistical dynamics of intensities for each modality. Real PET images of five patients with FDG and FMISO are used to evaluate quantitatively and qualitatively our method. A comparison between individual and multi-tracer segmentations was conducted to show advantages of the proposed fusion method. Results: The segmentation results show that fusion with Gaussian copula can receive high Dice coefficient of 0.84 compared to that of 0.54 and 0.3 of monomodal segmentation results based on individual segmentation of FDG and FMISO PET images. In addition, high correlation coefficients (0.75 to 0.91) for the Gaussian copula for all five testing patients indicates the dependency between tumor regions in the multi-tracer PET images. Conclusion: This study shows that using multi-tracer PET imaging can efficiently improve the segmentation of tumor region where hypoxia and glucidic consumption are present at the same time. Introduction of copulas for modeling the dependency between two tracers can simultaneously take into account information from both tracers and deal with two pathological phenomena. Future work will be to consider other families of copula such as spherical and archimedian copulas, and to eliminate partial volume effect by considering dependency between neighboring voxels.« less

Voxel classification based airway tree segmentation

NASA Astrophysics Data System (ADS)

Lo, Pechin; de Bruijne, Marleen

2008-03-01

This paper presents a voxel classification based method for segmenting the human airway tree in volumetric computed tomography (CT) images. In contrast to standard methods that use only voxel intensities, our method uses a more complex appearance model based on a set of local image appearance features and Kth nearest neighbor (KNN) classification. The optimal set of features for classification is selected automatically from a large set of features describing the local image structure at several scales. The use of multiple features enables the appearance model to differentiate between airway tree voxels and other voxels of similar intensities in the lung, thus making the segmentation robust to pathologies such as emphysema. The classifier is trained on imperfect segmentations that can easily be obtained using region growing with a manual threshold selection. Experiments show that the proposed method results in a more robust segmentation that can grow into the smaller airway branches without leaking into emphysematous areas, and is able to segment many branches that are not present in the training set.

On Voxel based Iso-Tumor Control Probabilty and Iso-Complication Maps for Selective Boosting and Selective Avoidance Intensity Modulated Radiotherapy

PubMed Central

Kim, Yusung; Tomé, Wolfgang A.

2010-01-01

Summary Voxel based iso-Tumor Control Probability (TCP) maps and iso-Complication maps are proposed as a plan-review tool especially for functional image-guided intensity-modulated radiotherapy (IMRT) strategies such as selective boosting (dose painting) and conformal avoidance IMRT. The maps employ voxel-based phenomenological biological dose-response models for target volumes and normal organs. Two IMRT strategies for prostate cancer, namely conventional uniform IMRT delivering an EUD = 84 Gy (equivalent uniform dose) to the entire PTV and selective boosting delivering an EUD = 82 Gy to the entire PTV, are investigated, to illustrate the advantages of this approach over iso-dose maps. Conventional uniform IMRT did yield a more uniform isodose map to the entire PTV while selective boosting did result in a nonuniform isodose map. However, when employing voxel based iso-TCP maps selective boosting exhibited a more uniform tumor control probability map compared to what could be achieved using conventional uniform IMRT, which showed TCP cold spots in high-risk tumor subvolumes despite delivering a higher EUD to the entire PTV. Voxel based iso-Complication maps are presented for rectum and bladder, and their utilization for selective avoidance IMRT strategies are discussed. We believe as the need for functional image guided treatment planning grows, voxel based iso-TCP and iso-Complication maps will become an important tool to assess the integrity of such treatment plans. PMID:21151734

Voxel-Based Approach for Estimating Urban Tree Volume from Terrestrial Laser Scanning Data

NASA Astrophysics Data System (ADS)

Vonderach, C.; Voegtle, T.; Adler, P.

2012-07-01

The importance of single trees and the determination of related parameters has been recognized in recent years, e.g. for forest inventories or management. For urban areas an increasing interest in the data acquisition of trees can be observed concerning aspects like urban climate, CO2 balance, and environmental protection. Urban trees differ significantly from natural systems with regard to the site conditions (e.g. technogenic soils, contaminants, lower groundwater level, regular disturbance), climate (increased temperature, reduced humidity) and species composition and arrangement (habitus and health status) and therefore allometric relations cannot be transferred from natural sites to urban areas. To overcome this problem an extended approach was developed for a fast and non-destructive extraction of branch volume, DBH (diameter at breast height) and height of single trees from point clouds of terrestrial laser scanning (TLS). For data acquisition, the trees were scanned with highest scan resolution from several (up to five) positions located around the tree. The resulting point clouds (20 to 60 million points) are analysed with an algorithm based on voxel (volume elements) structure, leading to an appropriate data reduction. In a first step, two kinds of noise reduction are carried out: the elimination of isolated voxels as well as voxels with marginal point density. To obtain correct volume estimates, the voxels inside the stem and branches (interior voxels) where voxels contain no laser points must be regarded. For this filling process, an easy and robust approach was developed based on a layer-wise (horizontal layers of the voxel structure) intersection of four orthogonal viewing directions. However, this procedure also generates several erroneous "phantom" voxels, which have to be eliminated. For this purpose the previous approach was extended by a special region growing algorithm. In a final step the volume is determined layer-wise based on the extracted branch areas Ai of this horizontal cross-section multiplied by the thickness of the voxel layer. A significant improvement of this method could be obtained by a reasonable determination of the threshold for excluding sparsely filled voxels for noise reduction which can be defined based on the function change of filled voxels. Field measurements were used to validate this method. For a quality assessment nine deciduous trees were selected for control and were scanned before felling and weighing. The results are in good accordance to the control trees within a range of only -5.1% to +14.3%. The determined DBH values show only minor deviations, while the heights of trees are systematically underestimated, mainly due to field measurements. Possible error sources including gaps in surface voxels, influence of thin twigs and others are discussed in detail and several improvements of this approach are suggested. The advantages of the algorithm are the robustness and simple structure as well as the quality of the results obtained. The drawbacks are the high effort both in data acquisition and analysis, even if a remarkable data reduction can be obtained by the voxel structure.

Voxel-based statistical analysis of cerebral glucose metabolism in patients with permanent vegetative state after acquired brain injury.

PubMed

Kim, Yong Wook; Kim, Hyoung Seop; An, Young-Sil; Im, Sang Hee

2010-10-01

Permanent vegetative state is defined as the impaired level of consciousness longer than 12 months after traumatic causes and 3 months after non-traumatic causes of brain injury. Although many studies assessed the cerebral metabolism in patients with acute and persistent vegetative state after brain injury, few studies investigated the cerebral metabolism in patients with permanent vegetative state. In this study, we performed the voxel-based analysis of cerebral glucose metabolism and investigated the relationship between regional cerebral glucose metabolism and the severity of impaired consciousness in patients with permanent vegetative state after acquired brain injury. We compared the regional cerebral glucose metabolism as demonstrated by F-18 fluorodeoxyglucose positron emission tomography from 12 patients with permanent vegetative state after acquired brain injury with those from 12 control subjects. Additionally, covariance analysis was performed to identify regions where decreased changes in regional cerebral glucose metabolism significantly correlated with a decrease of level of consciousness measured by JFK-coma recovery scale. Statistical analysis was performed using statistical parametric mapping. Compared with controls, patients with permanent vegetative state demonstrated decreased cerebral glucose metabolism in the left precuneus, both posterior cingulate cortices, the left superior parietal lobule (P(corrected) < 0.001), and increased cerebral glucose metabolism in the both cerebellum and the right supramarginal cortices (P(corrected) < 0.001). In the covariance analysis, a decrease in the level of consciousness was significantly correlated with decreased cerebral glucose metabolism in the both posterior cingulate cortices (P(uncorrected) < 0.005). Our findings suggest that the posteromedial parietal cortex, which are part of neural network for consciousness, may be relevant structure for pathophysiological mechanism in patients with permanent vegetative state after acquired brain injury.

Right Brodmann area 18 predicts tremor arrest after Vim radiosurgery: a voxel-based morphometry study.

PubMed

Tuleasca, Constantin; Witjas, Tatiana; Van de Ville, Dimitri; Najdenovska, Elena; Verger, Antoine; Girard, Nadine; Champoudry, Jerome; Thiran, Jean-Philippe; Cuadra, Meritxell Bach; Levivier, Marc; Guedj, Eric; Régis, Jean

2018-03-01

Drug-resistant essential tremor (ET) can benefit from open standard stereotactic procedures, such as deep-brain stimulation or radiofrequency thalamotomy. Non-surgical candidates can be offered either high-focused ultrasound (HIFU) or radiosurgery (RS). All procedures aim to target the same thalamic site, the ventro-intermediate nucleus (e.g., Vim). The mechanisms by which tremor stops after Vim RS or HIFU remain unknown. We used voxel-based morphometry (VBM) on pretherapeutic neuroimaging data and assessed which anatomical site would best correlate with tremor arrest 1 year after Vim RS. Fifty-two patients (30 male, 22 female; mean age 71.6 years, range 49-82) with right-sided ET benefited from left unilateral Vim RS in Marseille, France. Targeting was performed in a uniform manner, using 130 Gy and a single 4-mm collimator. Neurological (pretherapeutic and 1 year after) and neuroimaging (baseline) assessments were completed. Tremor score on the treated hand (TSTH) at 1 year after Vim RS was included in a statistical parametric mapping analysis of variance (ANOVA) model as a continuous variable with pretherapeutic neuroimaging data. Pretherapeutic gray matter density (GMD) was further correlated with TSTH improvement. No a priori hypothesis was used in the statistical model. The only statistically significant region was right Brodmann area (BA) 18 (visual association area V2, p = 0.05, cluster size K c  = 71). Higher baseline GMD correlated with better TSTH improvement at 1 year after Vim RS (Spearman's rank correlation coefficient = 0.002). Routine baseline structural neuroimaging predicts TSTH improvement 1 year after Vim RS. The relevant anatomical area is the right visual association cortex (BA 18, V2). The question whether visual areas should be included in the targeting remains open.

Prospective multi-centre Voxel Based Morphometry study employing scanner specific segmentations: Procedure development using CaliBrain structural MRI data

PubMed Central

2009-01-01

Background Structural Magnetic Resonance Imaging (sMRI) of the brain is employed in the assessment of a wide range of neuropsychiatric disorders. In order to improve statistical power in such studies it is desirable to pool scanning resources from multiple centres. The CaliBrain project was designed to provide for an assessment of scanner differences at three centres in Scotland, and to assess the practicality of pooling scans from multiple-centres. Methods We scanned healthy subjects twice on each of the 3 scanners in the CaliBrain project with T1-weighted sequences. The tissue classifier supplied within the Statistical Parametric Mapping (SPM5) application was used to map the grey and white tissue for each scan. We were thus able to assess within scanner variability and between scanner differences. We have sought to correct for between scanner differences by adjusting the probability mappings of tissue occupancy (tissue priors) used in SPM5 for tissue classification. The adjustment procedure resulted in separate sets of tissue priors being developed for each scanner and we refer to these as scanner specific priors. Results Voxel Based Morphometry (VBM) analyses and metric tests indicated that the use of scanner specific priors reduced tissue classification differences between scanners. However, the metric results also demonstrated that the between scanner differences were not reduced to the level of within scanner variability, the ideal for scanner harmonisation. Conclusion Our results indicate the development of scanner specific priors for SPM can assist in pooling of scan resources from different research centres. This can facilitate improvements in the statistical power of quantitative brain imaging studies. PMID:19445668

A universal algorithm for an improved finite element mesh generation Mesh quality assessment in comparison to former automated mesh-generators and an analytic model.

PubMed

Kaminsky, Jan; Rodt, Thomas; Gharabaghi, Alireza; Forster, Jan; Brand, Gerd; Samii, Madjid

2005-06-01

The FE-modeling of complex anatomical structures is not solved satisfyingly so far. Voxel-based as opposed to contour-based algorithms allow an automated mesh generation based on the image data. Nonetheless their geometric precision is limited. We developed an automated mesh-generator that combines the advantages of voxel-based generation with improved representation of the geometry by displacement of nodes on the object-surface. Models of an artificial 3D-pipe-section and a skullbase were generated with different mesh-densities using the newly developed geometric, unsmoothed and smoothed voxel generators. Compared to the analytic calculation of the 3D-pipe-section model the normalized RMS error of the surface stress was 0.173-0.647 for the unsmoothed voxel models, 0.111-0.616 for the smoothed voxel models with small volume error and 0.126-0.273 for the geometric models. The highest element-energy error as a criterion for the mesh quality was 2.61x10(-2) N mm, 2.46x10(-2) N mm and 1.81x10(-2) N mm for unsmoothed, smoothed and geometric voxel models, respectively. The geometric model of the 3D-skullbase resulted in the lowest element-energy error and volume error. This algorithm also allowed the best representation of anatomical details. The presented geometric mesh-generator is universally applicable and allows an automated and accurate modeling by combining the advantages of the voxel-technique and of improved surface-modeling.

Use of Brain MRI Atlases to Determine Boundaries of Age-Related Pathology: The Importance of Statistical Method

PubMed Central

Dickie, David Alexander; Job, Dominic E.; Gonzalez, David Rodriguez; Shenkin, Susan D.; Wardlaw, Joanna M.

2015-01-01

Introduction Neurodegenerative disease diagnoses may be supported by the comparison of an individual patient’s brain magnetic resonance image (MRI) with a voxel-based atlas of normal brain MRI. Most current brain MRI atlases are of young to middle-aged adults and parametric, e.g., mean ±standard deviation (SD); these atlases require data to be Gaussian. Brain MRI data, e.g., grey matter (GM) proportion images, from normal older subjects are apparently not Gaussian. We created a nonparametric and a parametric atlas of the normal limits of GM proportions in older subjects and compared their classifications of GM proportions in Alzheimer’s disease (AD) patients. Methods Using publicly available brain MRI from 138 normal subjects and 138 subjects diagnosed with AD (all 55–90 years), we created: a mean ±SD atlas to estimate parametrically the percentile ranks and limits of normal ageing GM; and, separately, a nonparametric, rank order-based GM atlas from the same normal ageing subjects. GM images from AD patients were then classified with respect to each atlas to determine the effect statistical distributions had on classifications of proportions of GM in AD patients. Results The parametric atlas often defined the lower normal limit of the proportion of GM to be negative (which does not make sense physiologically as the lowest possible proportion is zero). Because of this, for approximately half of the AD subjects, 25–45% of voxels were classified as normal when compared to the parametric atlas; but were classified as abnormal when compared to the nonparametric atlas. These voxels were mainly concentrated in the frontal and occipital lobes. Discussion To our knowledge, we have presented the first nonparametric brain MRI atlas. In conditions where there is increasing variability in brain structure, such as in old age, nonparametric brain MRI atlases may represent the limits of normal brain structure more accurately than parametric approaches. Therefore, we conclude that the statistical method used for construction of brain MRI atlases should be selected taking into account the population and aim under study. Parametric methods are generally robust for defining central tendencies, e.g., means, of brain structure. Nonparametric methods are advisable when studying the limits of brain structure in ageing and neurodegenerative disease. PMID:26023913

Diagnostic implications of a small-voxel reconstruction for loco-regional lymph node characterization in breast cancer patients using FDG-PET/CT.

PubMed

Koopman, Daniëlle; van Dalen, Jorn A; Arkies, Hester; Oostdijk, Ad H J; Francken, Anne Brecht; Bart, Jos; Slump, Cornelis H; Knollema, Siert; Jager, Pieter L

2018-01-16

We evaluated the diagnostic implications of a small-voxel reconstruction for lymph node characterization in breast cancer patients, using state-of-the-art FDG-PET/CT. We included 69 FDG-PET/CT scans from breast cancer patients. PET data were reconstructed using standard 4 × 4 × 4 mm 3 and small 2 × 2 × 2 mm 3 voxels. Two hundred thirty loco-regional lymph nodes were included, of which 209 nodes were visualised on PET/CT. All nodes were visually scored as benign or malignant, and SUV max and TB ratio (=SUV max /SUV background ) were measured. Final diagnosis was based on histological or imaging information. We determined the accuracy, sensitivity and specificity for both reconstruction methods and calculated optimal cut-off values to distinguish benign from malignant nodes. Sixty-one benign and 169 malignant lymph nodes were included. Visual evaluation accuracy was 73% (sensitivity 67%, specificity 89%) on standard-voxel images and 77% (sensitivity 78%, specificity 74%) on small-voxel images (p = 0.13). Across malignant nodes visualised on PET/CT, the small-voxel score was more often correct compared with the standard-voxel score (89 vs. 76%, p <  0.001). In benign nodes, the standard-voxel score was more often correct (89 vs. 74%, p = 0.04). Quantitative data were based on the 61 benign and 148 malignant lymph nodes visualised on PET/CT. SUVs and TB ratio were on average 3.0 and 1.6 times higher in malignant nodes compared to those in benign nodes (p <  0.001), on standard- and small-voxel PET images respectively. Small-voxel PET showed average increases in SUV max and TB ratio of typically 40% over standard-voxel PET. The optimal SUV max cut-off using standard-voxels was 1.8 (sensitivity 81%, specificity 95%, accuracy 85%) while for small-voxels, the optimal SUV max cut-off was 2.6 (sensitivity 78%, specificity 98%, accuracy 84%). Differences in accuracy were non-significant. Small-voxel PET/CT improves the sensitivity of visual lymph node characterization and provides a higher detection rate of malignant lymph nodes. However, small-voxel PET/CT also introduced more false-positive results in benign nodes. Across all nodes, differences in accuracy were non-significant. Quantitatively, small-voxel images require higher cut-off values. Readers have to adapt their reference standards.

Postprocessing of Voxel-Based Topologies for Additive Manufacturing Using the Computational Geometry Algorithms Library (CGAL)

DTIC Science & Technology

2015-06-01

10-2014 to 00-11-2014 4. TITLE AND SUBTITLE Postprocessing of Voxel-Based Topologies for Additive Manufacturing Using the Computational Geometry...ABSTRACT Postprocessing of 3-dimensional (3-D) topologies that are defined as a set of voxels using the Computational Geometry Algorithms Library (CGAL... computational geometry algorithms, several of which are suited to the task. The work flow described in this report involves first defining a set of

Using support vector machines with tract-based spatial statistics for automated classification of Tourette syndrome children

NASA Astrophysics Data System (ADS)

Wen, Hongwei; Liu, Yue; Wang, Jieqiong; Zhang, Jishui; Peng, Yun; He, Huiguang

2016-03-01

Tourette syndrome (TS) is a developmental neuropsychiatric disorder with the cardinal symptoms of motor and vocal tics which emerges in early childhood and fluctuates in severity in later years. To date, the neural basis of TS is not fully understood yet and TS has a long-term prognosis that is difficult to accurately estimate. Few studies have looked at the potential of using diffusion tensor imaging (DTI) in conjunction with machine learning algorithms in order to automate the classification of healthy children and TS children. Here we apply Tract-Based Spatial Statistics (TBSS) method to 44 TS children and 48 age and gender matched healthy children in order to extract the diffusion values from each voxel in the white matter (WM) skeleton, and a feature selection algorithm (ReliefF) was used to select the most salient voxels for subsequent classification with support vector machine (SVM). We use a nested cross validation to yield an unbiased assessment of the classification method and prevent overestimation. The accuracy (88.04%), sensitivity (88.64%) and specificity (87.50%) were achieved in our method as peak performance of the SVM classifier was achieved using the axial diffusion (AD) metric, demonstrating the potential of a joint TBSS and SVM pipeline for fast, objective classification of healthy and TS children. These results support that our methods may be useful for the early identification of subjects with TS, and hold promise for predicting prognosis and treatment outcome for individuals with TS.

Assessing the clinical outcome of Vim radiosurgery with voxel-based morphometry: visual areas are linked with tremor arrest!

PubMed

Tuleasca, Constantin; Witjas, Tatiana; Najdenovska, Elena; Verger, Antoine; Girard, Nadine; Champoudry, Jerome; Thiran, Jean-Philippe; Van de Ville, Dimitri; Cuadra, Meritxell Bach; Levivier, Marc; Guedj, Eric; Régis, Jean

2017-11-01

Radiosurgery (RS) is an alternative to open standard stereotactic procedures (deep-brain stimulation or radiofrequency thalamotomy) for drug-resistant essential tremor (ET), aiming at the same target (ventro-intermediate nucleus, Vim). We investigated the Vim RS outcome using voxel-based morphometry by evaluating the interaction between clinical response and time. Thirty-eight patients with right-sided ET benefited from left unilateral Vim RS. Targeting was performed using 130 Gy and a single 4-mm collimator. Neurological and neuroimaging assessment was completed at baseline and 1 year. Clinical responders were considered those with at least 50% improvement in tremor score on the treated hand (TSTH). Interaction between clinical response and time showed the left temporal pole and occipital cortex (Brodmann area 19, including V4, V5 and the parahippocampal place area) as statistically significant. A decrease in gray matter density (GMD) 1 year after Vim RS correlated with higher TSTH improvement (Spearman = 0.01) for both anatomical areas. Higher baseline GMD within the left temporal pole correlated with better TSTH improvement (Spearman = 0.004). Statistically significant structural changes in the relationship to clinical response after Vim RS are present in remote areas, advocating a distant neurobiological effect. The former regions are mainly involved in locomotor monitoring toward the local and distant environment, suggesting the recruiting requirement in targeting of the specific visuomotor networks.

Limbic and corpus callosum aberrations in adolescents with bipolar disorder: a tract-based spatial statistics analysis.

PubMed

Barnea-Goraly, Naama; Chang, Kiki D; Karchemskiy, Asya; Howe, Meghan E; Reiss, Allan L

2009-08-01

Bipolar disorder (BD) is a common and debilitating condition, often beginning in adolescence. Converging evidence from genetic and neuroimaging studies indicates that white matter abnormalities may be involved in BD. In this study, we investigated white matter structure in adolescents with familial bipolar disorder using diffusion tensor imaging (DTI) and a whole brain analysis. We analyzed DTI images using tract-based spatial statistics (TBSS), a whole-brain voxel-by-voxel analysis, to investigate white matter structure in 21 adolescents with BD, who also were offspring of at least one parent with BD, and 18 age- and IQ-matched control subjects. Fractional anisotropy (FA; a measure of diffusion anisotropy), trace values (average diffusivity), and apparent diffusion coefficient (ADC; a measure of overall diffusivity) were used as variables in this analysis. In a post hoc analysis, we correlated between FA values, behavioral measures, and medication exposure. Adolescents with BD had lower FA values than control subjects in the fornix, the left mid-posterior cingulate gyrus, throughout the corpus callosum, in fibers extending from the fornix to the thalamus, and in parietal and occipital corona radiata bilaterally. There were no significant between-group differences in trace or ADC values and no significant correlation between behavioral measures, medication exposure, and FA values. Significant white matter tract alterations in adolescents with BD were observed in regions involved in emotional, behavioral, and cognitive regulation. These results suggest that alterations in white matter are present early in the course of disease in familial BD.

Differences in Brain Metabolic Impairment between Chronic Mild/Moderate TBI Patients with and without Visible Brain Lesions Based on MRI.

PubMed

Ito, Keiichi; Asano, Yoshitaka; Ikegame, Yuka; Shinoda, Jun

2016-01-01

Introduction. Many patients with mild/moderate traumatic brain injury (m/mTBI) in the chronic stage suffer from executive brain function impairment. Analyzing brain metabolism is important for elucidating the pathological mechanisms associated with their symptoms. This study aimed to determine the differences in brain glucose metabolism between m/mTBI patients with and without visible traumatic brain lesions based on MRI. Methods. Ninety patients with chronic m/mTBI due to traffic accidents were enrolled and divided into two groups based on their MRI findings. Group A comprised 50 patients with visible lesions. Group B comprised 40 patients without visible lesions. Patients underwent FDG-PET scans following cognitive tests. FDG-PET images were analyzed using voxel-by-voxel univariate statistical tests. Results. There were no significant differences in the cognitive tests between Group A and Group B. Based on FDG-PET findings, brain metabolism significantly decreased in the orbital gyrus, cingulate gyrus, and medial thalamus but increased in the parietal and occipital convexity in Group A compared with that in the control. Compared with the control, patients in Group B exhibited no significant changes. Conclusions. These results suggest that different pathological mechanisms may underlie cognitive impairment in m/mTBI patients with and without organic brain damage.

Absorbed fractions in a voxel-based phantom calculated with the MCNP-4B code.

PubMed

Yoriyaz, H; dos Santos, A; Stabin, M G; Cabezas, R

2000-07-01

A new approach for calculating internal dose estimates was developed through the use of a more realistic computational model of the human body. The present technique shows the capability to build a patient-specific phantom with tomography data (a voxel-based phantom) for the simulation of radiation transport and energy deposition using Monte Carlo methods such as in the MCNP-4B code. MCNP-4B absorbed fractions for photons in the mathematical phantom of Snyder et al. agreed well with reference values. Results obtained through radiation transport simulation in the voxel-based phantom, in general, agreed well with reference values. Considerable discrepancies, however, were found in some cases due to two major causes: differences in the organ masses between the phantoms and the occurrence of organ overlap in the voxel-based phantom, which is not considered in the mathematical phantom.

Mapping of ApoE4 related white matter damage using diffusion MRI

NASA Astrophysics Data System (ADS)

Tsao, Sinchai; Gajawelli, Niharika; Hwang, Darryl H.; Kriger, Stephen; Law, Meng; Chui, Helena; Weiner, Michael; Lepore, Natasha

2014-04-01

ApoliopoproteinE Ɛ4 (ApoE-Ɛ4) polymorphism is the most well known genetic risk factor for developing Alzheimers Disease. The exact mechanism through which ApoE 4 increases AD risk is not fully known, but may be related to decreased clearance and increased oligomerization of Aβ. By making measurements of white matter integrity via diffusion MR and correlating the metrics in a voxel-based statistical analysis with ApoE-Ɛ4 genotype (whilst controlling for vascular risk factor, gender, cognitive status and age) we are able to identify changes in white matter associated with carrying an ApoE Ɛ4 allele. We found potentially significant regions (Puncorrected < 0:05) near the hippocampus and the posterior cingulum that were independent of voxels that correlated with age or clinical dementia rating (CDR) status suggesting that ApoE may affect cognitive decline via a pathway in dependent of normal aging and acute insults that can be measured by CDR and Framingham Coronary Risk Score (FCRS).

Validation of a personalized dosimetric evaluation tool (Oedipe) for targeted radiotherapy based on the Monte Carlo MCNPX code

NASA Astrophysics Data System (ADS)

Chiavassa, S.; Aubineau-Lanièce, I.; Bitar, A.; Lisbona, A.; Barbet, J.; Franck, D.; Jourdain, J. R.; Bardiès, M.

2006-02-01

Dosimetric studies are necessary for all patients treated with targeted radiotherapy. In order to attain the precision required, we have developed Oedipe, a dosimetric tool based on the MCNPX Monte Carlo code. The anatomy of each patient is considered in the form of a voxel-based geometry created using computed tomography (CT) images or magnetic resonance imaging (MRI). Oedipe enables dosimetry studies to be carried out at the voxel scale. Validation of the results obtained by comparison with existing methods is complex because there are multiple sources of variation: calculation methods (different Monte Carlo codes, point kernel), patient representations (model or specific) and geometry definitions (mathematical or voxel-based). In this paper, we validate Oedipe by taking each of these parameters into account independently. Monte Carlo methodology requires long calculation times, particularly in the case of voxel-based geometries, and this is one of the limits of personalized dosimetric methods. However, our results show that the use of voxel-based geometry as opposed to a mathematically defined geometry decreases the calculation time two-fold, due to an optimization of the MCNPX2.5e code. It is therefore possible to envisage the use of Oedipe for personalized dosimetry in the clinical context of targeted radiotherapy.

Genetic and environmental influences on structural variability of the brain in pediatric twin: deformation based morphometry.

PubMed

Yoon, Uicheul; Perusse, Daniel; Lee, Jong-Min; Evans, Alan C

2011-04-08

Twin studies are one of the most powerful study designs for estimating the relative contribution of genetic and environmental influences on phenotypic variation inhuman brain morphology. In this study, we applied deformation based morphometry, a technique that provides a voxel-wise index of local tissue growth or atrophy relative to a template brain, combined with univariate ACE model, to investigate the genetic and environmental effects on the human brain structural variations in a cohort of homogeneously aged healthy pediatric twins. In addition, anatomical regions of interest (ROIs) were defined in order to explore global and regional genetic effects. ROI results showed that the influence of genetic factors on cerebrum (h(2)=0.70), total gray matter (0.67), and total white matter (0.73) volumes were significant. In particular, structural variability of left-side lobar volumes showed a significant heritability. Several subcortical structures such as putamen (h(ROI)(2)=0.79/0.77(L/R),h(MAX)(2)=0.82/0.79) and globus pallidus (0.81/0.76, 0.88/0.82) were also significantly heritable in both voxel-wise and ROI-based results. In the voxel-wise results, lateral parts of right cerebellum (c(2)=0.68) and the posterior portion of the corpus callosum (0.63) were rather environmentally determined, but it failed to reach statistical significance. Pediatric twin studies are important because they can discriminate several influences on developmental brain trajectories and identify relationships between gene and behavior. Several brain structures showed significant genetic effects and might therefore serve as biological markers for inherited traits, or as targets for genetic linkage and association studies. Copyright © 2011 Elsevier Ireland Ltd. All rights reserved.

Quantification of micro-CT images of textile reinforcements

NASA Astrophysics Data System (ADS)

Straumit, Ilya; Lomov, Stepan V.; Wevers, Martine

2017-10-01

VoxTex software (KU Leuven) employs 3D image processing, which use the local directionality information, retrieved using analysis of local structure tensor. The processing results in a voxel 3D array, with each voxel carrying information on (1) material type (matrix; yarn/ply, with identification of the yarn/ply in the reinforcement architecture; void) and (2) fibre direction for fibrous yarns/plies. The knowledge of the material phase volume and known characterisation of the textile structure allows assigning to the voxels (3) fibre volume fraction. This basic voxel model can be further used for different type of the material analysis: Internal geometry and characterisation of defects; permeability; micromechanics; mesoFE voxel models. Apart from the voxel based analysis, approaches to reconstruction of the yarn paths are presented.

TH-A-9A-01: Active Optical Flow Model: Predicting Voxel-Level Dose Prediction in Spine SBRT

DOE Office of Scientific and Technical Information (OSTI.GOV)

Liu, J; Wu, Q.J.; Yin, F

2014-06-15

Purpose: To predict voxel-level dose distribution and enable effective evaluation of cord dose sparing in spine SBRT. Methods: We present an active optical flow model (AOFM) to statistically describe cord dose variations and train a predictive model to represent correlations between AOFM and PTV contours. Thirty clinically accepted spine SBRT plans are evenly divided into training and testing datasets. The development of predictive model consists of 1) collecting a sequence of dose maps including PTV and OAR (spinal cord) as well as a set of associated PTV contours adjacent to OAR from the training dataset, 2) classifying data into fivemore » groups based on PTV's locations relative to OAR, two “Top”s, “Left”, “Right”, and “Bottom”, 3) randomly selecting a dose map as the reference in each group and applying rigid registration and optical flow deformation to match all other maps to the reference, 4) building AOFM by importing optical flow vectors and dose values into the principal component analysis (PCA), 5) applying another PCA to features of PTV and OAR contours to generate an active shape model (ASM), and 6) computing a linear regression model of correlations between AOFM and ASM.When predicting dose distribution of a new case in the testing dataset, the PTV is first assigned to a group based on its contour characteristics. Contour features are then transformed into ASM's principal coordinates of the selected group. Finally, voxel-level dose distribution is determined by mapping from the ASM space to the AOFM space using the predictive model. Results: The DVHs predicted by the AOFM-based model and those in clinical plans are comparable in training and testing datasets. At 2% volume the dose difference between predicted and clinical plans is 4.2±4.4% and 3.3±3.5% in the training and testing datasets, respectively. Conclusion: The AOFM is effective in predicting voxel-level dose distribution for spine SBRT. Partially supported by NIH/NCI under grant #R21CA161389 and a master research grant by Varian Medical System.« less

Automated segmentation of thyroid gland on CT images with multi-atlas label fusion and random classification forest

NASA Astrophysics Data System (ADS)

Liu, Jiamin; Chang, Kevin; Kim, Lauren; Turkbey, Evrim; Lu, Le; Yao, Jianhua; Summers, Ronald

2015-03-01

The thyroid gland plays an important role in clinical practice, especially for radiation therapy treatment planning. For patients with head and neck cancer, radiation therapy requires a precise delineation of the thyroid gland to be spared on the pre-treatment planning CT images to avoid thyroid dysfunction. In the current clinical workflow, the thyroid gland is normally manually delineated by radiologists or radiation oncologists, which is time consuming and error prone. Therefore, a system for automated segmentation of the thyroid is desirable. However, automated segmentation of the thyroid is challenging because the thyroid is inhomogeneous and surrounded by structures that have similar intensities. In this work, the thyroid gland segmentation is initially estimated by multi-atlas label fusion algorithm. The segmentation is refined by supervised statistical learning based voxel labeling with a random forest algorithm. Multiatlas label fusion (MALF) transfers expert-labeled thyroids from atlases to a target image using deformable registration. Errors produced by label transfer are reduced by label fusion that combines the results produced by all atlases into a consensus solution. Then, random forest (RF) employs an ensemble of decision trees that are trained on labeled thyroids to recognize features. The trained forest classifier is then applied to the thyroid estimated from the MALF by voxel scanning to assign the class-conditional probability. Voxels from the expert-labeled thyroids in CT volumes are treated as positive classes; background non-thyroid voxels as negatives. We applied this automated thyroid segmentation system to CT scans of 20 patients. The results showed that the MALF achieved an overall 0.75 Dice Similarity Coefficient (DSC) and the RF classification further improved the DSC to 0.81.

Multi-resolution statistical image reconstruction for mitigation of truncation effects: application to cone-beam CT of the head

NASA Astrophysics Data System (ADS)

Dang, Hao; Webster Stayman, J.; Sisniega, Alejandro; Zbijewski, Wojciech; Xu, Jennifer; Wang, Xiaohui; Foos, David H.; Aygun, Nafi; Koliatsos, Vassilis E.; Siewerdsen, Jeffrey H.

2017-01-01

A prototype cone-beam CT (CBCT) head scanner featuring model-based iterative reconstruction (MBIR) has been recently developed and demonstrated the potential for reliable detection of acute intracranial hemorrhage (ICH), which is vital to diagnosis of traumatic brain injury and hemorrhagic stroke. However, data truncation (e.g. due to the head holder) can result in artifacts that reduce image uniformity and challenge ICH detection. We propose a multi-resolution MBIR method with an extended reconstruction field of view (RFOV) to mitigate truncation effects in CBCT of the head. The image volume includes a fine voxel size in the (inner) nontruncated region and a coarse voxel size in the (outer) truncated region. This multi-resolution scheme allows extension of the RFOV to mitigate truncation effects while introducing minimal increase in computational complexity. The multi-resolution method was incorporated in a penalized weighted least-squares (PWLS) reconstruction framework previously developed for CBCT of the head. Experiments involving an anthropomorphic head phantom with truncation due to a carbon-fiber holder were shown to result in severe artifacts in conventional single-resolution PWLS, whereas extending the RFOV within the multi-resolution framework strongly reduced truncation artifacts. For the same extended RFOV, the multi-resolution approach reduced computation time compared to the single-resolution approach (viz. time reduced by 40.7%, 83.0%, and over 95% for an image volume of 6003, 8003, 10003 voxels). Algorithm parameters (e.g. regularization strength, the ratio of the fine and coarse voxel size, and RFOV size) were investigated to guide reliable parameter selection. The findings provide a promising method for truncation artifact reduction in CBCT and may be useful for other MBIR methods and applications for which truncation is a challenge.

Voxel-Based LIDAR Analysis and Applications

NASA Astrophysics Data System (ADS)

Hagstrom, Shea T.

One of the greatest recent changes in the field of remote sensing is the addition of high-quality Light Detection and Ranging (LIDAR) instruments. In particular, the past few decades have been greatly beneficial to these systems because of increases in data collection speed and accuracy, as well as a reduction in the costs of components. These improvements allow modern airborne instruments to resolve sub-meter details, making them ideal for a wide variety of applications. Because LIDAR uses active illumination to capture 3D information, its output is fundamentally different from other modalities. Despite this difference, LIDAR datasets are often processed using methods appropriate for 2D images and that do not take advantage of its primary virtue of 3-dimensional data. It is this problem we explore by using volumetric voxel modeling. Voxel-based analysis has been used in many applications, especially medical imaging, but rarely in traditional remote sensing. In part this is because the memory requirements are substantial when handling large areas, but with modern computing and storage this is no longer a significant impediment. Our reason for using voxels to model scenes from LIDAR data is that there are several advantages over standard triangle-based models, including better handling of overlapping surfaces and complex shapes. We show how incorporating system position information from early in the LIDAR point cloud generation process allows radiometrically-correct transmission and other novel voxel properties to be recovered. This voxelization technique is validated on simulated data using the Digital Imaging and Remote Sensing Image Generation (DIRSIG) software, a first-principles based ray-tracer developed at the Rochester Institute of Technology. Voxel-based modeling of LIDAR can be useful on its own, but we believe its primary advantage is when applied to problems where simpler surface-based 3D models conflict with the requirement of realistic geometry. To show the voxel model's advantage, we apply it to several outstanding problems in remote sensing: LIDAR quality metrics, line-of-sight mapping, and multi-model fusion. Each of these applications is derived, validated, and examined in detail, and our results compared with other state-of-the-art methods. In most cases the voxel-based methods demonstrate superior results and are able to derive information not available to existing methods. Realizing these improvements requires only a shift away from traditional 3D model generation, and our results give a small indicator of what is possible. Many examples of possible areas for future improvement and expansion of algorithms beyond the scope of our work are also noted.

Voxel-wise mapping of cervical cord damage in multiple sclerosis patients with different clinical phenotypes.

PubMed

Rocca, Maria A; Valsasina, Paola; Damjanovic, Dusan; Horsfield, Mark A; Mesaros, Sarlota; Stosic-Opincal, Tatjana; Drulovic, Jelena; Filippi, Massimo

2013-01-01

To apply voxel-based methods to map the regional distribution of atrophy and T2 hyperintense lesions in the cervical cord of multiple sclerosis (MS) patients with different clinical phenotypes. Brain and cervical cord 3D T1-weighted and T2-weighted scans were acquired from 31 healthy controls (HC) and 77 MS patients (15 clinically isolated syndromes (CIS), 15 relapsing-remitting (RR), 19 benign (B), 15 primary progressive (PP) and 13 secondary progressive (SP) MS). Hyperintense cord lesions were outlined on T2-weighted scans. The T2- and 3D T1-weighted cord images were then analysed using an active surface method which created output images reformatted in planes perpendicular to the estimated cord centre line. These unfolded cervical cord images were co-registered into a common space; then smoothed binary cord masks and lesion masks underwent spatial statistic analysis (SPM8). No cord atrophy was found in CIS patients versus HC, while PPMS had significant cord atrophy. Clusters of cord atrophy were found in BMS versus RRMS, and in SPMS versus RRMS, BMS and PPMS patients, mainly involving the posterior and lateral cord segments. Cord lesion probability maps showed a significantly greater likelihood of abnormalities in RRMS, PPMS and SPMS than in CIS and BMS patients. The spatial distributions of cord atrophy and cord lesions were not correlated. In progressive MS, regional cord atrophy was correlated with clinical disability and impairment in the pyramidal system. Voxel-based assessment of cervical cord damage is feasible and may contribute to a better characterisation of the clinical heterogeneity of MS patients.

Towards real-time diffuse optical tomography for imaging brain functions cooperated with Kalman estimator

NASA Astrophysics Data System (ADS)

Wang, Bingyuan; Zhang, Yao; Liu, Dongyuan; Ding, Xuemei; Dan, Mai; Pan, Tiantian; Wang, Yihan; Li, Jiao; Zhou, Zhongxing; Zhang, Limin; Zhao, Huijuan; Gao, Feng

2018-02-01

Functional near-infrared spectroscopy (fNIRS) is a non-invasive neuroimaging method to monitor the cerebral hemodynamic through the optical changes measured at the scalp surface. It has played a more and more important role in psychology and medical imaging communities. Real-time imaging of brain function using NIRS makes it possible to explore some sophisticated human brain functions unexplored before. Kalman estimator has been frequently used in combination with modified Beer-Lamber Law (MBLL) based optical topology (OT), for real-time brain function imaging. However, the spatial resolution of the OT is low, hampering the application of OT in exploring some complicated brain functions. In this paper, we develop a real-time imaging method combining diffuse optical tomography (DOT) and Kalman estimator, much improving the spatial resolution. Instead of only presenting one spatially distributed image indicating the changes of the absorption coefficients at each time point during the recording process, one real-time updated image using the Kalman estimator is provided. Its each voxel represents the amplitude of the hemodynamic response function (HRF) associated with this voxel. We evaluate this method using some simulation experiments, demonstrating that this method can obtain more reliable spatial resolution images. Furthermore, a statistical analysis is also conducted to help to decide whether a voxel in the field of view is activated or not.

The partial volume effect in the quantification of 1H magnetic resonance spectroscopy in Alzheimer's disease and aging.

PubMed

Mato Abad, Virginia; Quirós, Alicia; García-Álvarez, Roberto; Loureiro, Javier Pereira; Alvarez-Linera, Juan; Frank, Ana; Hernández-Tamames, Juan Antonio

2014-01-01

1H-MRS variability increases due to normal aging and also as a result of atrophy in grey and white matter caused by neurodegeneration. In this work, an automatic process was developed to integrate data from spectra and high-resolution anatomical images to quantify metabolites, taking into account tissue partial volumes within the voxel of interest avoiding additional spectra acquisitions required for partial volume correction. To evaluate this method, we use a cohort of 135 subjects (47 male and 88 female, aged between 57 and 99 years) classified into 4 groups: 38 healthy participants, 20 amnesic mild cognitive impairment patients, 22 multi-domain mild cognitive impairment patients, and 55 Alzheimer's disease patients. Our findings suggest that knowing the voxel composition of white and grey matter and cerebrospinal fluid is necessary to avoid partial volume variations in a single-voxel study and to decrease part of the variability found in metabolites quantification, particularly in those studies involving elder patients and neurodegenerative diseases. The proposed method facilitates the use of 1H-MRS techniques in statistical studies in Alzheimer's disease, because it provides more accurate quantitative measurements, reduces the inter-subject variability, and improves statistical results when performing group comparisons.

Statistical Segmentation of Surgical Instruments in 3D Ultrasound Images

PubMed Central

Linguraru, Marius George; Vasilyev, Nikolay V.; Del Nido, Pedro J.; Howe, Robert D.

2008-01-01

The recent development of real-time 3D ultrasound enables intracardiac beating heart procedures, but the distorted appearance of surgical instruments is a major challenge to surgeons. In addition, tissue and instruments have similar gray levels in US images and the interface between instruments and tissue is poorly defined. We present an algorithm that automatically estimates instrument location in intracardiac procedures. Expert-segmented images are used to initialize the statistical distributions of blood, tissue and instruments. Voxels are labeled through an iterative expectation-maximization algorithm using information from the neighboring voxels through a smoothing kernel. Once the three classes of voxels are separated, additional neighboring information is combined with the known shape characteristics of instruments in order to correct for misclassifications. We analyze the major axis of segmented data through their principal components and refine the results by a watershed transform, which corrects the results at the contact between instrument and tissue. We present results on 3D in-vitro data from a tank trial, and 3D in-vivo data from cardiac interventions on porcine beating hearts, using instruments of four types of materials. The comparison of algorithm results to expert-annotated images shows the correct segmentation and position of the instrument shaft. PMID:17521802

Modeling Dynamic Contrast-Enhanced MRI Data with a Constrained Local AIF.

PubMed

Duan, Chong; Kallehauge, Jesper F; Pérez-Torres, Carlos J; Bretthorst, G Larry; Beeman, Scott C; Tanderup, Kari; Ackerman, Joseph J H; Garbow, Joel R

2018-02-01

This study aims to develop a constrained local arterial input function (cL-AIF) to improve quantitative analysis of dynamic contrast-enhanced (DCE)-magnetic resonance imaging (MRI) data by accounting for the contrast-agent bolus amplitude error in the voxel-specific AIF. Bayesian probability theory-based parameter estimation and model selection were used to compare tracer kinetic modeling employing either the measured remote-AIF (R-AIF, i.e., the traditional approach) or an inferred cL-AIF against both in silico DCE-MRI data and clinical, cervical cancer DCE-MRI data. When the data model included the cL-AIF, tracer kinetic parameters were correctly estimated from in silico data under contrast-to-noise conditions typical of clinical DCE-MRI experiments. Considering the clinical cervical cancer data, Bayesian model selection was performed for all tumor voxels of the 16 patients (35,602 voxels in total). Among those voxels, a tracer kinetic model that employed the voxel-specific cL-AIF was preferred (i.e., had a higher posterior probability) in 80 % of the voxels compared to the direct use of a single R-AIF. Maps of spatial variation in voxel-specific AIF bolus amplitude and arrival time for heterogeneous tissues, such as cervical cancer, are accessible with the cL-AIF approach. The cL-AIF method, which estimates unique local-AIF amplitude and arrival time for each voxel within the tissue of interest, provides better modeling of DCE-MRI data than the use of a single, measured R-AIF. The Bayesian-based data analysis described herein affords estimates of uncertainties for each model parameter, via posterior probability density functions, and voxel-wise comparison across methods/models, via model selection in data modeling.

The effect of in situ/in vitro three-dimensional quantitative computed tomography image voxel size on the finite element model of human vertebral cancellous bone.

PubMed

Lu, Yongtao; Engelke, Klaus; Glueer, Claus-C; Morlock, Michael M; Huber, Gerd

2014-11-01

Quantitative computed tomography-based finite element modeling technique is a promising clinical tool for the prediction of bone strength. However, quantitative computed tomography-based finite element models were created from image datasets with different image voxel sizes. The aim of this study was to investigate whether there is an influence of image voxel size on the finite element models. In all 12 thoracolumbar vertebrae were scanned prior to autopsy (in situ) using two different quantitative computed tomography scan protocols, which resulted in image datasets with two different voxel sizes (0.29 × 0.29 × 1.3 mm(3) vs 0.18 × 0.18 × 0.6 mm(3)). Eight of them were scanned after autopsy (in vitro) and the datasets were reconstructed with two voxel sizes (0.32 × 0.32 × 0.6 mm(3) vs. 0.18 × 0.18 × 0.3 mm(3)). Finite element models with cuboid volume of interest extracted from the vertebral cancellous part were created and inhomogeneous bilinear bone properties were defined. Axial compression was simulated. No effect of voxel size was detected on the apparent bone mineral density for both the in situ and in vitro cases. However, the apparent modulus and yield strength showed significant differences in the two voxel size group pairs (in situ and in vitro). In conclusion, the image voxel size may have to be considered when the finite element voxel modeling technique is used in clinical applications. © IMechE 2014.

Accounting for Non-Gaussian Sources of Spatial Correlation in Parametric Functional Magnetic Resonance Imaging Paradigms II: A Method to Obtain First-Level Analysis Residuals with Uniform and Gaussian Spatial Autocorrelation Function and Independent and Identically Distributed Time-Series.

PubMed

Gopinath, Kaundinya; Krishnamurthy, Venkatagiri; Lacey, Simon; Sathian, K

2018-02-01

In a recent study Eklund et al. have shown that cluster-wise family-wise error (FWE) rate-corrected inferences made in parametric statistical method-based functional magnetic resonance imaging (fMRI) studies over the past couple of decades may have been invalid, particularly for cluster defining thresholds less stringent than p < 0.001; principally because the spatial autocorrelation functions (sACFs) of fMRI data had been modeled incorrectly to follow a Gaussian form, whereas empirical data suggest otherwise. Hence, the residuals from general linear model (GLM)-based fMRI activation estimates in these studies may not have possessed a homogenously Gaussian sACF. Here we propose a method based on the assumption that heterogeneity and non-Gaussianity of the sACF of the first-level GLM analysis residuals, as well as temporal autocorrelations in the first-level voxel residual time-series, are caused by unmodeled MRI signal from neuronal and physiological processes as well as motion and other artifacts, which can be approximated by appropriate decompositions of the first-level residuals with principal component analysis (PCA), and removed. We show that application of this method yields GLM residuals with significantly reduced spatial correlation, nearly Gaussian sACF and uniform spatial smoothness across the brain, thereby allowing valid cluster-based FWE-corrected inferences based on assumption of Gaussian spatial noise. We further show that application of this method renders the voxel time-series of first-level GLM residuals independent, and identically distributed across time (which is a necessary condition for appropriate voxel-level GLM inference), without having to fit ad hoc stochastic colored noise models. Furthermore, the detection power of individual subject brain activation analysis is enhanced. This method will be especially useful for case studies, which rely on first-level GLM analysis inferences.

Voxel-based morphometric analysis in hypothyroidism using diffeomorphic anatomic registration via an exponentiated lie algebra algorithm approach.

PubMed

Singh, S; Modi, S; Bagga, D; Kaur, P; Shankar, L R; Khushu, S

2013-03-01

The present study aimed to investigate whether brain morphological differences exist between adult hypothyroid subjects and age-matched controls using voxel-based morphometry (VBM) with diffeomorphic anatomic registration via an exponentiated lie algebra algorithm (DARTEL) approach. High-resolution structural magnetic resonance images were taken in ten healthy controls and ten hypothyroid subjects. The analysis was conducted using statistical parametric mapping. The VBM study revealed a reduction in grey matter volume in the left postcentral gyrus and cerebellum of hypothyroid subjects compared to controls. A significant reduction in white matter volume was also found in the cerebellum, right inferior and middle frontal gyrus, right precentral gyrus, right inferior occipital gyrus and right temporal gyrus of hypothyroid patients compared to healthy controls. Moreover, no meaningful cluster for greater grey or white matter volume was obtained in hypothyroid subjects compared to controls. Our study is the first VBM study of hypothyroidism in an adult population and suggests that, compared to controls, this disorder is associated with differences in brain morphology in areas corresponding to known functional deficits in attention, language, motor speed, visuospatial processing and memory in hypothyroidism. © 2012 British Society for Neuroendocrinology.

Imaging Effects of Neurotrophic Factor Genes on Brain Plasticity and Repair in Multiple Sclerosis

DTIC Science & Technology

2010-07-01

cortical thickness and subcortical volume measures, lesion volumetry , and voxel-based morphometry and diffusion imaging. We are continuing to...th ickness and subcortical volume measures, lesion volumetry , and voxel-based morphometry and diffusion imaging. Regressio n and symbolic modeling

Hippocampal and cerebellar atrophy in patients with Cushing's disease.

PubMed

Burkhardt, Till; Lüdecke, Daniel; Spies, Lothar; Wittmann, Linus; Westphal, Manfred; Flitsch, Jörg

2015-11-01

OBJECT Cushing's disease (CD) may cause atrophy of different regions of the human brain, mostly affecting the hippocampus and the cerebellum. This study evaluates the use of 3-T MRI of newly diagnosed patients with CD to detect atrophic degeneration with voxel-based volumetry. METHODS Subjects with newly diagnosed, untreated CD were included and underwent 3-T MRI. Images were analyzed using a voxelwise statistical test to detect reduction of brain parenchyma. In addition, an atlas-based volumetric study for regions likely to be affected by CD was performed. RESULTS Nineteen patients with a mean disease duration of 24 months were included. Tumor markers included adrenocorticotropic hormone (median 17.5 pmol/L), cortisol (949.4 nmol/L), and dehydroepiandrosterone sulfate (5.4 μmol/L). The following values are expressed as the mean ± SD. The voxelwise statistical test revealed clusters of significantly reduced gray matter in the hippocampus and cerebellum, with volumes of 2.90 ± 0.26 ml (right hippocampus), 2.89 ± 0.28 ml (left hippocampus), 41.95 ± 4.67 ml (right cerebellar hemisphere), and 42.11 ± 4.59 ml (left cerebellar hemisphere). Healthy control volunteers showed volumes of 3.22 ± 0.25 ml for the right hippocampus, 3.23 ± 0.25 ml for the left hippocampus, 50.87 ± 4.23 ml for the right cerebellar hemisphere, and 50.42 ± 3.97 ml for the left cerebellar hemisphere. CONCLUSIONS Patients with untreated CD show significant reduction of gray matter in the cerebellum and hippocampus. These changes can be analyzed and objectified with the quantitative voxel-based method described in this study.

Task-Related Edge Density (TED)—A New Method for Revealing Dynamic Network Formation in fMRI Data of the Human Brain

PubMed Central

Lohmann, Gabriele; Stelzer, Johannes; Zuber, Verena; Buschmann, Tilo; Margulies, Daniel; Bartels, Andreas; Scheffler, Klaus

2016-01-01

The formation of transient networks in response to external stimuli or as a reflection of internal cognitive processes is a hallmark of human brain function. However, its identification in fMRI data of the human brain is notoriously difficult. Here we propose a new method of fMRI data analysis that tackles this problem by considering large-scale, task-related synchronisation networks. Networks consist of nodes and edges connecting them, where nodes correspond to voxels in fMRI data, and the weight of an edge is determined via task-related changes in dynamic synchronisation between their respective times series. Based on these definitions, we developed a new data analysis algorithm that identifies edges that show differing levels of synchrony between two distinct task conditions and that occur in dense packs with similar characteristics. Hence, we call this approach “Task-related Edge Density” (TED). TED proved to be a very strong marker for dynamic network formation that easily lends itself to statistical analysis using large scale statistical inference. A major advantage of TED compared to other methods is that it does not depend on any specific hemodynamic response model, and it also does not require a presegmentation of the data for dimensionality reduction as it can handle large networks consisting of tens of thousands of voxels. We applied TED to fMRI data of a fingertapping and an emotion processing task provided by the Human Connectome Project. TED revealed network-based involvement of a large number of brain areas that evaded detection using traditional GLM-based analysis. We show that our proposed method provides an entirely new window into the immense complexity of human brain function. PMID:27341204

Task-Related Edge Density (TED)-A New Method for Revealing Dynamic Network Formation in fMRI Data of the Human Brain.

PubMed

Lohmann, Gabriele; Stelzer, Johannes; Zuber, Verena; Buschmann, Tilo; Margulies, Daniel; Bartels, Andreas; Scheffler, Klaus

2016-01-01

The formation of transient networks in response to external stimuli or as a reflection of internal cognitive processes is a hallmark of human brain function. However, its identification in fMRI data of the human brain is notoriously difficult. Here we propose a new method of fMRI data analysis that tackles this problem by considering large-scale, task-related synchronisation networks. Networks consist of nodes and edges connecting them, where nodes correspond to voxels in fMRI data, and the weight of an edge is determined via task-related changes in dynamic synchronisation between their respective times series. Based on these definitions, we developed a new data analysis algorithm that identifies edges that show differing levels of synchrony between two distinct task conditions and that occur in dense packs with similar characteristics. Hence, we call this approach "Task-related Edge Density" (TED). TED proved to be a very strong marker for dynamic network formation that easily lends itself to statistical analysis using large scale statistical inference. A major advantage of TED compared to other methods is that it does not depend on any specific hemodynamic response model, and it also does not require a presegmentation of the data for dimensionality reduction as it can handle large networks consisting of tens of thousands of voxels. We applied TED to fMRI data of a fingertapping and an emotion processing task provided by the Human Connectome Project. TED revealed network-based involvement of a large number of brain areas that evaded detection using traditional GLM-based analysis. We show that our proposed method provides an entirely new window into the immense complexity of human brain function.

Discrimination of dementia with Lewy bodies from Alzheimer's disease using voxel-based morphometry of white matter by statistical parametric mapping 8 plus diffeomorphic anatomic registration through exponentiated Lie algebra.

PubMed

Nakatsuka, Tomoya; Imabayashi, Etsuko; Matsuda, Hiroshi; Sakakibara, Ryuji; Inaoka, Tsutomu; Terada, Hitoshi

2013-05-01

The purpose of this study was to identify brain atrophy specific for dementia with Lewy bodies (DLB) and to evaluate the discriminatory performance of this specific atrophy between DLB and Alzheimer's disease (AD). We retrospectively reviewed 60 DLB and 30 AD patients who had undergone 3D T1-weighted MRI. We randomly divided the DLB patients into two equal groups (A and B). First, we obtained a target volume of interest (VOI) for DLB-specific atrophy using correlation analysis of the percentage rate of significant whole white matter (WM) atrophy calculated using the Voxel-based Specific Regional Analysis System for Alzheimer's Disease (VSRAD) based on statistical parametric mapping 8 (SPM8) plus diffeomorphic anatomic registration through exponentiated Lie algebra, with segmented WM images in group A. We then evaluated the usefulness of this target VOI for discriminating the remaining 30 DLB patients in group B from the 30 AD patients. Z score values in this target VOI obtained from VSRAD were used as the determinant in receiver operating characteristic (ROC) analysis. Specific target VOIs for DLB were determined in the right-side dominant dorsal midbrain, right-side dominant dorsal pons, and bilateral cerebellum. ROC analysis revealed that the target VOI limited to the midbrain exhibited the highest area under the ROC curves of 0.75. DLB patients showed specific atrophy in the midbrain, pons, and cerebellum. Midbrain atrophy demonstrated the highest power for discriminating DLB and AD. This approach may be useful for determining the contributions of DLB and AD pathologies to the dementia syndrome.

How reliable are gray matter disruptions in specific reading disability across multiple countries and languages? Insights from a large-scale voxel-based morphometry study.

PubMed

Jednoróg, Katarzyna; Marchewka, Artur; Altarelli, Irene; Monzalvo Lopez, Ana Karla; van Ermingen-Marbach, Muna; Grande, Marion; Grabowska, Anna; Heim, Stefan; Ramus, Franck

2015-05-01

The neural basis of specific reading disability (SRD) remains only partly understood. A dozen studies have used voxel-based morphometry (VBM) to investigate gray matter volume (GMV) differences between SRD and control children, however, recent meta-analyses suggest that few regions are consistent across studies. We used data collected across three countries (France, Poland, and Germany) with the aim of both increasing sample size (236 SRD and controls) to obtain a clearer picture of group differences, and of further assessing the consistency of the findings across languages. VBM analysis reveals a significant group difference in a single cluster in the left thalamus. Furthermore, we observe correlations between reading accuracy and GMV in the left supramarginal gyrus and in the left cerebellum, in controls only. Most strikingly, we fail to replicate all the group differences in GMV reported in previous studies, despite the superior statistical power. The main limitation of this study is the heterogeneity of the sample drawn from different countries (i.e., speaking languages with varying orthographic transparencies) and selected based on different assessment batteries. Nevertheless, analyses within each country support the conclusions of the cross-linguistic analysis. Explanations for the discrepancy between the present and previous studies may include: (1) the limited suitability of VBM to reveal the subtle brain disruptions underlying SRD; (2) insufficient correction for multiple statistical tests and flexibility in data analysis, and (3) publication bias in favor of positive results. Thus the study echoes widespread concerns about the risk of false-positive results inherent to small-scale VBM studies. © 2015 Wiley Periodicals, Inc.

Bayesian Optimization for Neuroimaging Pre-processing in Brain Age Classification and Prediction

PubMed Central

Lancaster, Jenessa; Lorenz, Romy; Leech, Rob; Cole, James H.

2018-01-01

Neuroimaging-based age prediction using machine learning is proposed as a biomarker of brain aging, relating to cognitive performance, health outcomes and progression of neurodegenerative disease. However, even leading age-prediction algorithms contain measurement error, motivating efforts to improve experimental pipelines. T1-weighted MRI is commonly used for age prediction, and the pre-processing of these scans involves normalization to a common template and resampling to a common voxel size, followed by spatial smoothing. Resampling parameters are often selected arbitrarily. Here, we sought to improve brain-age prediction accuracy by optimizing resampling parameters using Bayesian optimization. Using data on N = 2003 healthy individuals (aged 16–90 years) we trained support vector machines to (i) distinguish between young (<22 years) and old (>50 years) brains (classification) and (ii) predict chronological age (regression). We also evaluated generalisability of the age-regression model to an independent dataset (CamCAN, N = 648, aged 18–88 years). Bayesian optimization was used to identify optimal voxel size and smoothing kernel size for each task. This procedure adaptively samples the parameter space to evaluate accuracy across a range of possible parameters, using independent sub-samples to iteratively assess different parameter combinations to arrive at optimal values. When distinguishing between young and old brains a classification accuracy of 88.1% was achieved, (optimal voxel size = 11.5 mm3, smoothing kernel = 2.3 mm). For predicting chronological age, a mean absolute error (MAE) of 5.08 years was achieved, (optimal voxel size = 3.73 mm3, smoothing kernel = 3.68 mm). This was compared to performance using default values of 1.5 mm3 and 4mm respectively, resulting in MAE = 5.48 years, though this 7.3% improvement was not statistically significant. When assessing generalisability, best performance was achieved when applying the entire Bayesian optimization framework to the new dataset, out-performing the parameters optimized for the initial training dataset. Our study outlines the proof-of-principle that neuroimaging models for brain-age prediction can use Bayesian optimization to derive case-specific pre-processing parameters. Our results suggest that different pre-processing parameters are selected when optimization is conducted in specific contexts. This potentially motivates use of optimization techniques at many different points during the experimental process, which may improve statistical sensitivity and reduce opportunities for experimenter-led bias. PMID:29483870

Improving accuracy and power with transfer learning using a meta-analytic database.

PubMed

Schwartz, Yannick; Varoquaux, Gaël; Pallier, Christophe; Pinel, Philippe; Poline, Jean-Baptiste; Thirion, Bertrand

2012-01-01

Typical cohorts in brain imaging studies are not large enough for systematic testing of all the information contained in the images. To build testable working hypotheses, investigators thus rely on analysis of previous work, sometimes formalized in a so-called meta-analysis. In brain imaging, this approach underlies the specification of regions of interest (ROIs) that are usually selected on the basis of the coordinates of previously detected effects. In this paper, we propose to use a database of images, rather than coordinates, and frame the problem as transfer learning: learning a discriminant model on a reference task to apply it to a different but related new task. To facilitate statistical analysis of small cohorts, we use a sparse discriminant model that selects predictive voxels on the reference task and thus provides a principled procedure to define ROIs. The benefits of our approach are twofold. First it uses the reference database for prediction, i.e., to provide potential biomarkers in a clinical setting. Second it increases statistical power on the new task. We demonstrate on a set of 18 pairs of functional MRI experimental conditions that our approach gives good prediction. In addition, on a specific transfer situation involving different scanners at different locations, we show that voxel selection based on transfer learning leads to higher detection power on small cohorts.

Brain changes in children and adolescents with obsessive-compulsive disorder before and after treatment: a voxel-based morphometric MRI study.

PubMed

Lázaro, Luisa; Bargalló, Nuria; Castro-Fornieles, Josefina; Falcón, Carles; Andrés, Susana; Calvo, Rosa; Junqué, Carme

2009-05-15

The aim of this study is to determine whether children and adolescents with treatment-naïve obsessive-compulsive disorder (OCD) present brain structure differences in comparison with healthy subjects, and to evaluate brain changes after treatment and clinical improvement. Initial and 6 months' follow-up evaluations were performed in 15 children and adolescents (age range=9-17 years, mean=13.7, S.D.=2.5; 8 male, 7 female) with DSM-IV OCD and 15 healthy subjects matched for age, sex and estimated intellectual level. An evaluation with psychopathological scales and magnetic resonance imaging (MRI) was carried out at admission and after 6 months' follow-up. Axial three-dimensional T1-weighted images were obtained in a 1.5 T scanner and analysed using optimized voxel-based morphometry (VBM) and longitudinal VBM approaches. Compared with controls, OCD patients presented significantly less gray matter volume bilaterally in right and left parietal lobes and right parietal white matter (P=0.001 FWE corrected) at baseline evaluation. After 6 months of treatment, and with a clear clinical improvement, the differences between OCD patients and controls in the parietal lobes in gray and white matter were no longer statistically significant. During follow-up in the longitudinal study, an increase in gray matter volume in the right striatum of OCD patients was observed, though the difference was not statistically significant. Children and adolescents with untreated OCD present gray and white matter decreases in lateral parietal cortices, but this abnormality is reversible after clinical improvement.

Fully automated treatment planning for head and neck radiotherapy using a voxel-based dose prediction and dose mimicking method

NASA Astrophysics Data System (ADS)

McIntosh, Chris; Welch, Mattea; McNiven, Andrea; Jaffray, David A.; Purdie, Thomas G.

2017-08-01

Recent works in automated radiotherapy treatment planning have used machine learning based on historical treatment plans to infer the spatial dose distribution for a novel patient directly from the planning image. We present a probabilistic, atlas-based approach which predicts the dose for novel patients using a set of automatically selected most similar patients (atlases). The output is a spatial dose objective, which specifies the desired dose-per-voxel, and therefore replaces the need to specify and tune dose-volume objectives. Voxel-based dose mimicking optimization then converts the predicted dose distribution to a complete treatment plan with dose calculation using a collapsed cone convolution dose engine. In this study, we investigated automated planning for right-sided oropharaynx head and neck patients treated with IMRT and VMAT. We compare four versions of our dose prediction pipeline using a database of 54 training and 12 independent testing patients by evaluating 14 clinical dose evaluation criteria. Our preliminary results are promising and demonstrate that automated methods can generate comparable dose distributions to clinical. Overall, automated plans achieved an average of 0.6% higher dose for target coverage evaluation criteria, and 2.4% lower dose at the organs at risk criteria levels evaluated compared with clinical. There was no statistically significant difference detected in high-dose conformity between automated and clinical plans as measured by the conformation number. Automated plans achieved nine more unique criteria than clinical across the 12 patients tested and automated plans scored a significantly higher dose at the evaluation limit for two high-risk target coverage criteria and a significantly lower dose in one critical organ maximum dose. The novel dose prediction method with dose mimicking can generate complete treatment plans in 12-13 min without user interaction. It is a promising approach for fully automated treatment planning and can be readily applied to different treatment sites and modalities.

Fully automated treatment planning for head and neck radiotherapy using a voxel-based dose prediction and dose mimicking method.

PubMed

McIntosh, Chris; Welch, Mattea; McNiven, Andrea; Jaffray, David A; Purdie, Thomas G

2017-07-06

Recent works in automated radiotherapy treatment planning have used machine learning based on historical treatment plans to infer the spatial dose distribution for a novel patient directly from the planning image. We present a probabilistic, atlas-based approach which predicts the dose for novel patients using a set of automatically selected most similar patients (atlases). The output is a spatial dose objective, which specifies the desired dose-per-voxel, and therefore replaces the need to specify and tune dose-volume objectives. Voxel-based dose mimicking optimization then converts the predicted dose distribution to a complete treatment plan with dose calculation using a collapsed cone convolution dose engine. In this study, we investigated automated planning for right-sided oropharaynx head and neck patients treated with IMRT and VMAT. We compare four versions of our dose prediction pipeline using a database of 54 training and 12 independent testing patients by evaluating 14 clinical dose evaluation criteria. Our preliminary results are promising and demonstrate that automated methods can generate comparable dose distributions to clinical. Overall, automated plans achieved an average of 0.6% higher dose for target coverage evaluation criteria, and 2.4% lower dose at the organs at risk criteria levels evaluated compared with clinical. There was no statistically significant difference detected in high-dose conformity between automated and clinical plans as measured by the conformation number. Automated plans achieved nine more unique criteria than clinical across the 12 patients tested and automated plans scored a significantly higher dose at the evaluation limit for two high-risk target coverage criteria and a significantly lower dose in one critical organ maximum dose. The novel dose prediction method with dose mimicking can generate complete treatment plans in 12-13 min without user interaction. It is a promising approach for fully automated treatment planning and can be readily applied to different treatment sites and modalities.

The role of gray and white matter segmentation in quantitative proton MR spectroscopic imaging.

PubMed

Tal, Assaf; Kirov, Ivan I; Grossman, Robert I; Gonen, Oded

2012-12-01

Since the brain's gray matter (GM) and white matter (WM) metabolite concentrations differ, their partial volumes can vary the voxel's ¹H MR spectroscopy (¹H-MRS) signal, reducing sensitivity to changes. While single-voxel ¹H-MRS cannot differentiate between WM and GM signals, partial volume correction is feasible by MR spectroscopic imaging (MRSI) using segmentation of the MRI acquired for VOI placement. To determine the magnitude of this effect on metabolic quantification, we segmented a 1-mm³ resolution MRI into GM, WM and CSF masks that were co-registered with the MRSI grid to yield their partial volumes in approximately every 1 cm³ spectroscopic voxel. Each voxel then provided one equation with two unknowns: its i- metabolite's GM and WM concentrations C(i) (GM) , C(i) (WM) . With the voxels' GM and WM volumes as independent coefficients, the over-determined system of equations was solved for the global averaged C(i) (GM) and C(i) (WM) . Trading off local concentration differences offers three advantages: (i) higher sensitivity due to combined data from many voxels; (ii) improved specificity to WM versus GM changes; and (iii) reduced susceptibility to partial volume effects. These improvements made no additional demands on the protocol, measurement time or hardware. Applying this approach to 18 volunteered 3D MRSI sets of 480 voxels each yielded N-acetylaspartate, creatine, choline and myo-inositol C(i) (GM) concentrations of 8.5 ± 0.7, 6.9 ± 0.6, 1.2 ± 0.2, 5.3 ± 0.6 mM, respectively, and C(i) (WM) concentrations of 7.7 ± 0.6, 4.9 ± 0.5, 1.4 ± 0.1 and 4.4 ± 0.6mM, respectively. We showed that unaccounted voxel WM or GM partial volume can vary absolute quantification by 5-10% (more for ratios), which can often double the sample size required to establish statistical significance. Copyright © 2012 John Wiley & Sons, Ltd.

An analysis of regional cerebral blood flow in impulsive murderers using single photon emission computed tomography.

PubMed

Amen, Daniel G; Hanks, Chris; Prunella, Jill R; Green, Aisa

2007-01-01

The authors explored differences in regional cerebral blood flow in 11 impulsive murderers and 11 healthy comparison subjects using single photon emission computed tomography. The authors assessed subjects at rest and during a computerized go/no-go concentration task. Using statistical parametric mapping software, the authors performed voxel-by-voxel t tests to assess significant differences, making family-wide error corrections for multiple comparisons. Murderers were found to have significantly lower relative rCBF during concentration, particularly in areas associated with concentration and impulse control. These results indicate that nonemotionally laden stimuli may result in frontotemporal dysregulation in people predisposed to impulsive violence.

Comparative Study of SVM Methods Combined with Voxel Selection for Object Category Classification on fMRI Data

PubMed Central

Song, Sutao; Zhan, Zhichao; Long, Zhiying; Zhang, Jiacai; Yao, Li

2011-01-01

Background Support vector machine (SVM) has been widely used as accurate and reliable method to decipher brain patterns from functional MRI (fMRI) data. Previous studies have not found a clear benefit for non-linear (polynomial kernel) SVM versus linear one. Here, a more effective non-linear SVM using radial basis function (RBF) kernel is compared with linear SVM. Different from traditional studies which focused either merely on the evaluation of different types of SVM or the voxel selection methods, we aimed to investigate the overall performance of linear and RBF SVM for fMRI classification together with voxel selection schemes on classification accuracy and time-consuming. Methodology/Principal Findings Six different voxel selection methods were employed to decide which voxels of fMRI data would be included in SVM classifiers with linear and RBF kernels in classifying 4-category objects. Then the overall performances of voxel selection and classification methods were compared. Results showed that: (1) Voxel selection had an important impact on the classification accuracy of the classifiers: in a relative low dimensional feature space, RBF SVM outperformed linear SVM significantly; in a relative high dimensional space, linear SVM performed better than its counterpart; (2) Considering the classification accuracy and time-consuming holistically, linear SVM with relative more voxels as features and RBF SVM with small set of voxels (after PCA) could achieve the better accuracy and cost shorter time. Conclusions/Significance The present work provides the first empirical result of linear and RBF SVM in classification of fMRI data, combined with voxel selection methods. Based on the findings, if only classification accuracy was concerned, RBF SVM with appropriate small voxels and linear SVM with relative more voxels were two suggested solutions; if users concerned more about the computational time, RBF SVM with relative small set of voxels when part of the principal components were kept as features was a better choice. PMID:21359184

Comparative study of SVM methods combined with voxel selection for object category classification on fMRI data.

PubMed

Song, Sutao; Zhan, Zhichao; Long, Zhiying; Zhang, Jiacai; Yao, Li

2011-02-16

Support vector machine (SVM) has been widely used as accurate and reliable method to decipher brain patterns from functional MRI (fMRI) data. Previous studies have not found a clear benefit for non-linear (polynomial kernel) SVM versus linear one. Here, a more effective non-linear SVM using radial basis function (RBF) kernel is compared with linear SVM. Different from traditional studies which focused either merely on the evaluation of different types of SVM or the voxel selection methods, we aimed to investigate the overall performance of linear and RBF SVM for fMRI classification together with voxel selection schemes on classification accuracy and time-consuming. Six different voxel selection methods were employed to decide which voxels of fMRI data would be included in SVM classifiers with linear and RBF kernels in classifying 4-category objects. Then the overall performances of voxel selection and classification methods were compared. Results showed that: (1) Voxel selection had an important impact on the classification accuracy of the classifiers: in a relative low dimensional feature space, RBF SVM outperformed linear SVM significantly; in a relative high dimensional space, linear SVM performed better than its counterpart; (2) Considering the classification accuracy and time-consuming holistically, linear SVM with relative more voxels as features and RBF SVM with small set of voxels (after PCA) could achieve the better accuracy and cost shorter time. The present work provides the first empirical result of linear and RBF SVM in classification of fMRI data, combined with voxel selection methods. Based on the findings, if only classification accuracy was concerned, RBF SVM with appropriate small voxels and linear SVM with relative more voxels were two suggested solutions; if users concerned more about the computational time, RBF SVM with relative small set of voxels when part of the principal components were kept as features was a better choice.

Robust adaptive 3-D segmentation of vessel laminae from fluorescence confocal microscope images and parallel GPU implementation.

PubMed

Narayanaswamy, Arunachalam; Dwarakapuram, Saritha; Bjornsson, Christopher S; Cutler, Barbara M; Shain, William; Roysam, Badrinath

2010-03-01

This paper presents robust 3-D algorithms to segment vasculature that is imaged by labeling laminae, rather than the lumenal volume. The signal is weak, sparse, noisy, nonuniform, low-contrast, and exhibits gaps and spectral artifacts, so adaptive thresholding and Hessian filtering based methods are not effective. The structure deviates from a tubular geometry, so tracing algorithms are not effective. We propose a four step approach. The first step detects candidate voxels using a robust hypothesis test based on a model that assumes Poisson noise and locally planar geometry. The second step performs an adaptive region growth to extract weakly labeled and fine vessels while rejecting spectral artifacts. To enable interactive visualization and estimation of features such as statistical confidence, local curvature, local thickness, and local normal, we perform the third step. In the third step, we construct an accurate mesh representation using marching tetrahedra, volume-preserving smoothing, and adaptive decimation algorithms. To enable topological analysis and efficient validation, we describe a method to estimate vessel centerlines using a ray casting and vote accumulation algorithm which forms the final step of our algorithm. Our algorithm lends itself to parallel processing, and yielded an 8 x speedup on a graphics processor (GPU). On synthetic data, our meshes had average error per face (EPF) values of (0.1-1.6) voxels per mesh face for peak signal-to-noise ratios from (110-28 dB). Separately, the error from decimating the mesh to less than 1% of its original size, the EPF was less than 1 voxel/face. When validated on real datasets, the average recall and precision values were found to be 94.66% and 94.84%, respectively.

Multi-layer cube sampling for liver boundary detection in PET-CT images.

PubMed

Liu, Xinxin; Yang, Jian; Song, Shuang; Song, Hong; Ai, Danni; Zhu, Jianjun; Jiang, Yurong; Wang, Yongtian

2018-06-01

Liver metabolic information is considered as a crucial diagnostic marker for the diagnosis of fever of unknown origin, and liver recognition is the basis of automatic diagnosis of metabolic information extraction. However, the poor quality of PET and CT images is a challenge for information extraction and target recognition in PET-CT images. The existing detection method cannot meet the requirement of liver recognition in PET-CT images, which is the key problem in the big data analysis of PET-CT images. A novel texture feature descriptor called multi-layer cube sampling (MLCS) is developed for liver boundary detection in low-dose CT and PET images. The cube sampling feature is proposed for extracting more texture information, which uses a bi-centric voxel strategy. Neighbour voxels are divided into three regions by the centre voxel and the reference voxel in the histogram, and the voxel distribution information is statistically classified as texture feature. Multi-layer texture features are also used to improve the ability and adaptability of target recognition in volume data. The proposed feature is tested on the PET and CT images for liver boundary detection. For the liver in the volume data, mean detection rate (DR) and mean error rate (ER) reached 95.15 and 7.81% in low-quality PET images, and 83.10 and 21.08% in low-contrast CT images. The experimental results demonstrated that the proposed method is effective and robust for liver boundary detection.

DEMARCATE: Density-based magnetic resonance image clustering for assessing tumor heterogeneity in cancer.

PubMed

Saha, Abhijoy; Banerjee, Sayantan; Kurtek, Sebastian; Narang, Shivali; Lee, Joonsang; Rao, Ganesh; Martinez, Juan; Bharath, Karthik; Rao, Arvind U K; Baladandayuthapani, Veerabhadran

2016-01-01

Tumor heterogeneity is a crucial area of cancer research wherein inter- and intra-tumor differences are investigated to assess and monitor disease development and progression, especially in cancer. The proliferation of imaging and linked genomic data has enabled us to evaluate tumor heterogeneity on multiple levels. In this work, we examine magnetic resonance imaging (MRI) in patients with brain cancer to assess image-based tumor heterogeneity. Standard approaches to this problem use scalar summary measures (e.g., intensity-based histogram statistics) that do not adequately capture the complete and finer scale information in the voxel-level data. In this paper, we introduce a novel technique, DEMARCATE (DEnsity-based MAgnetic Resonance image Clustering for Assessing Tumor hEterogeneity) to explore the entire tumor heterogeneity density profiles (THDPs) obtained from the full tumor voxel space. THDPs are smoothed representations of the probability density function of the tumor images. We develop tools for analyzing such objects under the Fisher-Rao Riemannian framework that allows us to construct metrics for THDP comparisons across patients, which can be used in conjunction with standard clustering approaches. Our analyses of The Cancer Genome Atlas (TCGA) based Glioblastoma dataset reveal two significant clusters of patients with marked differences in tumor morphology, genomic characteristics and prognostic clinical outcomes. In addition, we see enrichment of image-based clusters with known molecular subtypes of glioblastoma multiforme, which further validates our representation of tumor heterogeneity and subsequent clustering techniques.

Localized shape abnormalities in the thalamus and pallidum are associated with secondarily generalized seizures in mesial temporal lobe epilepsy.

PubMed

Yang, Linglin; Li, Hong; Zhu, Lujia; Yu, Xinfeng; Jin, Bo; Chen, Cong; Wang, Shan; Ding, Meiping; Zhang, Minming; Chen, Zhong; Wang, Shuang

2017-05-01

Mesial temporal lobe epilepsy (mTLE) is a common type of drug-resistant epilepsy and secondarily generalized tonic-clonic seizures (sGTCS) have devastating consequences for patients' safety and quality of life. To probe the mechanism underlying the genesis of sGTCS, we investigated the structural differences between patients with and without sGTCS in a cohort of mTLE with radiologically defined unilateral hippocampal sclerosis. We performed voxel-based morphometric analysis of cortex and vertex-wise shape analysis of subcortical structures (the basal ganglia and thalamus) on MRI of 39 patients (21 with and 18 without sGTCS). Comparisons were initially made between sGTCS and non-sGTCS groups, and subsequently made between uncontrolled-sGTCS and controlled-sGTCS subgroups. Regional atrophy of the ipsilateral ventral pallidum (cluster size=450 voxels, corrected p=0.047, Max voxel coordinate=107, 120, 65), medial thalamus (cluster size=1128 voxels, corrected p=0.049, Max voxel coordinate=107, 93, 67), middle frontal gyrus (cluster size=60 voxels, corrected p<0.05, Max voxel coordinate=-30, 49.5, 6), and contralateral posterior cingulate cortex (cluster size=130 voxels, corrected p<0.05, Max voxel coordinate=16.5, -57, 27) was found in the sGTCS group relative to the non-sGTCS group. Furthermore, the uncontrolled-sGTCS subgroup showed more pronounced atrophy of the ipsilateral medial thalamus (cluster size=1240 voxels, corrected p=0.014, Max voxel coordinate=107, 93, 67) than the controlled-sGTCS subgroup. These findings indicate a central role of thalamus and pallidum in the pathophysiology of sGTCS in mTLE. Copyright © 2017 Elsevier Inc. All rights reserved.

Voxel-Based Neighborhood for Spatial Shape Pattern Classification of Lidar Point Clouds with Supervised Learning.

PubMed

Plaza-Leiva, Victoria; Gomez-Ruiz, Jose Antonio; Mandow, Anthony; García-Cerezo, Alfonso

2017-03-15

Improving the effectiveness of spatial shape features classification from 3D lidar data is very relevant because it is largely used as a fundamental step towards higher level scene understanding challenges of autonomous vehicles and terrestrial robots. In this sense, computing neighborhood for points in dense scans becomes a costly process for both training and classification. This paper proposes a new general framework for implementing and comparing different supervised learning classifiers with a simple voxel-based neighborhood computation where points in each non-overlapping voxel in a regular grid are assigned to the same class by considering features within a support region defined by the voxel itself. The contribution provides offline training and online classification procedures as well as five alternative feature vector definitions based on principal component analysis for scatter, tubular and planar shapes. Moreover, the feasibility of this approach is evaluated by implementing a neural network (NN) method previously proposed by the authors as well as three other supervised learning classifiers found in scene processing methods: support vector machines (SVM), Gaussian processes (GP), and Gaussian mixture models (GMM). A comparative performance analysis is presented using real point clouds from both natural and urban environments and two different 3D rangefinders (a tilting Hokuyo UTM-30LX and a Riegl). Classification performance metrics and processing time measurements confirm the benefits of the NN classifier and the feasibility of voxel-based neighborhood.

WE-D-BRE-06: Quantification of Dose-Response for High Grade Esophagtis Patients Using a Novel Voxel-To-Voxel Method

DOE Office of Scientific and Technical Information (OSTI.GOV)

Niedzielski, J; Martel, M; Tucker, S

2014-06-15

Purpose: Radiation induces an inflammatory response in the esophagus, discernible on CT studies. This work objectively quantifies the voxel esophageal radiation-response for patients with acute esophagitis. This knowledge is an important first-step towards predicting the effect of complex dose distributions on patient esophagitis symptoms. Methods: A previously validated voxel-based methodology of quantifying radiation esophagitis severity was used to identify the voxel dose-response for 18 NSCLC patients with severe esophagitis (CTCAE grading criteria, grade2 or higher). The response is quantified as percent voxel volume change for a given dose. During treatment (6–8 weeks), patients had weekly 4DCT studies and esophagitis scoring.more » Planning CT esophageal contours were deformed to each weekly CT using a demons DIR algorithm. An algorithm using the Jacobian Map from the DIR of the planning CT to all weekly CTs was used to quantify voxel-volume change, along with corresponding delivered voxel dose, to the planning voxel. Dose for each voxel for each time-point was calculated on each previous weekly CT image, and accumulated using DIR. Thus, for each voxel, the volume-change and delivered dose was calculated for each time-point. The data was binned according to when the volume-change first increased by a threshold volume (10%–100%, in 10% increments), and the average delivered dose calculated for each bin. Results: The average dose resulting in a voxel volume increase of 10–100% was 21.6 to 45.9Gy, respectively. The mean population dose to give a 50% volume increase was 36.3±4.4Gy, (range:29.8 to 43.5Gy). The average week of 50% response was 4.1 (range:4.9 to 2.8 weeks). All 18 patients showed similar dose to first response curves, showing a common trend in the initial inflammatoryresponse. Conclusion: We extracted the dose-response curve of the esophagus on a voxel-to-voxel level. This may be useful for estimating the esophagus response (and patient symptoms) to complicated dose distributions.« less

Impact of voxel size variation on CBCT-based diagnostic outcome in dentistry: a systematic review.

PubMed

Spin-Neto, Rubens; Gotfredsen, Erik; Wenzel, Ann

2013-08-01

The objective of this study was to make a systematic review on the impact of voxel size in cone beam computed tomography (CBCT)-based image acquisition, retrieving evidence regarding the diagnostic outcome of those images. The MEDLINE bibliographic database was searched from 1950 to June 2012 for reports comparing diverse CBCT voxel sizes. The search strategy was limited to English-language publications using the following combined terms in the search strategy: (voxel or FOV or field of view or resolution) and (CBCT or cone beam CT). The results from the review identified 20 publications that qualitatively or quantitatively assessed the influence of voxel size on CBCT-based diagnostic outcome, and in which the methodology/results comprised at least one of the expected parameters (image acquisition, reconstruction protocols, type of diagnostic task, and presence of a gold standard). The diagnostic task assessed in the studies was diverse, including the detection of root fractures, the detection of caries lesions, and accuracy of 3D surface reconstruction and of bony measurements, among others. From the studies assessed, it is clear that no general protocol can be yet defined for CBCT examination of specific diagnostic tasks in dentistry. Rationale in this direction is an important step to define the utility of CBCT imaging.

Towards a voxel-based geographic automata for the simulation of geospatial processes

NASA Astrophysics Data System (ADS)

Jjumba, Anthony; Dragićević, Suzana

2016-07-01

Many geographic processes evolve in a three dimensional space and time continuum. However, when they are represented with the aid of geographic information systems (GIS) or geosimulation models they are modelled in a framework of two-dimensional space with an added temporal component. The objective of this study is to propose the design and implementation of voxel-based automata as a methodological approach for representing spatial processes evolving in the four-dimensional (4D) space-time domain. Similar to geographic automata models which are developed to capture and forecast geospatial processes that change in a two-dimensional spatial framework using cells (raster geospatial data), voxel automata rely on the automata theory and use three-dimensional volumetric units (voxels). Transition rules have been developed to represent various spatial processes which range from the movement of an object in 3D to the diffusion of airborne particles and landslide simulation. In addition, the proposed 4D models demonstrate that complex processes can be readily reproduced from simple transition functions without complex methodological approaches. The voxel-based automata approach provides a unique basis to model geospatial processes in 4D for the purpose of improving representation, analysis and understanding their spatiotemporal dynamics. This study contributes to the advancement of the concepts and framework of 4D GIS.

Estimation of integral curves from high angular resolution diffusion imaging (HARDI) data.

PubMed

Carmichael, Owen; Sakhanenko, Lyudmila

2015-05-15

We develop statistical methodology for a popular brain imaging technique HARDI based on the high order tensor model by Özarslan and Mareci [10]. We investigate how uncertainty in the imaging procedure propagates through all levels of the model: signals, tensor fields, vector fields, and fibers. We construct asymptotically normal estimators of the integral curves or fibers which allow us to trace the fibers together with confidence ellipsoids. The procedure is computationally intense as it blends linear algebra concepts from high order tensors with asymptotical statistical analysis. The theoretical results are illustrated on simulated and real datasets. This work generalizes the statistical methodology proposed for low angular resolution diffusion tensor imaging by Carmichael and Sakhanenko [3], to several fibers per voxel. It is also a pioneering statistical work on tractography from HARDI data. It avoids all the typical limitations of the deterministic tractography methods and it delivers the same information as probabilistic tractography methods. Our method is computationally cheap and it provides well-founded mathematical and statistical framework where diverse functionals on fibers, directions and tensors can be studied in a systematic and rigorous way.

Estimation of integral curves from high angular resolution diffusion imaging (HARDI) data

PubMed Central

Carmichael, Owen; Sakhanenko, Lyudmila

2015-01-01

We develop statistical methodology for a popular brain imaging technique HARDI based on the high order tensor model by Özarslan and Mareci [10]. We investigate how uncertainty in the imaging procedure propagates through all levels of the model: signals, tensor fields, vector fields, and fibers. We construct asymptotically normal estimators of the integral curves or fibers which allow us to trace the fibers together with confidence ellipsoids. The procedure is computationally intense as it blends linear algebra concepts from high order tensors with asymptotical statistical analysis. The theoretical results are illustrated on simulated and real datasets. This work generalizes the statistical methodology proposed for low angular resolution diffusion tensor imaging by Carmichael and Sakhanenko [3], to several fibers per voxel. It is also a pioneering statistical work on tractography from HARDI data. It avoids all the typical limitations of the deterministic tractography methods and it delivers the same information as probabilistic tractography methods. Our method is computationally cheap and it provides well-founded mathematical and statistical framework where diverse functionals on fibers, directions and tensors can be studied in a systematic and rigorous way. PMID:25937674

Detection by voxel-wise statistical analysis of significant changes in regional cerebral glucose uptake in an APP/PS1 transgenic mouse model of Alzheimer's disease.

PubMed

Dubois, Albertine; Hérard, Anne-Sophie; Delatour, Benoît; Hantraye, Philippe; Bonvento, Gilles; Dhenain, Marc; Delzescaux, Thierry

2010-06-01

Biomarkers and technologies similar to those used in humans are essential for the follow-up of Alzheimer's disease (AD) animal models, particularly for the clarification of mechanisms and the screening and validation of new candidate treatments. In humans, changes in brain metabolism can be detected by 1-deoxy-2-[(18)F] fluoro-D-glucose PET (FDG-PET) and assessed in a user-independent manner with dedicated software, such as Statistical Parametric Mapping (SPM). FDG-PET can be carried out in small animals, but its resolution is low as compared to the size of rodent brain structures. In mouse models of AD, changes in cerebral glucose utilization are usually detected by [(14)C]-2-deoxyglucose (2DG) autoradiography, but this requires prior manual outlining of regions of interest (ROI) on selected sections. Here, we evaluate the feasibility of applying the SPM method to 3D autoradiographic data sets mapping brain metabolic activity in a transgenic mouse model of AD. We report the preliminary results obtained with 4 APP/PS1 (64+/-1 weeks) and 3 PS1 (65+/-2 weeks) mice. We also describe new procedures for the acquisition and use of "blockface" photographs and provide the first demonstration of their value for the 3D reconstruction and spatial normalization of post mortem mouse brain volumes. Despite this limited sample size, our results appear to be meaningful, consistent, and more comprehensive than findings from previously published studies based on conventional ROI-based methods. The establishment of statistical significance at the voxel level, rather than with a user-defined ROI, makes it possible to detect more reliably subtle differences in geometrically complex regions, such as the hippocampus. Our approach is generic and could be easily applied to other biomarkers and extended to other species and applications. Copyright 2010 Elsevier Inc. All rights reserved.

Summary Statistics for Homemade ?Play Dough? -- Data Acquired at LLNL

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kallman, J S; Morales, K E; Whipple, R E

Using x-ray computerized tomography (CT), we have characterized the x-ray linear attenuation coefficients (LAC) of a homemade Play Dough{trademark}-like material, designated as PDA. Table 1 gives the first-order statistics for each of four CT measurements, estimated with a Gaussian kernel density estimator (KDE) analysis. The mean values of the LAC range from a high of about 2700 LMHU{sub D} 100kVp to a low of about 1200 LMHUD at 300kVp. The standard deviation of each measurement is around 10% to 15% of the mean. The entropy covers the range from 6.0 to 7.4. Ordinarily, we would model the LAC of themore » material and compare the modeled values to the measured values. In this case, however, we did not have the detailed chemical composition of the material and therefore did not model the LAC. Using a method recently proposed by Lawrence Livermore National Laboratory (LLNL), we estimate the value of the effective atomic number, Z{sub eff}, to be near 10. LLNL prepared about 50mL of the homemade 'Play Dough' in a polypropylene vial and firmly compressed it immediately prior to the x-ray measurements. We used the computer program IMGREC to reconstruct the CT images. The values of the key parameters used in the data capture and image reconstruction are given in this report. Additional details may be found in the experimental SOP and a separate document. To characterize the statistical distribution of LAC values in each CT image, we first isolated an 80% central-core segment of volume elements ('voxels') lying completely within the specimen, away from the walls of the polypropylene vial. All of the voxels within this central core, including those comprised of voids and inclusions, are included in the statistics. We then calculated the mean value, standard deviation and entropy for (a) the four image segments and for (b) their digital gradient images. (A digital gradient image of a given image was obtained by taking the absolute value of the difference between the initial image and that same image offset by one voxel horizontally, parallel to the rows of the x-ray detector array.) The statistics of the initial image of LAC values are called 'first order statistics;' those of the gradient image, 'second order statistics.'« less

Cluster size statistic and cluster mass statistic: two novel methods for identifying changes in functional connectivity between groups or conditions.

PubMed

Ing, Alex; Schwarzbauer, Christian

2014-01-01

Functional connectivity has become an increasingly important area of research in recent years. At a typical spatial resolution, approximately 300 million connections link each voxel in the brain with every other. This pattern of connectivity is known as the functional connectome. Connectivity is often compared between experimental groups and conditions. Standard methods used to control the type 1 error rate are likely to be insensitive when comparisons are carried out across the whole connectome, due to the huge number of statistical tests involved. To address this problem, two new cluster based methods--the cluster size statistic (CSS) and cluster mass statistic (CMS)--are introduced to control the family wise error rate across all connectivity values. These methods operate within a statistical framework similar to the cluster based methods used in conventional task based fMRI. Both methods are data driven, permutation based and require minimal statistical assumptions. Here, the performance of each procedure is evaluated in a receiver operator characteristic (ROC) analysis, utilising a simulated dataset. The relative sensitivity of each method is also tested on real data: BOLD (blood oxygen level dependent) fMRI scans were carried out on twelve subjects under normal conditions and during the hypercapnic state (induced through the inhalation of 6% CO2 in 21% O2 and 73%N2). Both CSS and CMS detected significant changes in connectivity between normal and hypercapnic states. A family wise error correction carried out at the individual connection level exhibited no significant changes in connectivity.

Cluster Size Statistic and Cluster Mass Statistic: Two Novel Methods for Identifying Changes in Functional Connectivity Between Groups or Conditions

PubMed Central

Ing, Alex; Schwarzbauer, Christian

2014-01-01

Functional connectivity has become an increasingly important area of research in recent years. At a typical spatial resolution, approximately 300 million connections link each voxel in the brain with every other. This pattern of connectivity is known as the functional connectome. Connectivity is often compared between experimental groups and conditions. Standard methods used to control the type 1 error rate are likely to be insensitive when comparisons are carried out across the whole connectome, due to the huge number of statistical tests involved. To address this problem, two new cluster based methods – the cluster size statistic (CSS) and cluster mass statistic (CMS) – are introduced to control the family wise error rate across all connectivity values. These methods operate within a statistical framework similar to the cluster based methods used in conventional task based fMRI. Both methods are data driven, permutation based and require minimal statistical assumptions. Here, the performance of each procedure is evaluated in a receiver operator characteristic (ROC) analysis, utilising a simulated dataset. The relative sensitivity of each method is also tested on real data: BOLD (blood oxygen level dependent) fMRI scans were carried out on twelve subjects under normal conditions and during the hypercapnic state (induced through the inhalation of 6% CO2 in 21% O2 and 73%N2). Both CSS and CMS detected significant changes in connectivity between normal and hypercapnic states. A family wise error correction carried out at the individual connection level exhibited no significant changes in connectivity. PMID:24906136

Measure Projection Analysis: A Probabilistic Approach to EEG Source Comparison and Multi-Subject Inference

PubMed Central

Bigdely-Shamlo, Nima; Mullen, Tim; Kreutz-Delgado, Kenneth; Makeig, Scott

2013-01-01

A crucial question for the analysis of multi-subject and/or multi-session electroencephalographic (EEG) data is how to combine information across multiple recordings from different subjects and/or sessions, each associated with its own set of source processes and scalp projections. Here we introduce a novel statistical method for characterizing the spatial consistency of EEG dynamics across a set of data records. Measure Projection Analysis (MPA) first finds voxels in a common template brain space at which a given dynamic measure is consistent across nearby source locations, then computes local-mean EEG measure values for this voxel subspace using a statistical model of source localization error and between-subject anatomical variation. Finally, clustering the mean measure voxel values in this locally consistent brain subspace finds brain spatial domains exhibiting distinguishable measure features and provides 3-D maps plus statistical significance estimates for each EEG measure of interest. Applied to sufficient high-quality data, the scalp projections of many maximally independent component (IC) processes contributing to recorded high-density EEG data closely match the projection of a single equivalent dipole located in or near brain cortex. We demonstrate the application of MPA to a multi-subject EEG study decomposed using independent component analysis (ICA), compare the results to k-means IC clustering in EEGLAB (sccn.ucsd.edu/eeglab), and use surrogate data to test MPA robustness. A Measure Projection Toolbox (MPT) plug-in for EEGLAB is available for download (sccn.ucsd.edu/wiki/MPT). Together, MPA and ICA allow use of EEG as a 3-D cortical imaging modality with near-cm scale spatial resolution. PMID:23370059

Validation of DTI Tractography-Based Measures of Primary Motor Area Connectivity in the Squirrel Monkey Brain

PubMed Central

Gao, Yurui; Choe, Ann S.; Stepniewska, Iwona; Li, Xia; Avison, Malcolm J.; Anderson, Adam W.

2013-01-01

Diffusion tensor imaging (DTI) tractography provides noninvasive measures of structural cortico-cortical connectivity of the brain. However, the agreement between DTI-tractography-based measures and histological ‘ground truth’ has not been quantified. In this study, we reconstructed the 3D density distribution maps (DDM) of fibers labeled with an anatomical tracer, biotinylated dextran amine (BDA), as well as DTI tractography-derived streamlines connecting the primary motor (M1) cortex to other cortical regions in the squirrel monkey brain. We evaluated the agreement in M1-cortical connectivity between the fibers labeled in the brain tissue and DTI streamlines on a regional and voxel-by-voxel basis. We found that DTI tractography is capable of providing inter-regional connectivity comparable to the neuroanatomical connectivity, but is less reliable measuring voxel-to-voxel variations within regions. PMID:24098365

Subsurface data visualization in Virtual Reality

NASA Astrophysics Data System (ADS)

Krijnen, Robbert; Smelik, Ruben; Appleton, Rick; van Maanen, Peter-Paul

2017-04-01

Due to their increasing complexity and size, visualization of geological data is becoming more and more important. It enables detailed examining and reviewing of large volumes of geological data and it is often used as a communication tool for reporting and education to demonstrate the importance of the geology to policy makers. In the Netherlands two types of nation-wide geological models are available: 1) Layer-based models in which the subsurface is represented by a series of tops and bases of geological or hydrogeological units, and 2) Voxel models in which the subsurface is subdivided in a regular grid of voxels that can contain different properties per voxel. The Geological Survey of the Netherlands (GSN) provides an interactive web portal that delivers maps and vertical cross-sections of such layer-based and voxel models. From this portal you can download a 3D subsurface viewer that can visualize the voxel model data of an area of 20 × 25 km with 100 × 100 × 5 meter voxel resolution on a desktop computer. Virtual Reality (VR) technology enables us to enhance the visualization of this volumetric data in a more natural way as compared to a standard desktop, keyboard mouse setup. The use of VR for data visualization is not new but recent developments has made expensive hardware and complex setups unnecessary. The availability of consumer of-the-shelf VR hardware enabled us to create an new intuitive and low visualization tool. A VR viewer has been implemented using the HTC Vive head set and allows visualization and analysis of the GSN voxel model data with geological or hydrogeological units. The user can navigate freely around the voxel data (20 × 25 km) which is presented in a virtual room at a scale of 2 × 2 or 3 × 3 meters. To enable analysis, e.g. hydraulic conductivity, the user can select filters to remove specific hydrogeological units. The user can also use slicing to cut-off specific sections of the voxel data to get a closer look. This slicing can be done in any direction using a 'virtual knife'. Future plans are to further improve performance from 30 up to 90 Hz update rate to reduce possible motion sickness, add more advanced filtering capabilities as well as a multi user setup, annotation capabilities and visualizing of historical data.

Neural Correlates of Communication Skill and Symptom Severity in Autism: A Voxel-Based Morphometry Study

ERIC Educational Resources Information Center

Parks, Lauren K.; Hill, Dina E.; Thoma, Robert J.; Euler, Matthew J.; Lewine, Jeffrey D.; Yeo, Ronald A.

2009-01-01

Although many studies have compared the brains of normal controls and individuals with autism, especially older, higher-functioning individuals with autism, little is known of the neural correlates of the vast clinical heterogeneity characteristic of the disorder. In this study, we used voxel-based morphometry (VBM) to examine gray matter…

The Neural Basis of Reversible Sentence Comprehension: Evidence from Voxel-Based Lesion Symptom Mapping in Aphasia

ERIC Educational Resources Information Center

Thothathiri, Malathi; Kimberg, Daniel Y.; Schwartz, Myrna F.

2012-01-01

We explored the neural basis of reversible sentence comprehension in a large group of aphasic patients (n = 79). Voxel-based lesion symptom mapping revealed a significant association between damage in temporo-parietal cortex and impaired sentence comprehension. This association remained after we controlled for phonological working memory. We…

The Pivotal Role of the Parieto-Occipital Lobe in Card Game-Induced Reflex Epilepsy: A Voxel-Based Morphometry Study.

PubMed

Park, Kang Min; Kim, Sung Eun; Lee, Byung In

2016-01-01

The pathogenesis of card game-induced reflex epilepsy has not been determined so far. The aim of this study was to evaluate structural abnormalities using voxel-based morphometry (VBM) analysis, which may give some clue about the pathogenesis in card game-induced reflex epilepsy. The 3 subjects were diagnosed with card game-induced reflex epilepsy. Evaluation involved a structured interview to obtain clinical information and brain MRI. In VBM analysis, Statistical Parametric Mapping 8 running on the MATLAB platform was employed to analyze the structural differences between patients with card game-induced reflex epilepsy and age- and sex-matched control subjects. The results of VBM analysis revealed that patients with card game-induced reflex epilepsy had significantly increased gray matter volume in the right occipital and parietal lobe. However, there were no structures with decreased gray matter volume in patients with card game-induced reflex epilepsy compared with control subjects. In addition, we found that the patients with card game-induced reflex epilepsy had onset of seizures in adulthood rather than in adolescence, and all of the patients were men. The parieto-occipital lobes might be partially involved in the neuronal network responsible for card game-induced reflex epilepsy. © 2016 S. Karger AG, Basel.

Gray Matter Differences between Pediatric Obsessive-Compulsive Disorder Patients and High-Risk Siblings: A Preliminary Voxel-Based Morphometry Study

PubMed Central

Gilbert, Andrew R.; Keshavan, Matcheri S.; Diwadkar, Vaibhav; Nutche, Jeffrey; MacMaster, Frank; Easter, Phillip C.; Buhagiar, Christian J.; Rosenberg, David R.

2008-01-01

Neuroimaging studies have identified alterations in frontostriatal circuitry in OCD. Voxel-based morphometry (VBM) allows for the assessment of differences in gray matter density across the whole brain. VBM has not previously been used to examine regional gray matter density in pediatric OCD patients and the siblings of pediatric OCD patients. Volumetric magnetic resonance imaging (MRI) studies were conducted in 10 psychotropic-naïve pediatric patients with OCD, 10 unaffected siblings of pediatric patients with OCD, and 10 healthy controls. VBM analysis was conducted using SPM2. Statistical comparisons were performed with the general linear model, implementing small volume random field corrections for a priori regions of interest (anterior cingulate cortex or ACC, striatum and thalamus). VBM analysis revealed significantly lower gray matter density in OCD patients compared to healthy in the left ACC and bilateral medial superior frontal gyrus (SFG). Furthermore, a small volume correction was used to identify a significantly greater gray matter density in the right putamen in OCD patients as compared to unaffected siblings of OCD patients. These findings in patients, siblings, and healthy controls, although preliminary, suggest the presence of gray matter structural differences between affected subjects and healthy controls as well as between affected subjects and individuals at risk for OCD. PMID:18314272

Inter-patient image registration algorithms to disentangle regional dose bioeffects.

PubMed

Monti, Serena; Pacelli, Roberto; Cella, Laura; Palma, Giuseppe

2018-03-20

Radiation therapy (RT) technological advances call for a comprehensive reconsideration of the definition of dose features leading to radiation induced morbidity (RIM). In this context, the voxel-based approach (VBA) to dose distribution analysis in RT offers a radically new philosophy to evaluate local dose response patterns, as an alternative to dose-volume-histograms for identifying dose sensitive regions of normal tissue. The VBA relies on mapping patient dose distributions into a single reference case anatomy which serves as anchor for local dosimetric evaluations. The inter-patient elastic image registrations (EIRs) of the planning CTs provide the deformation fields necessary for the actual warp of dose distributions. In this study we assessed the impact of EIR on the VBA results in thoracic patients by identifying two state-of-the-art EIR algorithms (Demons and B-Spline). Our analysis demonstrated that both the EIR algorithms may be successfully used to highlight subregions with dose differences associated with RIM that substantially overlap. Furthermore, the inclusion for the first time of covariates within a dosimetric statistical model that faces the multiple comparison problem expands the potential of VBA, thus paving the way to a reliable voxel-based analysis of RIM in datasets with strong correlation of the outcome with non-dosimetric variables.

Applications of wavelets in morphometric analysis of medical images

NASA Astrophysics Data System (ADS)

Davatzikos, Christos; Tao, Xiaodong; Shen, Dinggang

2003-11-01

Morphometric analysis of medical images is playing an increasingly important role in understanding brain structure and function, as well as in understanding the way in which these change during development, aging and pathology. This paper presents three wavelet-based methods with related applications in morphometric analysis of magnetic resonance (MR) brain images. The first method handles cases where very limited datasets are available for the training of statistical shape models in the deformable segmentation. The method is capable of capturing a larger range of shape variability than the standard active shape models (ASMs) can, by using the elegant spatial-frequency decomposition of the shape contours provided by wavelet transforms. The second method addresses the difficulty of finding correspondences in anatomical images, which is a key step in shape analysis and deformable registration. The detection of anatomical correspondences is completed by using wavelet-based attribute vectors as morphological signatures of voxels. The third method uses wavelets to characterize the morphological measurements obtained from all voxels in a brain image, and the entire set of wavelet coefficients is further used to build a brain classifier. Since the classification scheme operates in a very-high-dimensional space, it can determine subtle population differences with complex spatial patterns. Experimental results are provided to demonstrate the performance of the proposed methods.

Simultaneous Analysis and Quality Assurance for Diffusion Tensor Imaging

PubMed Central

Lauzon, Carolyn B.; Asman, Andrew J.; Esparza, Michael L.; Burns, Scott S.; Fan, Qiuyun; Gao, Yurui; Anderson, Adam W.; Davis, Nicole; Cutting, Laurie E.; Landman, Bennett A.

2013-01-01

Diffusion tensor imaging (DTI) enables non-invasive, cyto-architectural mapping of in vivo tissue microarchitecture through voxel-wise mathematical modeling of multiple magnetic resonance imaging (MRI) acquisitions, each differently sensitized to water diffusion. DTI computations are fundamentally estimation processes and are sensitive to noise and artifacts. Despite widespread adoption in the neuroimaging community, maintaining consistent DTI data quality remains challenging given the propensity for patient motion, artifacts associated with fast imaging techniques, and the possibility of hardware changes/failures. Furthermore, the quantity of data acquired per voxel, the non-linear estimation process, and numerous potential use cases complicate traditional visual data inspection approaches. Currently, quality inspection of DTI data has relied on visual inspection and individual processing in DTI analysis software programs (e.g. DTIPrep, DTI-studio). However, recent advances in applied statistical methods have yielded several different metrics to assess noise level, artifact propensity, quality of tensor fit, variance of estimated measures, and bias in estimated measures. To date, these metrics have been largely studied in isolation. Herein, we select complementary metrics for integration into an automatic DTI analysis and quality assurance pipeline. The pipeline completes in 24 hours, stores statistical outputs, and produces a graphical summary quality analysis (QA) report. We assess the utility of this streamlined approach for empirical quality assessment on 608 DTI datasets from pediatric neuroimaging studies. The efficiency and accuracy of quality analysis using the proposed pipeline is compared with quality analysis based on visual inspection. The unified pipeline is found to save a statistically significant amount of time (over 70%) while improving the consistency of QA between a DTI expert and a pool of research associates. Projection of QA metrics to a low dimensional manifold reveal qualitative, but clear, QA-study associations and suggest that automated outlier/anomaly detection would be feasible. PMID:23637895

Chronic auditory hallucinations in schizophrenic patients: MR analysis of the coincidence between functional and morphologic abnormalities.

PubMed

Martí-Bonmatí, Luis; Lull, Juan José; García-Martí, Gracián; Aguilar, Eduardo J; Moratal-Pérez, David; Poyatos, Cecilio; Robles, Montserrat; Sanjuán, Julio

2007-08-01

To prospectively evaluate if functional magnetic resonance (MR) imaging abnormalities associated with auditory emotional stimuli coexist with focal brain reductions in schizophrenic patients with chronic auditory hallucinations. Institutional review board approval was obtained and all participants gave written informed consent. Twenty-one right-handed male patients with schizophrenia and persistent hallucinations (started to hear hallucinations at a mean age of 23 years +/- 10, with 15 years +/- 8 of mean illness duration) and 10 healthy paired participants (same ethnic group [white], age, and education level [secondary school]) were studied. Functional echo-planar T2*-weighted (after both emotional and neutral auditory stimulation) and morphometric three-dimensional gradient-recalled echo T1-weighted MR images were analyzed using Statistical Parametric Mapping (SPM2) software. Brain activation images were extracted by subtracting those with emotional from nonemotional words. Anatomic differences were explored by optimized voxel-based morphometry. The functional and morphometric MR images were overlaid to depict voxels statistically reported by both techniques. A coincidence map was generated by multiplying the emotional subtracted functional MR and volume decrement morphometric maps. Statistical analysis used the general linear model, Student t tests, random effects analyses, and analysis of covariance with a correction for multiple comparisons following the false discovery rate method. Large coinciding brain clusters (P < .005) were found in the left and right middle temporal and superior temporal gyri. Smaller coinciding clusters were found in the left posterior and right anterior cingular gyri, left inferior frontal gyrus, and middle occipital gyrus. The middle and superior temporal and the cingular gyri are closely related to the abnormal neural network involved in the auditory emotional dysfunction seen in schizophrenic patients.

Simultaneous analysis and quality assurance for diffusion tensor imaging.

PubMed

Lauzon, Carolyn B; Asman, Andrew J; Esparza, Michael L; Burns, Scott S; Fan, Qiuyun; Gao, Yurui; Anderson, Adam W; Davis, Nicole; Cutting, Laurie E; Landman, Bennett A

2013-01-01

Diffusion tensor imaging (DTI) enables non-invasive, cyto-architectural mapping of in vivo tissue microarchitecture through voxel-wise mathematical modeling of multiple magnetic resonance imaging (MRI) acquisitions, each differently sensitized to water diffusion. DTI computations are fundamentally estimation processes and are sensitive to noise and artifacts. Despite widespread adoption in the neuroimaging community, maintaining consistent DTI data quality remains challenging given the propensity for patient motion, artifacts associated with fast imaging techniques, and the possibility of hardware changes/failures. Furthermore, the quantity of data acquired per voxel, the non-linear estimation process, and numerous potential use cases complicate traditional visual data inspection approaches. Currently, quality inspection of DTI data has relied on visual inspection and individual processing in DTI analysis software programs (e.g. DTIPrep, DTI-studio). However, recent advances in applied statistical methods have yielded several different metrics to assess noise level, artifact propensity, quality of tensor fit, variance of estimated measures, and bias in estimated measures. To date, these metrics have been largely studied in isolation. Herein, we select complementary metrics for integration into an automatic DTI analysis and quality assurance pipeline. The pipeline completes in 24 hours, stores statistical outputs, and produces a graphical summary quality analysis (QA) report. We assess the utility of this streamlined approach for empirical quality assessment on 608 DTI datasets from pediatric neuroimaging studies. The efficiency and accuracy of quality analysis using the proposed pipeline is compared with quality analysis based on visual inspection. The unified pipeline is found to save a statistically significant amount of time (over 70%) while improving the consistency of QA between a DTI expert and a pool of research associates. Projection of QA metrics to a low dimensional manifold reveal qualitative, but clear, QA-study associations and suggest that automated outlier/anomaly detection would be feasible.

Regional Gray Matter Volumes Are Related to Concern About Falling in Older People: A Voxel-Based Morphometric Study.

PubMed

Tuerk, Carola; Zhang, Haobo; Sachdev, Perminder; Lord, Stephen R; Brodaty, Henry; Wen, Wei; Delbaere, Kim

2016-01-01

Concern about falling is common in older people. Various related psychological constructs as well as poor balance and slow gait have been associated with decreased gray matter (GM) volume in old age. The current study investigates the association between concern about falling and voxel-wise GM volumes. A total of 281 community-dwelling older people aged 70-90 years underwent structural magnetic resonance imaging. Concern about falling was assessed using Falls Efficacy Scale-International (FES-I). For each participant, voxel-wise GM volumes were generated with voxel-based morphometry and regressed on raw FES-I scores (p < .05 family-wise error corrected on cluster level). FES-I scores were negatively correlated with total brain volume (r = -.212; p ≤ .001), GM volume (r = -.210; p ≤ .001), and white matter volume (r = -.155; p ≤ .001). Voxel-based morphometry analysis revealed significant negative associations between FES-I and GM volumes of (i) left cerebellum and bilateral inferior occipital gyrus (voxels-in-cluster = 2,981; p < .001) and (ii) bilateral superior frontal gyrus and left supplementary motor area (voxels-in-cluster = 1,900; p = .004). Additional adjustment for vision and physical fall risk did not alter these associations. After adjustment for anxiety, only left cerebellum and bilateral inferior occipital gyrus remained negatively associated with FES-I scores (voxels-in-cluster = 2,426; p < .001). Adjustment for neuroticism removed all associations between FES-I and GM volumes. Our study findings show that concern about falling is negatively associated with brain volumes in areas important for emotional control and for motor control, executive functions and visual processing in a large sample of older men and women. Regression analyses suggest that these relationships were primarily accounted for by psychological factors (generalized anxiety and neuroticism) and not by physical fall risk or vision. © The Author 2015. Published by Oxford University Press on behalf of The Gerontological Society of America. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Atlas Based Segmentation and Mapping of Organs at Risk from Planning CT for the Development of Voxel-Wise Predictive Models of Toxicity in Prostate Radiotherapy

NASA Astrophysics Data System (ADS)

Acosta, Oscar; Dowling, Jason; Cazoulat, Guillaume; Simon, Antoine; Salvado, Olivier; de Crevoisier, Renaud; Haigron, Pascal

The prediction of toxicity is crucial to managing prostate cancer radiotherapy (RT). This prediction is classically organ wise and based on the dose volume histograms (DVH) computed during the planning step, and using for example the mathematical Lyman Normal Tissue Complication Probability (NTCP) model. However, these models lack spatial accuracy, do not take into account deformations and may be inappropiate to explain toxicity events related with the distribution of the delivered dose. Producing voxel wise statistical models of toxicity might help to explain the risks linked to the dose spatial distribution but is challenging due to the difficulties lying on the mapping of organs and dose in a common template. In this paper we investigate the use of atlas based methods to perform the non-rigid mapping and segmentation of the individuals' organs at risk (OAR) from CT scans. To build a labeled atlas, 19 CT scans were selected from a population of patients treated for prostate cancer by radiotherapy. The prostate and the OAR (Rectum, Bladder, Bones) were then manually delineated by an expert and constituted the training data. After a number of affine and non rigid registration iterations, an average image (template) representing the whole population was obtained. The amount of consensus between labels was used to generate probabilistic maps for each organ. We validated the accuracy of the approach by segmenting the organs using the training data in a leave one out scheme. The agreement between the volumes after deformable registration and the manually segmented organs was on average above 60% for the organs at risk. The proposed methodology provides a way to map the organs from a whole population on a single template and sets the stage to perform further voxel wise analysis. With this method new and accurate predictive models of toxicity will be built.

Connectopic mapping with resting-state fMRI.

PubMed

Haak, Koen V; Marquand, Andre F; Beckmann, Christian F

2018-04-15

Brain regions are often topographically connected: nearby locations within one brain area connect with nearby locations in another area. Mapping these connection topographies, or 'connectopies' in short, is crucial for understanding how information is processed in the brain. Here, we propose principled, fully data-driven methods for mapping connectopies using functional magnetic resonance imaging (fMRI) data acquired at rest by combining spectral embedding of voxel-wise connectivity 'fingerprints' with a novel approach to spatial statistical inference. We apply the approach in human primary motor and visual cortex, and show that it can trace biologically plausible, overlapping connectopies in individual subjects that follow these regions' somatotopic and retinotopic maps. As a generic mechanism to perform inference over connectopies, the new spatial statistics approach enables rigorous statistical testing of hypotheses regarding the fine-grained spatial profile of functional connectivity and whether that profile is different between subjects or between experimental conditions. The combined framework offers a fundamental alternative to existing approaches to investigating functional connectivity in the brain, from voxel- or seed-pair wise characterizations of functional association, towards a full, multivariate characterization of spatial topography. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.

Diffusion anisotropy in fresh and fixed prostate tissue ex vivo.

PubMed

Bourne, Roger M; Bongers, Andre; Chatterjee, Aritrick; Sved, Paul; Watson, Geoffrey

2016-08-01

To investigate diffusion anisotropy in whole human prostate specimens Seven whole radical prostatectomy specimens were obtained with informed patient consent and institutional ethics approval. Diffusion tensor imaging was performed at 9.4 Tesla. Diffusion tensors were calculated from the native acquired data and after progressive downsampling Fractional anisotropy (FA) decreased as voxel volume increased, and differed widely between prostates. Fixation decreased mean FA by ∼0.05-0.08 at all voxel volumes but did not alter principle eigenvector orientation. In unfixed tissue high FA (> 0.6) was found only in voxels of volume <0.5 mm(3) , and then only in a small fraction of all voxels. At typical clinical voxel volumes (4-16 mm(3) ) less than 50% of voxels had FA > 0.25. FA decreased at longer diffusion times (Δ = 60 or 80 ms compared with 20 ms), but only by ∼0.02 at typical clinical voxel volume. Peripheral zone FA was significantly lower than transition zone FA in five of the seven prostates FA varies widely between prostates. The very small proportion of clinical size voxels with high FA suggests that in clinical DWI studies ADC based on three-direction measurements will be minimally affected by anisotropy. Magn Reson Med 76:626-634, 2016. © 2015 Wiley Periodicals, Inc. © 2015 Wiley Periodicals, Inc.

SU-E-J-71: Spatially Preserving Prior Knowledge-Based Treatment Planning

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wang, H; Xing, L

2015-06-15

Purpose: Prior knowledge-based treatment planning is impeded by the use of a single dose volume histogram (DVH) curve. Critical spatial information is lost from collapsing the dose distribution into a histogram. Even similar patients possess geometric variations that becomes inaccessible in the form of a single DVH. We propose a simple prior knowledge-based planning scheme that extracts features from prior dose distribution while still preserving the spatial information. Methods: A prior patient plan is not used as a mere starting point for a new patient but rather stopping criteria are constructed. Each structure from the prior patient is partitioned intomore » multiple shells. For instance, the PTV is partitioned into an inner, middle, and outer shell. Prior dose statistics are then extracted for each shell and translated into the appropriate Dmin and Dmax parameters for the new patient. Results: The partitioned dose information from a prior case has been applied onto 14 2-D prostate cases. Using prior case yielded final DVHs that was comparable to manual planning, even though the DVH for the prior case was different from the DVH for the 14 cases. Solely using a single DVH for the entire organ was also performed for comparison but showed a much poorer performance. Different ways of translating the prior dose statistics into parameters for the new patient was also tested. Conclusion: Prior knowledge-based treatment planning need to salvage the spatial information without transforming the patients on a voxel to voxel basis. An efficient balance between the anatomy and dose domain is gained through partitioning the organs into multiple shells. The use of prior knowledge not only serves as a starting point for a new case but the information extracted from the partitioned shells are also translated into stopping criteria for the optimization problem at hand.« less

Accelerating IMRT optimization by voxel sampling

NASA Astrophysics Data System (ADS)

Martin, Benjamin C.; Bortfeld, Thomas R.; Castañon, David A.

2007-12-01

This paper presents a new method for accelerating intensity-modulated radiation therapy (IMRT) optimization using voxel sampling. Rather than calculating the dose to the entire patient at each step in the optimization, the dose is only calculated for some randomly selected voxels. Those voxels are then used to calculate estimates of the objective and gradient which are used in a randomized version of a steepest descent algorithm. By selecting different voxels on each step, we are able to find an optimal solution to the full problem. We also present an algorithm to automatically choose the best sampling rate for each structure within the patient during the optimization. Seeking further improvements, we experimented with several other gradient-based optimization algorithms and found that the delta-bar-delta algorithm performs well despite the randomness. Overall, we were able to achieve approximately an order of magnitude speedup on our test case as compared to steepest descent.

Distributed task coding throughout the multiple demand network of the human frontal-insular cortex.

PubMed

Stiers, Peter; Mennes, Maarten; Sunaert, Stefan

2010-08-01

The large variety of tasks that humans can perform is governed by a small number of key frontal-insular regions that are commonly active during task performance. Little is known about how this network distinguishes different tasks. We report on fMRI data in twelve participants while they performed four cognitive tasks. Of 20 commonly active frontal-insular regions in each hemisphere, five showed a BOLD response increase with increased task demands, regardless of the task. Although active in all tasks, each task invoked a unique response pattern across the voxels in each area that proved reliable in split-half multi-voxel correlation analysis. Consequently, voxels differed in their preference for one or more of the tasks. Voxel-based functional connectivity analyses revealed that same preference voxels distributed across all areas of the network constituted functional sub-networks that characterized the task being executed. Copyright 2010 Elsevier Inc. All rights reserved.

Development of local complexity metrics to quantify the effect of anatomical noise on detectability of lung nodules in chest CT imaging

NASA Astrophysics Data System (ADS)

Solomon, Justin; Rubin, Geoffrey; Smith, Taylor; Harrawood, Brian; Choudhury, Kingshuk Roy; Samei, Ehsan

2017-03-01

The purpose of this study was to develop metrics of local anatomical complexity and compare them with detectability of lung nodules in CT. Data were drawn retrospectively from a published perception experiment in which detectability was assessed in cases enriched with virtual nodules (13 radiologists x 157 total nodules = 2041 responses). A local anatomical complexity metric called the distractor index was developed, defined as the Gaussian weighted proportion (i.e., average) of distracting local voxels (50 voxels in-plane, 5 slices). A distracting voxel was classified by thresholding image data that had been selectively filtered to enhance nodule-like features. The distractor index was measured for each nodule location in the nodule-free images. The local pixel standard deviation (STD) was also measured for each nodule. Other confounding factors of search fraction (proportion of lung voxels to total voxels in the given slice) and peripheral distance (defined as the 3D distance of the nodule from the trachea bifurcation) were measured. A generalized linear mixed-effects statistical model (no interaction terms, probit link function, random reader term) was fit to the data to determine the influence of each metric on detectability. In order of decreasing effect size: distractor index, STD, and search fraction all significantly affected detectability (P < 0.001). Distance to the trachea did not have a significant effect (P < 0.05). These data demonstrate that local lung complexity degrades detection of lung nodules and the distractor index could serve as a good surrogate metric to quantify anatomical complexity.

Voxel-Based Neighborhood for Spatial Shape Pattern Classification of Lidar Point Clouds with Supervised Learning

PubMed Central

Plaza-Leiva, Victoria; Gomez-Ruiz, Jose Antonio; Mandow, Anthony; García-Cerezo, Alfonso

2017-01-01

Improving the effectiveness of spatial shape features classification from 3D lidar data is very relevant because it is largely used as a fundamental step towards higher level scene understanding challenges of autonomous vehicles and terrestrial robots. In this sense, computing neighborhood for points in dense scans becomes a costly process for both training and classification. This paper proposes a new general framework for implementing and comparing different supervised learning classifiers with a simple voxel-based neighborhood computation where points in each non-overlapping voxel in a regular grid are assigned to the same class by considering features within a support region defined by the voxel itself. The contribution provides offline training and online classification procedures as well as five alternative feature vector definitions based on principal component analysis for scatter, tubular and planar shapes. Moreover, the feasibility of this approach is evaluated by implementing a neural network (NN) method previously proposed by the authors as well as three other supervised learning classifiers found in scene processing methods: support vector machines (SVM), Gaussian processes (GP), and Gaussian mixture models (GMM). A comparative performance analysis is presented using real point clouds from both natural and urban environments and two different 3D rangefinders (a tilting Hokuyo UTM-30LX and a Riegl). Classification performance metrics and processing time measurements confirm the benefits of the NN classifier and the feasibility of voxel-based neighborhood. PMID:28294963

The importance of group-wise registration in tract based spatial statistics study of neurodegeneration: a simulation study in Alzheimer's disease.

PubMed

Keihaninejad, Shiva; Ryan, Natalie S; Malone, Ian B; Modat, Marc; Cash, David; Ridgway, Gerard R; Zhang, Hui; Fox, Nick C; Ourselin, Sebastien

2012-01-01

Tract-based spatial statistics (TBSS) is a popular method for the analysis of diffusion tensor imaging data. TBSS focuses on differences in white matter voxels with high fractional anisotropy (FA), representing the major fibre tracts, through registering all subjects to a common reference and the creation of a FA skeleton. This work considers the effect of choice of reference in the TBSS pipeline, which can be a standard template, an individual subject from the study, a study-specific template or a group-wise average. While TBSS attempts to overcome registration error by searching the neighbourhood perpendicular to the FA skeleton for the voxel with maximum FA, this projection step may not compensate for large registration errors that might occur in the presence of pathology such as atrophy in neurodegenerative diseases. This makes registration performance and choice of reference an important issue. Substantial work in the field of computational anatomy has shown the use of group-wise averages to reduce biases while avoiding the arbitrary selection of a single individual. Here, we demonstrate the impact of the choice of reference on: (a) specificity (b) sensitivity in a simulation study and (c) a real-world comparison of Alzheimer's disease patients to controls. In (a) and (b), simulated deformations and decreases in FA were applied to control subjects to simulate changes of shape and WM integrity similar to what would be seen in AD patients, in order to provide a "ground truth" for evaluating the various methods of TBSS reference. Using a group-wise average atlas as the reference outperformed other references in the TBSS pipeline in all evaluations.

The Importance of Group-Wise Registration in Tract Based Spatial Statistics Study of Neurodegeneration: A Simulation Study in Alzheimer's Disease

PubMed Central

Keihaninejad, Shiva; Ryan, Natalie S.; Malone, Ian B.; Modat, Marc; Cash, David; Ridgway, Gerard R.; Zhang, Hui; Fox, Nick C.; Ourselin, Sebastien

2012-01-01

Tract-based spatial statistics (TBSS) is a popular method for the analysis of diffusion tensor imaging data. TBSS focuses on differences in white matter voxels with high fractional anisotropy (FA), representing the major fibre tracts, through registering all subjects to a common reference and the creation of a FA skeleton. This work considers the effect of choice of reference in the TBSS pipeline, which can be a standard template, an individual subject from the study, a study-specific template or a group-wise average. While TBSS attempts to overcome registration error by searching the neighbourhood perpendicular to the FA skeleton for the voxel with maximum FA, this projection step may not compensate for large registration errors that might occur in the presence of pathology such as atrophy in neurodegenerative diseases. This makes registration performance and choice of reference an important issue. Substantial work in the field of computational anatomy has shown the use of group-wise averages to reduce biases while avoiding the arbitrary selection of a single individual. Here, we demonstrate the impact of the choice of reference on: (a) specificity (b) sensitivity in a simulation study and (c) a real-world comparison of Alzheimer's disease patients to controls. In (a) and (b), simulated deformations and decreases in FA were applied to control subjects to simulate changes of shape and WM integrity similar to what would be seen in AD patients, in order to provide a “ground truth” for evaluating the various methods of TBSS reference. Using a group-wise average atlas as the reference outperformed other references in the TBSS pipeline in all evaluations. PMID:23139736

Voxel-based morphometry and automated lobar volumetry: The trade-off between spatial scale and statistical correction

PubMed Central

Voormolen, Eduard H.J.; Wei, Corie; Chow, Eva W.C.; Bassett, Anne S.; Mikulis, David J.; Crawley, Adrian P.

2011-01-01

Voxel-based morphometry (VBM) and automated lobar region of interest (ROI) volumetry are comprehensive and fast methods to detect differences in overall brain anatomy on magnetic resonance images. However, VBM and automated lobar ROI volumetry have detected dissimilar gray matter differences within identical image sets in our own experience and in previous reports. To gain more insight into how diverging results arise and to attempt to establish whether one method is superior to the other, we investigated how differences in spatial scale and in the need to statistically correct for multiple spatial comparisons influence the relative sensitivity of either technique to group differences in gray matter volumes. We assessed the performance of both techniques on a small dataset containing simulated gray matter deficits and additionally on a dataset of 22q11-deletion syndrome patients with schizophrenia (22q11DS-SZ) vs. matched controls. VBM was more sensitive to simulated focal deficits compared to automated ROI volumetry, and could detect global cortical deficits equally well. Moreover, theoretical calculations of VBM and ROI detection sensitivities to focal deficits showed that at increasing ROI size, ROI volumetry suffers more from loss in sensitivity than VBM. Furthermore, VBM and automated ROI found corresponding GM deficits in 22q11DS-SZ patients, except in the parietal lobe. Here, automated lobar ROI volumetry found a significant deficit only after a smaller subregion of interest was employed. Thus, sensitivity to focal differences is impaired relatively more by averaging over larger volumes in automated ROI methods than by the correction for multiple comparisons in VBM. These findings indicate that VBM is to be preferred over automated lobar-scale ROI volumetry for assessing gray matter volume differences between groups. PMID:19619660

A decomposition model and voxel selection framework for fMRI analysis to predict neural response of visual stimuli.

PubMed

Raut, Savita V; Yadav, Dinkar M

2018-03-28

This paper presents an fMRI signal analysis methodology using geometric mean curve decomposition (GMCD) and mutual information-based voxel selection framework. Previously, the fMRI signal analysis has been conducted using empirical mean curve decomposition (EMCD) model and voxel selection on raw fMRI signal. The erstwhile methodology loses frequency component, while the latter methodology suffers from signal redundancy. Both challenges are addressed by our methodology in which the frequency component is considered by decomposing the raw fMRI signal using geometric mean rather than arithmetic mean and the voxels are selected from EMCD signal using GMCD components, rather than raw fMRI signal. The proposed methodologies are adopted for predicting the neural response. Experimentations are conducted in the openly available fMRI data of six subjects, and comparisons are made with existing decomposition models and voxel selection frameworks. Subsequently, the effect of degree of selected voxels and the selection constraints are analyzed. The comparative results and the analysis demonstrate the superiority and the reliability of the proposed methodology.

Use of the FLUKA Monte Carlo code for 3D patient-specific dosimetry on PET-CT and SPECT-CT images*

PubMed Central

Botta, F; Mairani, A; Hobbs, R F; Vergara Gil, A; Pacilio, M; Parodi, K; Cremonesi, M; Coca Pérez, M A; Di Dia, A; Ferrari, M; Guerriero, F; Battistoni, G; Pedroli, G; Paganelli, G; Torres Aroche, L A; Sgouros, G

2014-01-01

Patient-specific absorbed dose calculation for nuclear medicine therapy is a topic of increasing interest. 3D dosimetry at the voxel level is one of the major improvements for the development of more accurate calculation techniques, as compared to the standard dosimetry at the organ level. This study aims to use the FLUKA Monte Carlo code to perform patient-specific 3D dosimetry through direct Monte Carlo simulation on PET-CT and SPECT-CT images. To this aim, dedicated routines were developed in the FLUKA environment. Two sets of simulations were performed on model and phantom images. Firstly, the correct handling of PET and SPECT images was tested under the assumption of homogeneous water medium by comparing FLUKA results with those obtained with the voxel kernel convolution method and with other Monte Carlo-based tools developed to the same purpose (the EGS-based 3D-RD software and the MCNP5-based MCID). Afterwards, the correct integration of the PET/SPECT and CT information was tested, performing direct simulations on PET/CT images for both homogeneous (water) and non-homogeneous (water with air, lung and bone inserts) phantoms. Comparison was performed with the other Monte Carlo tools performing direct simulation as well. The absorbed dose maps were compared at the voxel level. In the case of homogeneous water, by simulating 108 primary particles a 2% average difference with respect to the kernel convolution method was achieved; such difference was lower than the statistical uncertainty affecting the FLUKA results. The agreement with the other tools was within 3–4%, partially ascribable to the differences among the simulation algorithms. Including the CT-based density map, the average difference was always within 4% irrespective of the medium (water, air, bone), except for a maximum 6% value when comparing FLUKA and 3D-RD in air. The results confirmed that the routines were properly developed, opening the way for the use of FLUKA for patient-specific, image-based dosimetry in nuclear medicine. PMID:24200697

Early Gray-Matter and White-Matter Concentration in Infancy Predict Later Language Skills: A Whole Brain Voxel-Based Morphometry Study

ERIC Educational Resources Information Center

Can, Dilara Deniz; Richards, Todd; Kuhl, Patricia K.

2013-01-01

Magnetic Resonance Imaging (MRI) brain scans were obtained from 19 infants at 7 months. Expressive and receptive language performance was assessed at 12 months. Voxel-based morphometry (VBM) identified brain regions where gray-matter and white-matter concentrations at 7 months correlated significantly with children's language scores at 12 months.…

Dyslexia and Voxel-Based Morphometry: Correlations between Five Behavioural Measures of Dyslexia and Gray and White Matter Volumes

ERIC Educational Resources Information Center

Tamboer, Peter; Scholte, H. Steven; Vorst, Harrie C. M.

2015-01-01

In voxel-based morphometry studies of dyslexia, the relation between causal theories of dyslexia and gray matter (GM) and white matter (WM) volume alterations is still under debate. Some alterations are consistently reported, but others failed to reach significance. We investigated GM alterations in a large sample of Dutch students (37 dyslexics…

Feature Selection based on Machine Learning in MRIs for Hippocampal Segmentation

NASA Astrophysics Data System (ADS)

Tangaro, Sabina; Amoroso, Nicola; Brescia, Massimo; Cavuoti, Stefano; Chincarini, Andrea; Errico, Rosangela; Paolo, Inglese; Longo, Giuseppe; Maglietta, Rosalia; Tateo, Andrea; Riccio, Giuseppe; Bellotti, Roberto

2015-01-01

Neurodegenerative diseases are frequently associated with structural changes in the brain. Magnetic resonance imaging (MRI) scans can show these variations and therefore can be used as a supportive feature for a number of neurodegenerative diseases. The hippocampus has been known to be a biomarker for Alzheimer disease and other neurological and psychiatric diseases. However, it requires accurate, robust, and reproducible delineation of hippocampal structures. Fully automatic methods are usually the voxel based approach; for each voxel a number of local features were calculated. In this paper, we compared four different techniques for feature selection from a set of 315 features extracted for each voxel: (i) filter method based on the Kolmogorov-Smirnov test; two wrapper methods, respectively, (ii) sequential forward selection and (iii) sequential backward elimination; and (iv) embedded method based on the Random Forest Classifier on a set of 10 T1-weighted brain MRIs and tested on an independent set of 25 subjects. The resulting segmentations were compared with manual reference labelling. By using only 23 feature for each voxel (sequential backward elimination) we obtained comparable state-of-the-art performances with respect to the standard tool FreeSurfer.

Generation of Ground Truth Datasets for the Analysis of 3d Point Clouds in Urban Scenes Acquired via Different Sensors

NASA Astrophysics Data System (ADS)

Xu, Y.; Sun, Z.; Boerner, R.; Koch, T.; Hoegner, L.; Stilla, U.

2018-04-01

In this work, we report a novel way of generating ground truth dataset for analyzing point cloud from different sensors and the validation of algorithms. Instead of directly labeling large amount of 3D points requiring time consuming manual work, a multi-resolution 3D voxel grid for the testing site is generated. Then, with the help of a set of basic labeled points from the reference dataset, we can generate a 3D labeled space of the entire testing site with different resolutions. Specifically, an octree-based voxel structure is applied to voxelize the annotated reference point cloud, by which all the points are organized by 3D grids of multi-resolutions. When automatically annotating the new testing point clouds, a voting based approach is adopted to the labeled points within multiple resolution voxels, in order to assign a semantic label to the 3D space represented by the voxel. Lastly, robust line- and plane-based fast registration methods are developed for aligning point clouds obtained via various sensors. Benefiting from the labeled 3D spatial information, we can easily create new annotated 3D point clouds of different sensors of the same scene directly by considering the corresponding labels of 3D space the points located, which would be convenient for the validation and evaluation of algorithms related to point cloud interpretation and semantic segmentation.

A Voxel-by-Voxel Comparison of Deformable Vector Fields Obtained by Three Deformable Image Registration Algorithms Applied to 4DCT Lung Studies.

PubMed

Fatyga, Mirek; Dogan, Nesrin; Weiss, Elizabeth; Sleeman, William C; Zhang, Baoshe; Lehman, William J; Williamson, Jeffrey F; Wijesooriya, Krishni; Christensen, Gary E

2015-01-01

Commonly used methods of assessing the accuracy of deformable image registration (DIR) rely on image segmentation or landmark selection. These methods are very labor intensive and thus limited to relatively small number of image pairs. The direct voxel-by-voxel comparison can be automated to examine fluctuations in DIR quality on a long series of image pairs. A voxel-by-voxel comparison of three DIR algorithms applied to lung patients is presented. Registrations are compared by comparing volume histograms formed both with individual DIR maps and with a voxel-by-voxel subtraction of the two maps. When two DIR maps agree one concludes that both maps are interchangeable in treatment planning applications, though one cannot conclude that either one agrees with the ground truth. If two DIR maps significantly disagree one concludes that at least one of the maps deviates from the ground truth. We use the method to compare 3 DIR algorithms applied to peak inhale-peak exhale registrations of 4DFBCT data obtained from 13 patients. All three algorithms appear to be nearly equivalent when compared using DICE similarity coefficients. A comparison based on Jacobian volume histograms shows that all three algorithms measure changes in total volume of the lungs with reasonable accuracy, but show large differences in the variance of Jacobian distribution on contoured structures. Analysis of voxel-by-voxel subtraction of DIR maps shows differences between algorithms that exceed a centimeter for some registrations. Deformation maps produced by DIR algorithms must be treated as mathematical approximations of physical tissue deformation that are not self-consistent and may thus be useful only in applications for which they have been specifically validated. The three algorithms tested in this work perform fairly robustly for the task of contour propagation, but produce potentially unreliable results for the task of DVH accumulation or measurement of local volume change. Performance of DIR algorithms varies significantly from one image pair to the next hence validation efforts, which are exhaustive but performed on a small number of image pairs may not reflect the performance of the same algorithm in practical clinical situations. Such efforts should be supplemented by validation based on a longer series of images of clinical quality.

Fine-resolution voxel S values for constructing absorbed dose distributions at variable voxel size.

PubMed

Dieudonné, Arnaud; Hobbs, Robert F; Bolch, Wesley E; Sgouros, George; Gardin, Isabelle

2010-10-01

This article presents a revised voxel S values (VSVs) approach for dosimetry in targeted radiotherapy, allowing dose calculation for any voxel size and shape of a given SPECT or PET dataset. This approach represents an update to the methodology presented in MIRD pamphlet no. 17. VSVs were generated in soft tissue with a fine spatial sampling using the Monte Carlo (MC) code MCNPX for particle emissions of 9 radionuclides: (18)F, (90)Y, (99m)Tc, (111)In, (123)I, (131)I, (177)Lu, (186)Re, and (201)Tl. A specific resampling algorithm was developed to compute VSVs for desired voxel dimensions. The dose calculation was performed by convolution via a fast Hartley transform. The fine VSVs were calculated for cubic voxels of 0.5 mm for electrons and 1.0 mm for photons. Validation studies were done for (90)Y and (131)I VSV sets by comparing the revised VSV approach to direct MC simulations. The first comparison included 20 spheres with different voxel sizes (3.8-7.7 mm) and radii (4-64 voxels) and the second comparison a hepatic tumor with cubic voxels of 3.8 mm. MC simulations were done with MCNPX for both. The third comparison was performed on 2 clinical patients with the 3D-RD (3-Dimensional Radiobiologic Dosimetry) software using the EGSnrc (Electron Gamma Shower National Research Council Canada)-based MC implementation, assuming a homogeneous tissue-density distribution. For the sphere model study, the mean relative difference in the average absorbed dose was 0.20% ± 0.41% for (90)Y and -0.36% ± 0.51% for (131)I (n = 20). For the hepatic tumor, the difference in the average absorbed dose to tumor was 0.33% for (90)Y and -0.61% for (131)I and the difference in average absorbed dose to the liver was 0.25% for (90)Y and -1.35% for (131)I. The comparison with the 3D-RD software showed an average voxel-to-voxel dose ratio between 0.991 and 0.996. The calculation time was below 10 s with the VSV approach and 50 and 15 h with 3D-RD for the 2 clinical patients. This new VSV approach enables the calculation of absorbed dose based on a SPECT or PET cumulated activity map, with good agreement with direct MC methods, in a faster and more clinically compatible manner.

Mapping Informative Clusters in a Hierarchial Framework of fMRI Multivariate Analysis

PubMed Central

Xu, Rui; Zhen, Zonglei; Liu, Jia

2010-01-01

Pattern recognition methods have become increasingly popular in fMRI data analysis, which are powerful in discriminating between multi-voxel patterns of brain activities associated with different mental states. However, when they are used in functional brain mapping, the location of discriminative voxels varies significantly, raising difficulties in interpreting the locus of the effect. Here we proposed a hierarchical framework of multivariate approach that maps informative clusters rather than voxels to achieve reliable functional brain mapping without compromising the discriminative power. In particular, we first searched for local homogeneous clusters that consisted of voxels with similar response profiles. Then, a multi-voxel classifier was built for each cluster to extract discriminative information from the multi-voxel patterns. Finally, through multivariate ranking, outputs from the classifiers were served as a multi-cluster pattern to identify informative clusters by examining interactions among clusters. Results from both simulated and real fMRI data demonstrated that this hierarchical approach showed better performance in the robustness of functional brain mapping than traditional voxel-based multivariate methods. In addition, the mapped clusters were highly overlapped for two perceptually equivalent object categories, further confirming the validity of our approach. In short, the hierarchical framework of multivariate approach is suitable for both pattern classification and brain mapping in fMRI studies. PMID:21152081

Relation between brain architecture and mathematical ability in children: a DBM study.

PubMed

Han, Zhaoying; Davis, Nicole; Fuchs, Lynn; Anderson, Adam W; Gore, John C; Dawant, Benoit M

2013-12-01

Population-based studies indicate that between 5 and 9 percent of US children exhibit significant deficits in mathematical reasoning, yet little is understood about the brain morphological features related to mathematical performances. In this work, deformation-based morphometry (DBM) analyses have been performed on magnetic resonance images of the brains of 79 third graders to investigate whether there is a correlation between brain morphological features and mathematical proficiency. Group comparison was also performed between Math Difficulties (MD-worst math performers) and Normal Controls (NC), where each subgroup consists of 20 age and gender matched subjects. DBM analysis is based on the analysis of the deformation fields generated by non-rigid registration algorithms, which warp the individual volumes to a common space. To evaluate the effect of registration algorithms on DBM results, five nonrigid registration algorithms have been used: (1) the Adaptive Bases Algorithm (ABA); (2) the Image Registration Toolkit (IRTK); (3) the FSL Nonlinear Image Registration Tool; (4) the Automatic Registration Tool (ART); and (5) the normalization algorithm available in SPM8. The deformation field magnitude (DFM) was used to measure the displacement at each voxel, and the Jacobian determinant (JAC) was used to quantify local volumetric changes. Results show there are no statistically significant volumetric differences between the NC and the MD groups using JAC. However, DBM analysis using DFM found statistically significant anatomical variations between the two groups around the left occipital-temporal cortex, left orbital-frontal cortex, and right insular cortex. Regions of agreement between at least two algorithms based on voxel-wise analysis were used to define Regions of Interest (ROIs) to perform an ROI-based correlation analysis on all 79 volumes. Correlations between average DFM values and standard mathematical scores over these regions were found to be significant. We also found that the choice of registration algorithm has an impact on DBM-based results, so we recommend using more than one algorithm when conducting DBM studies. To the best of our knowledge, this is the first study that uses DBM to investigate brain anatomical features related to mathematical performance in a relatively large population of children. © 2013.

SU-E-T-769: T-Test Based Prior Error Estimate and Stopping Criterion for Monte Carlo Dose Calculation in Proton Therapy

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hong, X; Gao, H; Schuemann, J

2015-06-15

Purpose: The Monte Carlo (MC) method is a gold standard for dose calculation in radiotherapy. However, it is not a priori clear how many particles need to be simulated to achieve a given dose accuracy. Prior error estimate and stopping criterion are not well established for MC. This work aims to fill this gap. Methods: Due to the statistical nature of MC, our approach is based on one-sample t-test. We design the prior error estimate method based on the t-test, and then use this t-test based error estimate for developing a simulation stopping criterion. The three major components are asmore » follows.First, the source particles are randomized in energy, space and angle, so that the dose deposition from a particle to the voxel is independent and identically distributed (i.i.d.).Second, a sample under consideration in the t-test is the mean value of dose deposition to the voxel by sufficiently large number of source particles. Then according to central limit theorem, the sample as the mean value of i.i.d. variables is normally distributed with the expectation equal to the true deposited dose.Third, the t-test is performed with the null hypothesis that the difference between sample expectation (the same as true deposited dose) and on-the-fly calculated mean sample dose from MC is larger than a given error threshold, in addition to which users have the freedom to specify confidence probability and region of interest in the t-test based stopping criterion. Results: The method is validated for proton dose calculation. The difference between the MC Result based on the t-test prior error estimate and the statistical Result by repeating numerous MC simulations is within 1%. Conclusion: The t-test based prior error estimate and stopping criterion are developed for MC and validated for proton dose calculation. Xiang Hong and Hao Gao were partially supported by the NSFC (#11405105), the 973 Program (#2015CB856000) and the Shanghai Pujiang Talent Program (#14PJ1404500)« less

SU-G-BRC-15: The Potential Clinical Significance of Dose Mapping Error for Intra- Fraction Dose Mapping for Lung Cancer Patients

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sayah, N; Weiss, E; Watkins, W

Purpose: To evaluate the dose-mapping error (DME) inherent to conventional dose-mapping algorithms as a function of dose-matrix resolution. Methods: As DME has been reported to be greatest where dose-gradients overlap tissue-density gradients, non-clinical 66 Gy IMRT plans were generated for 11 lung patients with the target edge defined as the maximum 3D density gradient on the 0% (end of inhale) breathing phase. Post-optimization, Beams were copied to 9 breathing phases. Monte Carlo dose computed (with 2*2*2 mm{sup 3} resolution) on all 10 breathing phases was deformably mapped to phase 0% using the Monte Carlo energy-transfer method with congruent mass-mapping (EMCM);more » an externally implemented tri-linear interpolation method with voxel sub-division; Pinnacle’s internal (tri-linear) method; and a post-processing energy-mass voxel-warping method (dTransform). All methods used the same base displacement-vector-field (or it’s pseudo-inverse as appropriate) for the dose mapping. Mapping was also performed at 4*4*4 mm{sup 3} by merging adjacent dose voxels. Results: Using EMCM as the reference standard, no clinically significant (>1 Gy) DMEs were found for the mean lung dose (MLD), lung V20Gy, or esophagus dose-volume indices, although MLD and V20Gy were statistically different (2*2*2 mm{sup 3}). Pinnacle-to-EMCM target D98% DMEs of 4.4 and 1.2 Gy were observed ( 2*2*2 mm{sup 3}). However dTransform, which like EMCM conserves integral dose, had DME >1 Gy for one case. The root mean square RMS of the DME for the tri-linear-to- EMCM methods was lower for the smaller voxel volume for the tumor 4D-D98%, lung V20Gy, and cord D1%. Conclusion: When tissue gradients overlap with dose gradients, organs-at-risk DME was statistically significant but not clinically significant. Target-D98%-DME was deemed clinically significant for 2/11 patients (2*2*2 mm{sup 3}). Since tri-linear RMS-DME between EMCM and tri-linear was reduced at 2*2*2 mm{sup 3}, use of this resolution is recommended for dose mapping. Interpolative dose methods are sufficiently accurate for the majority of cases. J.V. Siebers receives funding support from Varian Medical Systems.« less

Diagnostic accuracy of different display types in detection of recurrent caries under restorations by using CBCT.

PubMed

Baltacıoĝlu, İsmail H; Eren, Hakan; Yavuz, Yasemin; Kamburoğlu, Kıvanç

To assess the in vitro diagnostic ability of CBCT images using seven different display types in the detection of recurrent caries. Our study comprised 128 extracted human premolar and molar teeth. 8 groups each containing 16 teeth were obtained as follows: (1) Black Class I (Occlusal) amalgam filling without caries; (2) Black Class I (Occlusal) composite filling without caries; (3) Black Class II (Proximal) amalgam filling without caries; (4) Black Class II (Proximal) composite filling without caries; (5) Black Class I (Occlusal) amalgam filling with caries; (6) Black Class I (Occlusal) composite filling with caries; (7) Black Class II (Proximal) amalgam filling with caries; and (8) Black Class II (Proximal) composite filling with caries. Teeth were imaged using 100 × 90 mm field of view at three different voxel sizes of a CBCT unit (Planmeca ProMax(®) 3D ProFace™; Planmeca, Helsinki, Finland). CBCT TIFF images were opened and viewed using custom-designed software for computers on different display types. Intra- and interobserver agreements were calculated. The highest area under the receiver operating characteristic curve (Az) values for each image type, observer, reading and restoration were compared using z-tests against Az = 0.5. The significance level was set at p = 0.05. We found poor and moderate agreements. In general, Az values were found when software and medical diagnostic monitor were utilized. For Observer 2, Az values were statistically significantly higher when software was used on medical monitor [p = 0.036, p = 0.015 and p = 0.002, for normal-resolution mode (0.200 mm(3) voxel size), high-resolution mode (0.150 mm(3) voxel size) and low-resolution mode (0.400 mm(3) voxel size), respectively]. No statistically significant differences were found among other display types for all modes (p > 0.05). In general, no difference was found among 3 different voxel sizes (p > 0.05). In general, higher Az values were obtained for composite restorations than for amalgam restorations for all observers. For Observer 1, Az values for composite restorations were statistically significantly higher than those of amalgam restorations for MacBook and iPhone (Apple Inc., Cupertino, CA) assessments (p = 0.002 and p = 0.048, respectively). Higher Az values were observed with medical monitors when used with dedicated software compared to other display types which performed similarly in the diagnosis of recurrent caries under restorations. In addition, observers performed better in detection of recurrent caries when assessing composite restorations than amalgams.

Diagnostic accuracy of different display types in detection of recurrent caries under restorations by using CBCT

PubMed Central

Baltacıoĝlu, İsmail H; Eren, Hakan; Yavuz, Yasemin

2016-01-01

Objectives: To assess the in vitro diagnostic ability of CBCT images using seven different display types in the detection of recurrent caries. Methods: Our study comprised 128 extracted human premolar and molar teeth. 8 groups each containing 16 teeth were obtained as follows: (1) Black Class I (Occlusal) amalgam filling without caries; (2) Black Class I (Occlusal) composite filling without caries; (3) Black Class II (Proximal) amalgam filling without caries; (4) Black Class II (Proximal) composite filling without caries; (5) Black Class I (Occlusal) amalgam filling with caries; (6) Black Class I (Occlusal) composite filling with caries; (7) Black Class II (Proximal) amalgam filling with caries; and (8) Black Class II (Proximal) composite filling with caries. Teeth were imaged using 100 × 90 mm field of view at three different voxel sizes of a CBCT unit (Planmeca ProMax® 3D ProFace™; Planmeca, Helsinki, Finland). CBCT TIFF images were opened and viewed using custom-designed software for computers on different display types. Intra- and interobserver agreements were calculated. The highest area under the receiver operating characteristic curve (Az) values for each image type, observer, reading and restoration were compared using z-tests against Az = 0.5. The significance level was set at p = 0.05. Results: We found poor and moderate agreements. In general, Az values were found when software and medical diagnostic monitor were utilized. For Observer 2, Az values were statistically significantly higher when software was used on medical monitor [p = 0.036, p = 0.015 and p = 0.002, for normal-resolution mode (0.200 mm3 voxel size), high-resolution mode (0.150 mm3 voxel size) and low-resolution mode (0.400 mm3 voxel size), respectively]. No statistically significant differences were found among other display types for all modes (p > 0.05). In general, no difference was found among 3 different voxel sizes (p > 0.05). In general, higher Az values were obtained for composite restorations than for amalgam restorations for all observers. For Observer 1, Az values for composite restorations were statistically significantly higher than those of amalgam restorations for MacBook and iPhone (Apple Inc., Cupertino, CA) assessments (p = 0.002 and p = 0.048, respectively). Conclusions: Higher Az values were observed with medical monitors when used with dedicated software compared to other display types which performed similarly in the diagnosis of recurrent caries under restorations. In addition, observers performed better in detection of recurrent caries when assessing composite restorations than amalgams. PMID:27319604

White matter structural connectivity is associated with sensorimotor function in stroke survivors☆

PubMed Central

Kalinosky, Benjamin T.; Schindler-Ivens, Sheila; Schmit, Brian D.

2013-01-01

Purpose Diffusion tensor imaging (DTI) provides functionally relevant information about white matter structure. Local anatomical connectivity information combined with fractional anisotropy (FA) and mean diffusivity (MD) may predict functional outcomes in stroke survivors. Imaging methods for predicting functional outcomes in stroke survivors are not well established. This work uses DTI to objectively assess the effects of a stroke lesion on white matter structure and sensorimotor function. Methods A voxel-based approach is introduced to assess a stroke lesion's global impact on motor function. Anatomical T1-weighted and diffusion tensor images of the brain were acquired for nineteen subjects (10 post-stroke and 9 age-matched controls). A manually selected volume of interest was used to alleviate the effects of stroke lesions on image registration. Images from all subjects were registered to the images of the control subject that was anatomically closest to Talairach space. Each subject's transformed image was uniformly seeded for DTI tractography. Each seed was inversely transformed into the individual subject space, where DTI tractography was conducted and then the results were transformed back to the reference space. A voxel-wise connectivity matrix was constructed from the fibers, which was then used to calculate the number of directly and indirectly connected neighbors of each voxel. A novel voxel-wise indirect structural connectivity (VISC) index was computed as the average number of direct connections to a voxel's indirect neighbors. Voxel-based analyses (VBA) were performed to compare VISC, FA, and MD for the detection of lesion-induced changes in sensorimotor function. For each voxel, a t-value was computed from the differences between each stroke brain and the 9 controls. A series of linear regressions was performed between Fugl-Meyer (FM) assessment scores of sensorimotor impairment and each DTI metric's log number of voxels that differed from the control group. Results Correlation between the logarithm of the number of significant voxels in the ipsilesional hemisphere and total Fugl-Meyer score was moderate for MD (R2 = 0.512), and greater for VISC (R2 = 0.796) and FA (R2 = 0.674). The slopes of FA (p = 0.0036), VISC (p = 0.0005), and MD (p = 0.0199) versus the total FM score were significant. However, these correlations were driven by the upper extremity motor component of the FM score (VISC: R2 = 0.879) with little influence of the lower extremity motor component (FA: R2 = 0.177). Conclusion The results suggest that a voxel-wise metric based on DTI tractography can predict upper extremity sensorimotor function of stroke survivors, and that supraspinal intraconnectivity may have a less dominant role in lower extremity function. PMID:24179827

An energy-based equilibrium contact angle boundary condition on jagged surfaces for phase-field methods.

PubMed

Frank, Florian; Liu, Chen; Scanziani, Alessio; Alpak, Faruk O; Riviere, Beatrice

2018-08-01

We consider an energy-based boundary condition to impose an equilibrium wetting angle for the Cahn-Hilliard-Navier-Stokes phase-field model on voxel-set-type computational domains. These domains typically stem from μCT (micro computed tomography) imaging of porous rock and approximate a (on μm scale) smooth domain with a certain resolution. Planar surfaces that are perpendicular to the main axes are naturally approximated by a layer of voxels. However, planar surfaces in any other directions and curved surfaces yield a jagged/topologically rough surface approximation by voxels. For the standard Cahn-Hilliard formulation, where the contact angle between the diffuse interface and the domain boundary (fluid-solid interface/wall) is 90°, jagged surfaces have no impact on the contact angle. However, a prescribed contact angle smaller or larger than 90° on jagged voxel surfaces is amplified. As a remedy, we propose the introduction of surface energy correction factors for each fluid-solid voxel face that counterbalance the difference of the voxel-set surface area with the underlying smooth one. The discretization of the model equations is performed with the discontinuous Galerkin method. However, the presented semi-analytical approach of correcting the surface energy is equally applicable to other direct numerical methods such as finite elements, finite volumes, or finite differences, since the correction factors appear in the strong formulation of the model. Copyright © 2018 Elsevier Inc. All rights reserved.

NOTE: MCDE: a new Monte Carlo dose engine for IMRT

NASA Astrophysics Data System (ADS)

Reynaert, N.; DeSmedt, B.; Coghe, M.; Paelinck, L.; Van Duyse, B.; DeGersem, W.; DeWagter, C.; DeNeve, W.; Thierens, H.

2004-07-01

A new accurate Monte Carlo code for IMRT dose computations, MCDE (Monte Carlo dose engine), is introduced. MCDE is based on BEAMnrc/DOSXYZnrc and consequently the accurate EGSnrc electron transport. DOSXYZnrc is reprogrammed as a component module for BEAMnrc. In this way both codes are interconnected elegantly, while maintaining the BEAM structure and only minimal changes to BEAMnrc.mortran are necessary. The treatment head of the Elekta SLiplus linear accelerator is modelled in detail. CT grids consisting of up to 200 slices of 512 × 512 voxels can be introduced and up to 100 beams can be handled simultaneously. The beams and CT data are imported from the treatment planning system GRATIS via a DICOM interface. To enable the handling of up to 50 × 106 voxels the system was programmed in Fortran95 to enable dynamic memory management. All region-dependent arrays (dose, statistics, transport arrays) were redefined. A scoring grid was introduced and superimposed on the geometry grid, to be able to limit the number of scoring voxels. The whole system uses approximately 200 MB of RAM and runs on a PC cluster consisting of 38 1.0 GHz processors. A set of in-house made scripts handle the parallellization and the centralization of the Monte Carlo calculations on a server. As an illustration of MCDE, a clinical example is discussed and compared with collapsed cone convolution calculations. At present, the system is still rather slow and is intended to be a tool for reliable verification of IMRT treatment planning in the case of the presence of tissue inhomogeneities such as air cavities.

Effects of voxelization on dose volume histogram accuracy

NASA Astrophysics Data System (ADS)

Sunderland, Kyle; Pinter, Csaba; Lasso, Andras; Fichtinger, Gabor

2016-03-01

PURPOSE: In radiotherapy treatment planning systems, structures of interest such as targets and organs at risk are stored as 2D contours on evenly spaced planes. In order to be used in various algorithms, contours must be converted into binary labelmap volumes using voxelization. The voxelization process results in lost information, which has little effect on the volume of large structures, but has significant impact on small structures, which contain few voxels. Volume differences for segmented structures affects metrics such as dose volume histograms (DVH), which are used for treatment planning. Our goal is to evaluate the impact of voxelization on segmented structures, as well as how factors like voxel size affects metrics, such as DVH. METHODS: We create a series of implicit functions, which represent simulated structures. These structures are sampled at varying resolutions, and compared to labelmaps with high sub-millimeter resolutions. We generate DVH and evaluate voxelization error for the same structures at different resolutions by calculating the agreement acceptance percentage between the DVH. RESULTS: We implemented tools for analysis as modules in the SlicerRT toolkit based on the 3D Slicer platform. We found that there were large DVH variation from the baseline for small structures or for structures located in regions with a high dose gradient, potentially leading to the creation of suboptimal treatment plans. CONCLUSION: This work demonstrates that labelmap and dose volume voxel size is an important factor in DVH accuracy, which must be accounted for in order to ensure the development of accurate treatment plans.

Activity of left inferior frontal gyrus related to word repetition effects: LORETA imaging with 128-channel EEG and individual MRI.

PubMed

Kim, Young Youn; Lee, Boreom; Shin, Yong Wook; Kwon, Jun Soo; Kim, Myung-Sun

2006-02-01

We investigated the brain substrate of word repetition effects on the implicit memory task using low-resolution electromagnetic tomography (LORETA) with high-density 128-channel EEG and individual MRI as a realistic head model. Thirteen right-handed, healthy subjects performed a word/non-word discrimination task, in which the words and non-words were presented visually, and some of the words appeared twice with a lag of one or five items. All of the subjects exhibited word repetition effects with respect to the behavioral data, in which a faster reaction time was observed to the repeated word (old word) than to the first presentation of the word (new word). The old words elicited more positive-going potentials than the new words, beginning at 200 ms and lasting until 500 ms post-stimulus. We conducted source reconstruction using LORETA at a latency of 400 ms with the peak mean global field potentials and used statistical parametric mapping for the statistical analysis. We found that the source elicited by the old words exhibited a statistically significant current density reduction in the left inferior frontal gyrus. This is the first study to investigate the generators of word repetition effects using voxel-by-voxel statistical mapping of the current density with individual MRI and high-density EEG.

TH-CD-202-10: Tumor Metabolic Control Probability & Dose Response Mapping for Adaptive Dose Painting by Number

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chen, S; Wilson, G; Krauss, D

Purpose: Adaptive dose-painting-by-number (DPbN) requires a dose-response-mapping (DRM) obtained early in the treatment course. To obtain DRM, voxel-by-voxel tumor dose response needs to be quantified. Our recent study has demonstrated that voxel-by-voxel radio-sensitivity of patient tumor can be determined using tumor-metabolic-ratio measured early during the treatment using FDG-PET images. In this study, the measurements were utilized to construct tumor metabolic control probability (TMCP) and DRM for DPbN. Methods: FDG-PET/CT images of 18 HN cancer patients obtained pre- and weekly during the treatment were used. Spatial parametric images of tumor-metabolic-ratio (dSUV) were constructed following voxel-by-voxel deformable image registration. Each voxel valuemore » in dSUV was a function of baseline SUV and delivered dose. Utilizing all values of dSUV in the controlled tumor group at the last treatment week, a cut-off function between the baseline SUV and dSUV was formed, and applied in early treatment days on dSUV of all tumors to model the TMCP. At the treatment week k, TMCP was constructed with respect to the tumor voxel dSUV appeared at the week using the maximum likelihood estimation for all dose levels, and used for DRM construction. Results: TMCPs estimated in the week 2 & 3 have D{sub 50}=11.1∼47.6Gy; γ{sub 50}=0.55∼0.92 respectively with respect to dSUV=0.3∼1.2. The corresponding DRM between tumor voxel dSUV and the expected treatment dose has sigmoid shape. The expected treatment dose are 26∼40Gy (for 95% TMCP) for high sensitive tumor voxels with dSUV=0.3∼0.5; and 65∼110Gy for low sensitive tumor voxels with the dSUV>1.0 depending on the time of the estimation. Conclusion: TMCP can be constructed voxel-by-voxel in human tumor using multiple FDG-PET imaging obtained in early treatment days. TMCP provides a potential quantitative objective of tumor DRM for DPbN to plan the best dose, escalate or de-escalate, in tumor adaptively based on its own radio-sensitivity.« less

Identifying Voxels at Risk for Progression in Glioblastoma Based on Dosimetry, Physiologic and Metabolic MRI.

PubMed

Anwar, Mekhail; Molinaro, Annette M; Morin, Olivier; Chang, Susan M; Haas-Kogan, Daphne A; Nelson, Sarah J; Lupo, Janine M

2017-09-01

Despite the longstanding role of radiation in cancer treatment and the presence of advanced, high-resolution imaging techniques, delineation of voxels at-risk for progression remains purely a geometric expansion of anatomic images, missing subclinical disease at risk for recurrence while treating potentially uninvolved tissue and increasing toxicity. This remains despite the modern ability to precisely shape radiation fields. A striking example of this is the treatment of glioblastoma, a highly infiltrative tumor that may benefit from accurate identification of subclinical disease. In this study, we hypothesize that parameters from physiologic and metabolic magnetic resonance imaging (MRI) at diagnosis could predict the likelihood of voxel progression at radiographic recurrence in glioblastoma by identifying voxel characteristics that indicate subclinical disease. Integrating dosimetry can reveal its effect on voxel outcome, enabling risk-adapted voxel dosing. As a system example, 24 patients with glioblastoma treated with radiotherapy, temozolomide and an anti-angiogenic agent were analyzed. Pretreatment median apparent diffusion coefficient (ADC), fractional anisotropy (FA), relative cerebral blood volume (rCBV), vessel leakage (percentage recovery), choline-to-NAA index (CNI) and dose of voxels in the T2 nonenhancing lesion (NEL), T1 post-contrast enhancing lesion (CEL) or normal-appearing volume (NAV) of brain, were calculated for voxels that progressed [NAV→NEL, CEL (N = 8,765)] and compared against those that remained stable [NAV→NAV (N = 98,665)]. Voxels that progressed (NAV→NEL) had significantly different (P < 0.01) ADC (860), FA (0.36) and CNI (0.67) versus stable voxels (804, 0.43 and 0.05, respectively), indicating increased cell turnover, edema and decreased directionality, consistent with subclinical disease. NAV→CEL voxels were more abnormal (1,014, 0.28, 2.67, respectively) and leakier (percentage recovery = 70). A predictive model identified areas of recurrence, demonstrating that elevated CNI potentiates abnormal diffusion, even far (>2 cm) from the tumor and dose escalation >45 Gy has diminishing benefits. Integrating advanced MRI with dosimetry can identify at voxels at risk for progression and may allow voxel-level risk-adapted dose escalation to subclinical disease while sparing normal tissue. When combined with modern planning software, this technique may enable risk-adapted radiotherapy in any disease site with multimodal imaging.

Effects of Orientation and Anisometry of Magnetic Resonance Imaging Acquisitions on Diffusion Tensor Imaging and Structural Connectomes.

PubMed

Tudela, Raúl; Muñoz-Moreno, Emma; López-Gil, Xavier; Soria, Guadalupe

2017-01-01

Diffusion-weighted imaging (DWI) quantifies water molecule diffusion within tissues and is becoming an increasingly used technique. However, it is very challenging as correct quantification depends on many different factors, ranging from acquisition parameters to a long pipeline of image processing. In this work, we investigated the influence of voxel geometry on diffusion analysis, comparing different acquisition orientations as well as isometric and anisometric voxels. Diffusion-weighted images of one rat brain were acquired with four different voxel geometries (one isometric and three anisometric in different directions) and three different encoding orientations (coronal, axial and sagittal). Diffusion tensor scalar measurements, tractography and the brain structural connectome were analyzed for each of the 12 acquisitions. The acquisition direction with respect to the main magnetic field orientation affected the diffusion results. When the acquisition slice-encoding direction was not aligned with the main magnetic field, there were more artifacts and a lower signal-to-noise ratio that led to less anisotropic tensors (lower fractional anisotropic values), producing poorer quality results. The use of anisometric voxels generated statistically significant differences in the values of diffusion metrics in specific regions. It also elicited differences in tract reconstruction and in different graph metric values describing the brain networks. Our results highlight the importance of taking into account the geometric aspects of acquisitions, especially when comparing diffusion data acquired using different geometries.

Three-dimensional localization of abnormal EEG activity in migraine: a low resolution electromagnetic tomography (LORETA) study of migraine patients in the pain-free interval.

PubMed

Clemens, Béla; Bánk, József; Piros, Pálma; Bessenyei, Mónika; Veto, Sára; Tóth, Márton; Kondákor, István

2008-09-01

Investigating the brain of migraine patients in the pain-free interval may shed light on the basic cerebral abnormality of migraine, in other words, the liability of the brain to generate migraine attacks from time to time. Twenty unmedicated "migraine without aura" patients and a matched group of healthy controls were investigated in this explorative study. 19-channel EEG was recorded against the linked ears reference and was on-line digitized. 60 x 2-s epochs of eyes-closed, waking-relaxed activity were subjected to spectral analysis and a source localization method, low resolution electromagnetic tomography (LORETA). Absolute power was computed for 19 electrodes and four frequency bands (delta: 1.5-3.5 Hz, theta: 4.0-7.5 Hz, alpha: 8.0-12.5 Hz, beta: 13.0-25.0 Hz). LORETA "activity" (=current source density, ampers/meters squared) was computed for 2394 voxels and the above specified frequency bands. Group comparison was carried out for the specified quantitative EEG variables. Activity in the two groups was compared on a voxel-by-voxel basis for each frequency band. Statistically significant (uncorrected P < 0.01) group differences were projected to cortical anatomy. Spectral findings: there was a tendency for more alpha power in the migraine that in the control group in all but two (F4, C3) derivations. However, statistically significant (P < 0.01, Bonferroni-corrected) spectral difference was only found in the right occipital region. The main LORETA-finding was that voxels with P < 0.01 differences were crowded in anatomically contiguous cortical areas. Increased alpha activity was found in a cortical area including part of the precuneus, and the posterior part of the middle temporal gyrus in the right hemisphere. Decreased alpha activity was found bilaterally in medial parts of the frontal cortex including the anterior cingulate and the superior and medial frontal gyri. Neither spectral analysis, nor LORETA revealed statistically significant differences in the delta, theta, and beta bands. LORETA revealed the anatomical distribution of the cortical sources (generators) of the EEG abnormalities in migraine. The findings characterize the state of the cerebral cortex in the pain-free interval and might be suitable for planning forthcoming investigations.

Comparison of NMR simulations of porous media derived from analytical and voxelized representations.

PubMed

Jin, Guodong; Torres-Verdín, Carlos; Toumelin, Emmanuel

2009-10-01

We develop and compare two formulations of the random-walk method, grain-based and voxel-based, to simulate the nuclear-magnetic-resonance (NMR) response of fluids contained in various models of porous media. The grain-based approach uses a spherical grain pack as input, where the solid surface is analytically defined without an approximation. In the voxel-based approach, the input is a computer-tomography or computer-generated image of reconstructed porous media. Implementation of the two approaches is largely the same, except for the representation of porous media. For comparison, both approaches are applied to various analytical and digitized models of porous media: isolated spherical pore, simple cubic packing of spheres, and random packings of monodisperse and polydisperse spheres. We find that spin magnetization decays much faster in the digitized models than in their analytical counterparts. The difference in decay rate relates to the overestimation of surface area due to the discretization of the sample; it cannot be eliminated even if the voxel size decreases. However, once considering the effect of surface-area increase in the simulation of surface relaxation, good quantitative agreement is found between the two approaches. Different grain or pore shapes entail different rates of increase of surface area, whereupon we emphasize that the value of the "surface-area-corrected" coefficient may not be universal. Using an example of X-ray-CT image of Fontainebleau rock sample, we show that voxel size has a significant effect on the calculated surface area and, therefore, on the numerically simulated magnetization response.

Sparse and Adaptive Diffusion Dictionary (SADD) for recovering intra-voxel white matter structure.

PubMed

Aranda, Ramon; Ramirez-Manzanares, Alonso; Rivera, Mariano

2015-12-01

On the analysis of the Diffusion-Weighted Magnetic Resonance Images, multi-compartment models overcome the limitations of the well-known Diffusion Tensor model for fitting in vivo brain axonal orientations at voxels with fiber crossings, branching, kissing or bifurcations. Some successful multi-compartment methods are based on diffusion dictionaries. The diffusion dictionary-based methods assume that the observed Magnetic Resonance signal at each voxel is a linear combination of the fixed dictionary elements (dictionary atoms). The atoms are fixed along different orientations and diffusivity profiles. In this work, we present a sparse and adaptive diffusion dictionary method based on the Diffusion Basis Functions Model to estimate in vivo brain axonal fiber populations. Our proposal overcomes the following limitations of the diffusion dictionary-based methods: the limited angular resolution and the fixed shapes for the atom set. We propose to iteratively re-estimate the orientations and the diffusivity profile of the atoms independently at each voxel by using a simplified and easier-to-solve mathematical approach. As a result, we improve the fitting of the Diffusion-Weighted Magnetic Resonance signal. The advantages with respect to the former Diffusion Basis Functions method are demonstrated on the synthetic data-set used on the 2012 HARDI Reconstruction Challenge and in vivo human data. We demonstrate that improvements obtained in the intra-voxel fiber structure estimations benefit brain research allowing to obtain better tractography estimations. Hence, these improvements result in an accurate computation of the brain connectivity patterns. Copyright © 2015 Elsevier B.V. All rights reserved.

Whole-remnant and maximum-voxel SPECT/CT dosimetry in {sup 131}I-NaI treatments of differentiated thyroid cancer

DOE Office of Scientific and Technical Information (OSTI.GOV)

Mínguez, Pablo, E-mail: pablo.minguezgabina@osakid

Purpose: To investigate the possible differences between SPECT/CT based whole-remnant and maximum-voxel dosimetry in patients receiving radio-iodine ablation treatment of differentiated thyroid cancer (DTC). Methods: Eighteen DTC patients were administered 1.11 GBq of {sup 131}I-NaI after near-total thyroidectomy and rhTSH stimulation. Two patients had two remnants, so in total dosimetry was performed for 20 sites. Three SPECT/CT scans were performed for each patient at 1, 2, and 3–7 days after administration. The activity, the remnant mass, and the maximum-voxel activity were determined from these images and from a recovery-coefficient curve derived from experimental phantom measurements. The cumulated activity was estimatedmore » using trapezoidal-exponential integration. Finally, the absorbed dose was calculated using S-values for unit-density spheres in whole-remnant dosimetry and S-values for voxels in maximum-voxel dosimetry. Results: The mean absorbed dose obtained from whole-remnant dosimetry was 40 Gy (range 2–176 Gy) and from maximum-voxel dosimetry 34 Gy (range 2–145 Gy). For any given patient, the activity concentrations for each of the three time-points were approximately the same for the two methods. The effective half-lives varied (R = 0.865), mainly due to discrepancies in estimation of the longer effective half-lives. On average, absorbed doses obtained from whole-remnant dosimetry were 1.2 ± 0.2 (1 SD) higher than for maximum-voxel dosimetry, mainly due to differences in the S-values. The method-related differences were however small in comparison to the wide range of absorbed doses obtained in patients. Conclusions: Simple and consistent procedures for SPECT/CT based whole-volume and maximum-voxel dosimetry have been described, both based on experimentally determined recovery coefficients. Generally the results from the two approaches are consistent, although there is a small, systematic difference in the absorbed dose due to differences in the S-values, and some variability due to differences in the estimated effective half-lives, especially when the effective half-life is long. Irrespective of the method used, the patient absorbed doses obtained span over two orders of magnitude.« less

Visualising inter-subject variability in fMRI using threshold-weighted overlap maps

NASA Astrophysics Data System (ADS)

Seghier, Mohamed L.; Price, Cathy J.

2016-02-01

Functional neuroimaging studies are revealing the neural systems sustaining many sensory, motor and cognitive abilities. A proper understanding of these systems requires an appreciation of the degree to which they vary across subjects. Some sources of inter-subject variability might be easy to measure (demographics, behavioural scores, or experimental factors), while others are more difficult (cognitive strategies, learning effects, and other hidden sources). Here, we introduce a simple way of visualising whole-brain consistency and variability in brain responses across subjects using threshold-weighted voxel-based overlap maps. The output quantifies the proportion of subjects activating a particular voxel or region over a wide range of statistical thresholds. The sensitivity of our approach was assessed in 30 healthy adults performing a matching task with their dominant hand. We show how overlap maps revealed many effects that were only present in a subsample of our group; we discuss how overlap maps can provide information that may be missed or misrepresented by standard group analysis, and how this information can help users to understand their data. In particular, we emphasize that functional overlap maps can be particularly useful when it comes to explaining typical (or atypical) compensatory mechanisms used by patients following brain damage.

Comparison of accelerated 3-D spiral chemical shift imaging and single-voxel spectroscopy at 3T in the pediatric age group.

PubMed

Yazbek, Sandrine; Prabhu, Sanjay P; Connaughton, Pauline; Grant, Patricia E; Gagoski, Borjan

2015-08-01

Single-voxel spectroscopy (SVS) is usually used in the pediatric population when a short acquisition time is crucial. To overcome the long acquisition time of 3-D phase-encoded chemical shift imaging (CSI) and lack of spatial coverage of single-voxel spectroscopy, efficient encoding schemes using spiral k-space trajectories have been successfully deployed, enabling acquisition of volumetric CSI in <5 min. We assessed feasibility of using 3-D spiral CSI sequence routinely in pediatric clinical settings by comparing its reconstructed spectra against SVS spectra. Volumetric spiral CSI obtained spectra from 2-cc isotropic voxels over a 16×16×10-cm region. SVS acquisition encoded a 3.4-cc (1.5-mm) isotropic voxel. Acquisition time was 3 min for every technique. Data were gathered prospectively from 11 random pediatric patients. Spectra from left basal ganglia were obtained using both techniques and were processed with post-processing software. The following metabolite ratios were calculated: N-acetylaspartate/creatine (NAA/Cr), choline/creatine (Cho/Cr), lactate/creatine (Lac/Cr) and N-acetylapartate/choline (NAA/Cho). We collected data on 11 children ages 4 days to 10 years. In 10/11 cases, spectral quality of both methods was acceptable. Considering 10/11 cases, we found a statistically significant difference between SVS and 3-D spiral CSI for all three ratios. However, this difference was fixed and was probably caused by a fixed bias. This means that 3-D spiral CSI can be used instead of SVS by removing the mean difference between the methods for each ratio. Accelerated 3-D CSI is feasible in pediatric patients and can potentially substitute for SVS.

Terrestrial laser scanning for biomass assessment and tree reconstruction: improved processing efficiency

NASA Astrophysics Data System (ADS)

Alboabidallah, Ahmed; Martin, John; Lavender, Samantha; Abbott, Victor

2017-09-01

Terrestrial Laser Scanning (TLS) processing for biomass mapping involves large data volumes, and often includes relatively slow 3D object fitting steps that increase the processing time. This study aimed to test new features that can speed up the overall processing time. A new type of 3D voxel is used, where the horizontal layers are parallel to the Digital Terrain Model. This voxel type allows procedures to extract tree diameters using just one layer, but still gives direct tree-height estimations. Layer intersection is used to emphasize the trunks as upright standing objects, which are detected in the spatially segmented intersection of the breast-height voxels and then extended upwards and downwards. The diameters were calculated by fitting elliptical cylinders to the laser points in the detected trunk segments. Non-trunk segments, used in sub-tree- structures, were found using the parent-child relationships between successive layers. The branches were reconstructed by skeletonizing each sub-tree branch, and the biomass was distributed statistically amongst the weighted skeletons. The procedure was applied to nine plots within the UK. The average correlation coefficients between reconstructed and directly measured tree diameters, heights and branches were R2 = 0.92, 0.97 and 0.59 compared to 0.91, 0.95, and 0.63 when cylindrical fitting was used. The average time to apply the method reduced from 5hrs:18mins per plot, for the conventional methods, to 2hrs:24mins when the same hardware and software libraries were used with the 3D voxels. These results indicate that this 3D voxel method can produce, much more quickly, results of a similar accuracy that would improve efficiency if applied to projects with large volume TLS datasets.

Assessment of abnormal brain structures and networks in major depressive disorder using morphometric and connectome analyses.

PubMed

Chen, Vincent Chin-Hung; Shen, Chao-Yu; Liang, Sophie Hsin-Yi; Li, Zhen-Hui; Tyan, Yeu-Sheng; Liao, Yin-To; Huang, Yin-Chen; Lee, Yena; McIntyre, Roger S; Weng, Jun-Cheng

2016-11-15

It is hypothesized that the phenomenology of major depressive disorder (MDD) is subserved by disturbances in the structure and function of brain circuits; however, findings of structural abnormalities using MRI have been inconsistent. Generalized q-sampling imaging (GQI) methodology provides an opportunity to assess the functional integrity of white matter tracts in implicated circuits. The study population was comprised of 16 outpatients with MDD (mean age 44.81±2.2 years) and 30 age- and gender-matched healthy controls (mean age 45.03±1.88 years). We excluded participants with any other primary mental disorder, substance use disorder, or any neurological illnesses. We used T1-weighted 3D MRI with voxel-based morphometry (VBM) and vertex-wise shape analysis, and GQI with voxel-based statistical analysis (VBA), graph theoretical analysis (GTA) and network-based statistical (NBS) analysis to evaluate brain structure and connectivity abnormalities in MDD compared to healthy controls correlates with clinical measures of depressive symptom severity, Hamilton Depression Rating Scale 17-item (HAMD) and Hospital Anxiety and Depression Scale (HADS). Using VBM and vertex-wise shape analyses, we found significant volumetric decreases in the hippocampus and amygdala among subjects with MDD (p<0.001). Using GQI, we found decreases in diffusion anisotropy in the superior longitudinal fasciculus and increases in diffusion probability distribution in the frontal lobe among subjects with MDD (p<0.01). In GTA and NBS analyses, we found several disruptions in connectivity among subjects with MDD, particularly in the frontal lobes (p<0.05). In addition, structural alterations were correlated with depressive symptom severity (p<0.01). Small sample size; the cross-sectional design did not allow us to observe treatment effects in the MDD participants. Our results provide further evidence indicating that MDD may be conceptualized as a brain disorder with abnormal circuit structure and connectivity. Copyright © 2016 Elsevier B.V. All rights reserved.

An automated voxelized dosimetry tool for radionuclide therapy based on serial quantitative SPECT/CT imaging

DOE Office of Scientific and Technical Information (OSTI.GOV)

Jackson, Price A.; Kron, Tomas; Beauregard, Jean-Mathieu

2013-11-15

Purpose: To create an accurate map of the distribution of radiation dose deposition in healthy and target tissues during radionuclide therapy.Methods: Serial quantitative SPECT/CT images were acquired at 4, 24, and 72 h for 28 {sup 177}Lu-octreotate peptide receptor radionuclide therapy (PRRT) administrations in 17 patients with advanced neuroendocrine tumors. Deformable image registration was combined with an in-house programming algorithm to interpolate pharmacokinetic uptake and clearance at a voxel level. The resultant cumulated activity image series are comprised of values representing the total number of decays within each voxel's volume. For PRRT, cumulated activity was translated to absorbed dose basedmore » on Monte Carlo-determined voxel S-values at a combination of long and short ranges. These dosimetric image sets were compared for mean radiation absorbed dose to at-risk organs using a conventional MIRD protocol (OLINDA 1.1).Results: Absorbed dose values to solid organs (liver, kidneys, and spleen) were within 10% using both techniques. Dose estimates to marrow were greater using the voxelized protocol, attributed to the software incorporating crossfire effect from nearby tumor volumes.Conclusions: The technique presented offers an efficient, automated tool for PRRT dosimetry based on serial post-therapy imaging. Following retrospective analysis, this method of high-resolution dosimetry may allow physicians to prescribe activity based on required dose to tumor volume or radiation limits to healthy tissue in individual patients.« less

Summary Statistics for Fun Dough Data Acquired at LLNL

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kallman, J S; Morales, K E; Whipple, R E

Using x-ray computerized tomography (CT), we have characterized the x-ray linear attenuation coefficients (LAC) of a Play Dough{trademark}-like product, Fun Dough{trademark}, designated as PD. Table 1 gives the first-order statistics for each of four CT measurements, estimated with a Gaussian kernel density estimator (KDE) analysis. The mean values of the LAC range from a high of about 2100 LMHU{sub D} at 100kVp to a low of about 1100 LMHU{sub D} at 300kVp. The standard deviation of each measurement is around 1% of the mean. The entropy covers the range from 3.9 to 4.6. Ordinarily, we would model the LAC ofmore » the material and compare the modeled values to the measured values. In this case, however, we did not have the composition of the material and therefore did not model the LAC. Using a method recently proposed by Lawrence Livermore National Laboratory (LLNL), we estimate the value of the effective atomic number, Z{sub eff}, to be near 8.5. LLNL prepared about 50mL of the Fun Dough{trademark} in a polypropylene vial and firmly compressed it immediately prior to the x-ray measurements. Still, layers can plainly be seen in the reconstructed images, indicating that the bulk density of the material in the container is affected by voids and bubbles. We used the computer program IMGREC to reconstruct the CT images. The values of the key parameters used in the data capture and image reconstruction are given in this report. Additional details may be found in the experimental SOP and a separate document. To characterize the statistical distribution of LAC values in each CT image, we first isolated an 80% central-core segment of volume elements ('voxels') lying completely within the specimen, away from the walls of the polypropylene vial. All of the voxels within this central core, including those comprised of voids and inclusions, are included in the statistics. We then calculated the mean value, standard deviation and entropy for (a) the four image segments and for (b) their digital gradient images. (A digital gradient image of a given image was obtained by taking the absolute value of the difference between the initial image and that same image offset by one voxel horizontally, parallel to the rows of the x-ray detector array.) The statistics of the initial image of LAC values are called 'first order statistics;' those of the gradient image, 'second order statistics.'« less

Detection of Prostate Cancer: Quantitative Multiparametric MR Imaging Models Developed Using Registered Correlative Histopathology.

PubMed

Metzger, Gregory J; Kalavagunta, Chaitanya; Spilseth, Benjamin; Bolan, Patrick J; Li, Xiufeng; Hutter, Diane; Nam, Jung W; Johnson, Andrew D; Henriksen, Jonathan C; Moench, Laura; Konety, Badrinath; Warlick, Christopher A; Schmechel, Stephen C; Koopmeiners, Joseph S

2016-06-01

Purpose To develop multiparametric magnetic resonance (MR) imaging models to generate a quantitative, user-independent, voxel-wise composite biomarker score (CBS) for detection of prostate cancer by using coregistered correlative histopathologic results, and to compare performance of CBS-based detection with that of single quantitative MR imaging parameters. Materials and Methods Institutional review board approval and informed consent were obtained. Patients with a diagnosis of prostate cancer underwent multiparametric MR imaging before surgery for treatment. All MR imaging voxels in the prostate were classified as cancer or noncancer on the basis of coregistered histopathologic data. Predictive models were developed by using more than one quantitative MR imaging parameter to generate CBS maps. Model development and evaluation of quantitative MR imaging parameters and CBS were performed separately for the peripheral zone and the whole gland. Model accuracy was evaluated by using the area under the receiver operating characteristic curve (AUC), and confidence intervals were calculated with the bootstrap procedure. The improvement in classification accuracy was evaluated by comparing the AUC for the multiparametric model and the single best-performing quantitative MR imaging parameter at the individual level and in aggregate. Results Quantitative T2, apparent diffusion coefficient (ADC), volume transfer constant (K(trans)), reflux rate constant (kep), and area under the gadolinium concentration curve at 90 seconds (AUGC90) were significantly different between cancer and noncancer voxels (P < .001), with ADC showing the best accuracy (peripheral zone AUC, 0.82; whole gland AUC, 0.74). Four-parameter models demonstrated the best performance in both the peripheral zone (AUC, 0.85; P = .010 vs ADC alone) and whole gland (AUC, 0.77; P = .043 vs ADC alone). Individual-level analysis showed statistically significant improvement in AUC in 82% (23 of 28) and 71% (24 of 34) of patients with peripheral-zone and whole-gland models, respectively, compared with ADC alone. Model-based CBS maps for cancer detection showed improved visualization of cancer location and extent. Conclusion Quantitative multiparametric MR imaging models developed by using coregistered correlative histopathologic data yielded a voxel-wise CBS that outperformed single quantitative MR imaging parameters for detection of prostate cancer, especially when the models were assessed at the individual level. (©) RSNA, 2016 Online supplemental material is available for this article.

Impact of Apolipoprotein E4 Polymorphism on the Gray Matter Volume and the White Matter Integrity in Subjective Memory Impairment without White Matter Hyperintensities: Voxel-Based Morphometry and Tract-Based Spatial Statistics Study under 3-Tesla MRI.

PubMed

Lee, Young-Min; Ha, Ji-Kyung; Park, Je-Min; Lee, Byung-Dae; Moon, EunSoo; Chung, Young-In; Kim, Ji-Hoon; Kim, Hak-Jin; Mun, Chi-Woong; Kim, Tae-Hyung; Kim, Young-Hoon

2016-01-01

The aim of this study is to compare gray matter (GM) volume and white matter (WM) integrity in Apolipoprotein E4 (ApoE ε4) carriers with that of ApoE ε4 noncarriers using the voxel-based morphometry and diffusion tensor imaging (DTI) to investigate the effect of the ApoE ε4 on brain structures in subjective memory impairment (SMI) without white matter hyperintensities (WMH). Altogether, 26 participants with SMI without WMH were finally recruited from the Memory impairment clinics of Pusan National University Hospital in Korea. All participants were ApoE genotyped (ApoE ε4 carriers: n = 13, matched ApoE ε4 noncarriers: n = 13) and underwent 3-tesla magnetic resonance imaging (MRI) including 3-dimensional volumetric images for GM volume and DTI for WM integrity. ApoE ε4 carriers compared with noncarriers in SMI without WMH showed the atrophy of GM in inferior temporal gyrus, inferior parietal lobule, anterior cingulum, middle frontal gyrus, and precentral gyrus and significantly lower fractional anisotropy WM values in the splenium of corpus callosum and anterior corona radiate. Our findings suggest that the ApoE ε4 is associated with both atrophy of GM volume and disruption of WM integrity in SMI without WMH. Copyright © 2015 by the American Society of Neuroimaging.

Is there more valuable information in PWI datasets for a voxel-wise acute ischemic stroke tissue outcome prediction than what is represented by typical perfusion maps?

NASA Astrophysics Data System (ADS)

Forkert, Nils Daniel; Siemonsen, Susanne; Dalski, Michael; Verleger, Tobias; Kemmling, Andre; Fiehler, Jens

2014-03-01

The acute ischemic stroke is a leading cause for death and disability in the industry nations. In case of a present acute ischemic stroke, the prediction of the future tissue outcome is of high interest for the clinicians as it can be used to support therapy decision making. Within this context, it has already been shown that the voxel-wise multi-parametric tissue outcome prediction leads to more promising results compared to single channel perfusion map thresholding. Most previously published multi-parametric predictions employ information from perfusion maps derived from perfusion-weighted MRI together with other image sequences such as diffusion-weighted MRI. However, it remains unclear if the typically calculated perfusion maps used for this purpose really include all valuable information from the PWI dataset for an optimal tissue outcome prediction. To investigate this problem in more detail, two different methods to predict tissue outcome using a k-nearest-neighbor approach were developed in this work and evaluated based on 18 datasets of acute stroke patients with known tissue outcome. The first method integrates apparent diffusion coefficient and perfusion parameter (Tmax, MTT, CBV, CBF) information for the voxel-wise prediction, while the second method employs also apparent diffusion coefficient information but the complete perfusion information in terms of the voxel-wise residue functions instead of the perfusion parameter maps for the voxel-wise prediction. Overall, the comparison of the results of the two prediction methods for the 18 patients using a leave-one-out cross validation revealed no considerable differences. Quantitatively, the parameter-based prediction of tissue outcome led to a mean Dice coefficient of 0.474, while the prediction using the residue functions led to a mean Dice coefficient of 0.461. Thus, it may be concluded from the results of this study that the perfusion parameter maps typically derived from PWI datasets include all valuable perfusion information required for a voxel-based tissue outcome prediction, while the complete analysis of the residue functions does not add further benefits for the voxel-wise tissue outcome prediction and is also computationally more expensive.

On the construction of a ground truth framework for evaluating voxel-based diffusion tensor MRI analysis methods.

PubMed

Van Hecke, Wim; Sijbers, Jan; De Backer, Steve; Poot, Dirk; Parizel, Paul M; Leemans, Alexander

2009-07-01

Although many studies are starting to use voxel-based analysis (VBA) methods to compare diffusion tensor images between healthy and diseased subjects, it has been demonstrated that VBA results depend heavily on parameter settings and implementation strategies, such as the applied coregistration technique, smoothing kernel width, statistical analysis, etc. In order to investigate the effect of different parameter settings and implementations on the accuracy and precision of the VBA results quantitatively, ground truth knowledge regarding the underlying microstructural alterations is required. To address the lack of such a gold standard, simulated diffusion tensor data sets are developed, which can model an array of anomalies in the diffusion properties of a predefined location. These data sets can be employed to evaluate the numerous parameters that characterize the pipeline of a VBA algorithm and to compare the accuracy, precision, and reproducibility of different post-processing approaches quantitatively. We are convinced that the use of these simulated data sets can improve the understanding of how different diffusion tensor image post-processing techniques affect the outcome of VBA. In turn, this may possibly lead to a more standardized and reliable evaluation of diffusion tensor data sets of large study groups with a wide range of white matter altering pathologies. The simulated DTI data sets will be made available online (http://www.dti.ua.ac.be).

Are power calculations useful? A multicentre neuroimaging study

PubMed Central

Suckling, John; Henty, Julian; Ecker, Christine; Deoni, Sean C; Lombardo, Michael V; Baron-Cohen, Simon; Jezzard, Peter; Barnes, Anna; Chakrabarti, Bhismadev; Ooi, Cinly; Lai, Meng-Chuan; Williams, Steven C; Murphy, Declan GM; Bullmore, Edward

2014-01-01

There are now many reports of imaging experiments with small cohorts of typical participants that precede large-scale, often multicentre studies of psychiatric and neurological disorders. Data from these calibration experiments are sufficient to make estimates of statistical power and predictions of sample size and minimum observable effect sizes. In this technical note, we suggest how previously reported voxel-based power calculations can support decision making in the design, execution and analysis of cross-sectional multicentre imaging studies. The choice of MRI acquisition sequence, distribution of recruitment across acquisition centres, and changes to the registration method applied during data analysis are considered as examples. The consequences of modification are explored in quantitative terms by assessing the impact on sample size for a fixed effect size and detectable effect size for a fixed sample size. The calibration experiment dataset used for illustration was a precursor to the now complete Medical Research Council Autism Imaging Multicentre Study (MRC-AIMS). Validation of the voxel-based power calculations is made by comparing the predicted values from the calibration experiment with those observed in MRC-AIMS. The effect of non-linear mappings during image registration to a standard stereotactic space on the prediction is explored with reference to the amount of local deformation. In summary, power calculations offer a validated, quantitative means of making informed choices on important factors that influence the outcome of studies that consume significant resources. PMID:24644267

The effect of statistical noise on IMRT plan quality and convergence for MC-based and MC-correction-based optimized treatment plans.

PubMed

Siebers, Jeffrey V

2008-04-04

Monte Carlo (MC) is rarely used for IMRT plan optimization outside of research centres due to the extensive computational resources or long computation times required to complete the process. Time can be reduced by degrading the statistical precision of the MC dose calculation used within the optimization loop. However, this eventually introduces optimization convergence errors (OCEs). This study determines the statistical noise levels tolerated during MC-IMRT optimization under the condition that the optimized plan has OCEs <100 cGy (1.5% of the prescription dose) for MC-optimized IMRT treatment plans.Seven-field prostate IMRT treatment plans for 10 prostate patients are used in this study. Pre-optimization is performed for deliverable beams with a pencil-beam (PB) dose algorithm. Further deliverable-based optimization proceeds using: (1) MC-based optimization, where dose is recomputed with MC after each intensity update or (2) a once-corrected (OC) MC-hybrid optimization, where a MC dose computation defines beam-by-beam dose correction matrices that are used during a PB-based optimization. Optimizations are performed with nominal per beam MC statistical precisions of 2, 5, 8, 10, 15, and 20%. Following optimizer convergence, beams are re-computed with MC using 2% per beam nominal statistical precision and the 2 PTV and 10 OAR dose indices used in the optimization objective function are tallied. For both the MC-optimization and OC-optimization methods, statistical equivalence tests found that OCEs are less than 1.5% of the prescription dose for plans optimized with nominal statistical uncertainties of up to 10% per beam. The achieved statistical uncertainty in the patient for the 10% per beam simulations from the combination of the 7 beams is ~3% with respect to maximum dose for voxels with D>0.5D(max). The MC dose computation time for the OC-optimization is only 6.2 minutes on a single 3 Ghz processor with results clinically equivalent to high precision MC computations.

Efficient simulation of voxelized phantom in GATE with embedded SimSET multiple photon history generator.

PubMed

Lin, Hsin-Hon; Chuang, Keh-Shih; Lin, Yi-Hsing; Ni, Yu-Ching; Wu, Jay; Jan, Meei-Ling

2014-10-21

GEANT4 Application for Tomographic Emission (GATE) is a powerful Monte Carlo simulator that combines the advantages of the general-purpose GEANT4 simulation code and the specific software tool implementations dedicated to emission tomography. However, the detailed physical modelling of GEANT4 is highly computationally demanding, especially when tracking particles through voxelized phantoms. To circumvent the relatively slow simulation of voxelized phantoms in GATE, another efficient Monte Carlo code can be used to simulate photon interactions and transport inside a voxelized phantom. The simulation system for emission tomography (SimSET), a dedicated Monte Carlo code for PET/SPECT systems, is well-known for its efficiency in simulation of voxel-based objects. An efficient Monte Carlo workflow integrating GATE and SimSET for simulating pinhole SPECT has been proposed to improve voxelized phantom simulation. Although the workflow achieves a desirable increase in speed, it sacrifices the ability to simulate decaying radioactive sources such as non-pure positron emitters or multiple emission isotopes with complex decay schemes and lacks the modelling of time-dependent processes due to the inherent limitations of the SimSET photon history generator (PHG). Moreover, a large volume of disk storage is needed to store the huge temporal photon history file produced by SimSET that must be transported to GATE. In this work, we developed a multiple photon emission history generator (MPHG) based on SimSET/PHG to support a majority of the medically important positron emitters. We incorporated the new generator codes inside GATE to improve the simulation efficiency of voxelized phantoms in GATE, while eliminating the need for the temporal photon history file. The validation of this new code based on a MicroPET R4 system was conducted for (124)I and (18)F with mouse-like and rat-like phantoms. Comparison of GATE/MPHG with GATE/GEANT4 indicated there is a slight difference in energy spectra for energy below 50 keV due to the lack of x-ray simulation from (124)I decay in the new code. The spatial resolution, scatter fraction and count rate performance are in good agreement between the two codes. For the case studies of (18)F-NaF ((124)I-IAZG) using MOBY phantom with 1  ×  1 × 1 mm(3) voxel sizes, the results show that GATE/MPHG can achieve acceleration factors of approximately 3.1 × (4.5 ×), 6.5 × (10.7 ×) and 9.5 × (31.0 ×) compared with GATE using the regular navigation method, the compressed voxel method and the parameterized tracking technique, respectively. In conclusion, the implementation of MPHG in GATE allows for improved efficiency of voxelized phantom simulations and is suitable for studying clinical and preclinical imaging.

Semi-automated brain tumor segmentation on multi-parametric MRI using regularized non-negative matrix factorization.

PubMed

Sauwen, Nicolas; Acou, Marjan; Sima, Diana M; Veraart, Jelle; Maes, Frederik; Himmelreich, Uwe; Achten, Eric; Huffel, Sabine Van

2017-05-04

Segmentation of gliomas in multi-parametric (MP-)MR images is challenging due to their heterogeneous nature in terms of size, appearance and location. Manual tumor segmentation is a time-consuming task and clinical practice would benefit from (semi-) automated segmentation of the different tumor compartments. We present a semi-automated framework for brain tumor segmentation based on non-negative matrix factorization (NMF) that does not require prior training of the method. L1-regularization is incorporated into the NMF objective function to promote spatial consistency and sparseness of the tissue abundance maps. The pathological sources are initialized through user-defined voxel selection. Knowledge about the spatial location of the selected voxels is combined with tissue adjacency constraints in a post-processing step to enhance segmentation quality. The method is applied to an MP-MRI dataset of 21 high-grade glioma patients, including conventional, perfusion-weighted and diffusion-weighted MRI. To assess the effect of using MP-MRI data and the L1-regularization term, analyses are also run using only conventional MRI and without L1-regularization. Robustness against user input variability is verified by considering the statistical distribution of the segmentation results when repeatedly analyzing each patient's dataset with a different set of random seeding points. Using L1-regularized semi-automated NMF segmentation, mean Dice-scores of 65%, 74 and 80% are found for active tumor, the tumor core and the whole tumor region. Mean Hausdorff distances of 6.1 mm, 7.4 mm and 8.2 mm are found for active tumor, the tumor core and the whole tumor region. Lower Dice-scores and higher Hausdorff distances are found without L1-regularization and when only considering conventional MRI data. Based on the mean Dice-scores and Hausdorff distances, segmentation results are competitive with state-of-the-art in literature. Robust results were found for most patients, although careful voxel selection is mandatory to avoid sub-optimal segmentation.

Imaging Bone–Cartilage Interactions in Osteoarthritis Using [18F]-NaF PET-MRI

PubMed Central

Pedoia, Valentina; Seo, Youngho; Yang, Jaewon; Bucknor, Matt; Franc, Benjamin L.; Majumdar, Sharmila

2016-01-01

Purpose: Simultaneous positron emission tomography–magnetic resonance imaging (PET-MRI) is an emerging technology providing both anatomical and functional images without increasing the scan time. Compared to the traditional PET/computed tomography imaging, it also exposes the patient to significantly less radiation and provides better anatomical images as MRI provides superior soft tissue characterization. Using PET-MRI, we aim to study interactions between cartilage composition and bone function simultaneously, in knee osteoarthritis (OA). Procedures: In this article, bone turnover and remodeling was studied using [18F]-sodium fluoride (NaF) PET data. Quantitative MR-derived T1ρ relaxation times characterized the biochemical cartilage degeneration. Sixteen participants with early signs of OA of the knee received intravenous injections of [18F]-NaF at the onset of PET-MR image acquisition. Regions of interest were identified, and kinetic analysis of dynamic PET data provided the rate of uptake (Ki) and the normalized uptake (standardized uptake value) of [18F]-NaF in the bone. Morphological MR images and quantitative voxel-based T1ρ maps of cartilage were obtained using an atlas-based registration technique to segment cartilage automatically. Voxel-by-voxel statistical parameter mapping was used to investigate the relationship between bone and cartilage. Results: Increases in cartilage T1ρ, indicating degenerative changes, were associated with increased turnover in the adjoining bone but reduced turnover in the nonadjoining compartments. Associations between pain and increased bone uptake were seen in the absence of morphological lesions in cartilage, but the relationship was reversed in the presence of incident cartilage lesions. Conclusion: This study shows significant cartilage and bone interactions in OA of the knee joint using simultaneous [18F]-NaF PET-MR, the first in human study. These observations highlight the complex biomechanical and biochemical interactions in the whole knee joint in OA, which potentially could help assess therapeutic targets in treating OA. PMID:28654417

Stroke Location Is an Independent Predictor of Cognitive Outcome.

PubMed

Munsch, Fanny; Sagnier, Sharmila; Asselineau, Julien; Bigourdan, Antoine; Guttmann, Charles R; Debruxelles, Sabrina; Poli, Mathilde; Renou, Pauline; Perez, Paul; Dousset, Vincent; Sibon, Igor; Tourdias, Thomas

2016-01-01

On top of functional outcome, accurate prediction of cognitive outcome for stroke patients is an unmet need with major implications for clinical management. We investigated whether stroke location may contribute independent prognostic value to multifactorial predictive models of functional and cognitive outcomes. Four hundred twenty-eight consecutive patients with ischemic stroke were prospectively assessed with magnetic resonance imaging at 24 to 72 hours and at 3 months for functional outcome using the modified Rankin Scale and cognitive outcome using the Montreal Cognitive Assessment (MoCA). Statistical maps of functional and cognitive eloquent regions were derived from the first 215 patients (development sample) using voxel-based lesion-symptom mapping. We used multivariate logistic regression models to study the influence of stroke location (number of eloquent voxels from voxel-based lesion-symptom mapping maps), age, initial National Institutes of Health Stroke Scale and stroke volume on modified Rankin Scale and MoCA. The second part of our cohort was used as an independent replication sample. In univariate analyses, stroke location, age, initial National Institutes of Health Stroke Scale, and stroke volume were all predictive of poor modified Rankin Scale and MoCA. In multivariable analyses, stroke location remained the strongest independent predictor of MoCA and significantly improved the prediction compared with using only age, initial National Institutes of Health Stroke Scale, and stroke volume (area under the curve increased from 0.697-0.771; difference=0.073; 95% confidence interval, 0.008-0.155). In contrast, stroke location did not persist as independent predictor of modified Rankin Scale that was mainly driven by initial National Institutes of Health Stroke Scale (area under the curve going from 0.840 to 0.835). Similar results were obtained in the replication sample. Stroke location is an independent predictor of cognitive outcome (MoCA) at 3 months post stroke. © 2015 American Heart Association, Inc.

Calculation of Dose Deposition in 3D Voxels by Heavy Ions

NASA Technical Reports Server (NTRS)

Plante, Ianik; Cucinotta, Francis A.

2010-01-01

The biological response to high-LET radiation is very different from low-LET radiation, and can be partly attributed to the energy deposition by the radiation. Several experiments, notably detection of gamma-H2AX foci by immunofluorescence, has revealed important differences in the nature and in the spatial distribution of double-strand breaks (DSB) induced by low- and high-LET radiations. Many calculations, most of which are based on amorphous track models with radial dose, have been combined with chromosome models to calculate the number and distribution of DSB within nuclei and chromosome aberrations. In this work, the Monte-Carlo track structure simulation code RITRACKS have been used to calculate directly the energy deposition in voxels (3D pixels). A cubic volume of 5 micrometers of side was irradiated by 1) 450 (1)H+ ions of 300 MeV (LET is approximately 0.3 keV/micrometer) and 2) by 1 (56)Fe26+ ion of 1 GeV/amu (LET is approximately 150 keV/micrometer). In both cases, the dose deposited in the volume is approximately 1 Gy. All energy deposition events are recorded and dose is calculated in voxels of 20 micrometers of side. The voxels are then visualized in 3D by using a color scale to represent the intensity of the dose in a voxel. This simple approach has revealed several important points which may help understand experimental observations. In both simulations, voxels which receive low dose are the most numerous, and those corresponding to electron track ends received a dose which is in the higher range. The dose voxels are distributed randomly and scattered uniformly within the volume irradiated by low-LET radiation. The distribution of the voxels shows major differences for the (56)Fe26+ ion. The track structure can still be seen, and voxels with much higher dose are found in the region corresponding to the track "core". These high-dose voxels are not found in the low-LET irradiation simulation and may be responsible for DSB that are more difficult to repair. By applying a threshold on the dose visualization, voxels corresponding to electron track ends are evidenced and the spatial distribution of voxels is very similar to the distribution of DSB observed in gamma H2AX experiments, even if no chromosomes have been included in the simulation. Furthermore, this work has shown that a significant dose is deposited in voxels corresponding to electron track ends. Since some delta-rays from iron ion can travel several millimeters, they may also be of radiobiological importance.

Effect of trial-to-trial variability on optimal event-related fMRI design: Implications for Beta-series correlation and multi-voxel pattern analysis

PubMed Central

Abdulrahman, Hunar; Henson, Richard N.

2016-01-01

Functional magnetic resonance imaging (fMRI) studies typically employ rapid, event-related designs for behavioral reasons and for reasons associated with statistical efficiency. Efficiency is calculated from the precision of the parameters (Betas) estimated from a General Linear Model (GLM) in which trial onsets are convolved with a Hemodynamic Response Function (HRF). However, previous calculations of efficiency have ignored likely variability in the neural response from trial to trial, for example due to attentional fluctuations, or different stimuli across trials. Here we compare three GLMs in their efficiency for estimating average and individual Betas across trials as a function of trial variability, scan noise and Stimulus Onset Asynchrony (SOA): “Least Squares All” (LSA), “Least Squares Separate” (LSS) and “Least Squares Unitary” (LSU). Estimation of responses to individual trials in particular is important for both functional connectivity using “Beta-series correlation” and “multi-voxel pattern analysis” (MVPA). Our simulations show that the ratio of trial-to-trial variability to scan noise impacts both the optimal SOA and optimal GLM, especially for short SOAs < 5 s: LSA is better when this ratio is high, whereas LSS and LSU are better when the ratio is low. For MVPA, the consistency across voxels of trial variability and of scan noise is also critical. These findings not only have important implications for design of experiments using Beta-series regression and MVPA, but also statistical parametric mapping studies that seek only efficient estimation of the mean response across trials. PMID:26549299

Statistical representative elementary volumes of porous media determined using greyscale analysis of 3D tomograms

NASA Astrophysics Data System (ADS)

Bruns, S.; Stipp, S. L. S.; Sørensen, H. O.

2017-09-01

Digital rock physics carries the dogmatic concept of having to segment volume images for quantitative analysis but segmentation rejects huge amounts of signal information. Information that is essential for the analysis of difficult and marginally resolved samples, such as materials with very small features, is lost during segmentation. In X-ray nanotomography reconstructions of Hod chalk we observed partial volume voxels with an abundance that limits segmentation based analysis. Therefore, we investigated the suitability of greyscale analysis for establishing statistical representative elementary volumes (sREV) for the important petrophysical parameters of this type of chalk, namely porosity, specific surface area and diffusive tortuosity, by using volume images without segmenting the datasets. Instead, grey level intensities were transformed to a voxel level porosity estimate using a Gaussian mixture model. A simple model assumption was made that allowed formulating a two point correlation function for surface area estimates using Bayes' theory. The same assumption enables random walk simulations in the presence of severe partial volume effects. The established sREVs illustrate that in compacted chalk, these simulations cannot be performed in binary representations without increasing the resolution of the imaging system to a point where the spatial restrictions of the represented sample volume render the precision of the measurement unacceptable. We illustrate this by analyzing the origins of variance in the quantitative analysis of volume images, i.e. resolution dependence and intersample and intrasample variance. Although we cannot make any claims on the accuracy of the approach, eliminating the segmentation step from the analysis enables comparative studies with higher precision and repeatability.

Effect of SOHAM meditation on human brain: a voxel-based morphometry study.

PubMed

Kumar, Uttam; Guleria, Anupam; Kishan, Sadguru Sri Kunal; Khetrapal, C L

2014-01-01

The anatomical correlates of long-term meditators involved in practice of "SOHAM" meditation have been studied using voxel-based morphometry (VBM). The VBM analysis indicates significantly higher gray matter density in brain stem, ventral pallidum, and supplementary motor area in the meditators as compared with age-matched nonmeditators. The observed changes in brain structure are compared with other forms of meditation. Copyright © 2013 by the American Society of Neuroimaging.

[Voxel-Based Morphometry in Autism Spectrum Disorder].

PubMed

Yamasue, Hidenori

2017-05-01

Autism spectrum disorder shows deficits in social communication and interaction including nonverbal communicative behaviors (e.g., eye contact, gestures, voice prosody, and facial expressions) and restricted and repetitive behaviors as its core symptoms. These core symptoms are emerged as an atypical behavioral development in toddlers with the disorder. Atypical neural development is considered to be a neural underpinning of such behaviorally atypical development. A number of studies using voxel-based morphometry have already been conducted to compare regional brain volumes between individuals with autism spectrum disorder and those with typical development. Furthermore, more than ten papers employing meta-analyses of the comparisons using voxel based morphometry between individuals with autism spectrum disorder and those with typical development have already been published. The current review paper adds some brief discussions about potential factors contributing to the inconsistency observed in the previous findings such as difficulty in controlling the confounding effects of different developmental phases among study participants.

Representation and visualization of variability in a 3D anatomical atlas using the kidney as an example

NASA Astrophysics Data System (ADS)

Hacker, Silke; Handels, Heinz

2006-03-01

Computer-based 3D atlases allow an interactive exploration of the human body. However, in most cases such 3D atlases are derived from one single individual, and therefore do not regard the variability of anatomical structures concerning their shape and size. Since the geometric variability across humans plays an important role in many medical applications, our goal is to develop a framework of an anatomical atlas for representation and visualization of the variability of selected anatomical structures. The basis of the project presented is the VOXEL-MAN atlas of inner organs that was created from the Visible Human data set. For modeling anatomical shapes and their variability we utilize "m-reps" which allow a compact representation of anatomical objects on the basis of their skeletons. As an example we used a statistical model of the kidney that is based on 48 different variants. With the integration of a shape description into the VOXEL-MAN atlas it is now possible to query and visualize different shape variations of an organ, e.g. by specifying a person's age or gender. In addition to the representation of individual shape variants, the average shape of a population can be displayed. Besides a surface representation, a volume-based representation of the kidney's shape variants is also possible. It results from the deformation of the reference kidney of the volume-based model using the m-rep shape description. In this way a realistic visualization of the shape variants becomes possible, as well as the visualization of the organ's internal structures.

Coordinate based random effect size meta-analysis of neuroimaging studies.

PubMed

Tench, C R; Tanasescu, Radu; Constantinescu, C S; Auer, D P; Cottam, W J

2017-06-01

Low power in neuroimaging studies can make them difficult to interpret, and Coordinate based meta-analysis (CBMA) may go some way to mitigating this issue. CBMA has been used in many analyses to detect where published functional MRI or voxel-based morphometry studies testing similar hypotheses report significant summary results (coordinates) consistently. Only the reported coordinates and possibly t statistics are analysed, and statistical significance of clusters is determined by coordinate density. Here a method of performing coordinate based random effect size meta-analysis and meta-regression is introduced. The algorithm (ClusterZ) analyses both coordinates and reported t statistic or Z score, standardised by the number of subjects. Statistical significance is determined not by coordinate density, but by a random effects meta-analyses of reported effects performed cluster-wise using standard statistical methods and taking account of censoring inherent in the published summary results. Type 1 error control is achieved using the false cluster discovery rate (FCDR), which is based on the false discovery rate. This controls both the family wise error rate under the null hypothesis that coordinates are randomly drawn from a standard stereotaxic space, and the proportion of significant clusters that are expected under the null. Such control is necessary to avoid propagating and even amplifying the very issues motivating the meta-analysis in the first place. ClusterZ is demonstrated on both numerically simulated data and on real data from reports of grey matter loss in multiple sclerosis (MS) and syndromes suggestive of MS, and of painful stimulus in healthy controls. The software implementation is available to download and use freely. Copyright © 2017 Elsevier Inc. All rights reserved.

Real-time Interpolation for True 3-Dimensional Ultrasound Image Volumes

PubMed Central

Ji, Songbai; Roberts, David W.; Hartov, Alex; Paulsen, Keith D.

2013-01-01

We compared trilinear interpolation to voxel nearest neighbor and distance-weighted algorithms for fast and accurate processing of true 3-dimensional ultrasound (3DUS) image volumes. In this study, the computational efficiency and interpolation accuracy of the 3 methods were compared on the basis of a simulated 3DUS image volume, 34 clinical 3DUS image volumes from 5 patients, and 2 experimental phantom image volumes. We show that trilinear interpolation improves interpolation accuracy over both the voxel nearest neighbor and distance-weighted algorithms yet achieves real-time computational performance that is comparable to the voxel nearest neighbor algrorithm (1–2 orders of magnitude faster than the distance-weighted algorithm) as well as the fastest pixel-based algorithms for processing tracked 2-dimensional ultrasound images (0.035 seconds per 2-dimesional cross-sectional image [76,800 pixels interpolated, or 0.46 ms/1000 pixels] and 1.05 seconds per full volume with a 1-mm3 voxel size [4.6 million voxels interpolated, or 0.23 ms/1000 voxels]). On the basis of these results, trilinear interpolation is recommended as a fast and accurate interpolation method for rectilinear sampling of 3DUS image acquisitions, which is required to facilitate subsequent processing and display during operating room procedures such as image-guided neurosurgery. PMID:21266563

Real-time interpolation for true 3-dimensional ultrasound image volumes.

PubMed

Ji, Songbai; Roberts, David W; Hartov, Alex; Paulsen, Keith D

2011-02-01

We compared trilinear interpolation to voxel nearest neighbor and distance-weighted algorithms for fast and accurate processing of true 3-dimensional ultrasound (3DUS) image volumes. In this study, the computational efficiency and interpolation accuracy of the 3 methods were compared on the basis of a simulated 3DUS image volume, 34 clinical 3DUS image volumes from 5 patients, and 2 experimental phantom image volumes. We show that trilinear interpolation improves interpolation accuracy over both the voxel nearest neighbor and distance-weighted algorithms yet achieves real-time computational performance that is comparable to the voxel nearest neighbor algrorithm (1-2 orders of magnitude faster than the distance-weighted algorithm) as well as the fastest pixel-based algorithms for processing tracked 2-dimensional ultrasound images (0.035 seconds per 2-dimesional cross-sectional image [76,800 pixels interpolated, or 0.46 ms/1000 pixels] and 1.05 seconds per full volume with a 1-mm(3) voxel size [4.6 million voxels interpolated, or 0.23 ms/1000 voxels]). On the basis of these results, trilinear interpolation is recommended as a fast and accurate interpolation method for rectilinear sampling of 3DUS image acquisitions, which is required to facilitate subsequent processing and display during operating room procedures such as image-guided neurosurgery.

Preliminary study of brain glucose metabolism changes in patients with lung cancer of different histological types.

PubMed

Li, Wei-Ling; Fu, Chang; Xuan, Ang; Shi, Da-Peng; Gao, Yong-Ju; Zhang, Jie; Xu, Jun-Ling

2015-02-05

Cerebral glucose metabolism changes are always observed in patients suffering from malignant tumors. This preliminary study aimed to investigate the brain glucose metabolism changes in patients with lung cancer of different histological types. One hundred and twenty patients with primary untreated lung cancer, who visited People's Hospital of Zhengzhou University from February 2012 to July 2013, were divided into three groups based on histological types confirmed by biopsy or surgical pathology, which included adenocarcinoma (52 cases), squamous cell carcinoma (43 cases), and small-cell carcinoma (25 cases). The whole body 18F-fluorodeoxyglucose (18F-FDG) positron emission tomography (PET)/computed tomography (CT) of these cases was retrospectively studied. The brain PET data of three groups were analyzed individually using statistical parametric maps (SPM) software, with 50 age-matched and gender-matched healthy controls for comparison. The brain resting glucose metabolism in all three lung cancer groups showed regional cerebral metabolic reduction. The hypo-metabolic cerebral regions were mainly distributed at the left superior and middle frontal, bilateral superior and middle temporal and inferior and middle temporal gyrus. Besides, the hypo-metabolic regions were also found in the right inferior parietal lobule and hippocampus in the small-cell carcinoma group. The area of the total hypo-metabolic cerebral regions in the small-cell carcinoma group (total voxel value 3255) was larger than those in the adenocarcinoma group (total voxel value 1217) and squamous cell carcinoma group (total voxel value 1292). The brain resting glucose metabolism in patients with lung cancer shows regional cerebral metabolic reduction and the brain hypo-metabolic changes are related to the histological types of lung cancer.

egs_brachy: a versatile and fast Monte Carlo code for brachytherapy

NASA Astrophysics Data System (ADS)

Chamberland, Marc J. P.; Taylor, Randle E. P.; Rogers, D. W. O.; Thomson, Rowan M.

2016-12-01

egs_brachy is a versatile and fast Monte Carlo (MC) code for brachytherapy applications. It is based on the EGSnrc code system, enabling simulation of photons and electrons. Complex geometries are modelled using the EGSnrc C++ class library and egs_brachy includes a library of geometry models for many brachytherapy sources, in addition to eye plaques and applicators. Several simulation efficiency enhancing features are implemented in the code. egs_brachy is benchmarked by comparing TG-43 source parameters of three source models to previously published values. 3D dose distributions calculated with egs_brachy are also compared to ones obtained with the BrachyDose code. Well-defined simulations are used to characterize the effectiveness of many efficiency improving techniques, both as an indication of the usefulness of each technique and to find optimal strategies. Efficiencies and calculation times are characterized through single source simulations and simulations of idealized and typical treatments using various efficiency improving techniques. In general, egs_brachy shows agreement within uncertainties with previously published TG-43 source parameter values. 3D dose distributions from egs_brachy and BrachyDose agree at the sub-percent level. Efficiencies vary with radionuclide and source type, number of sources, phantom media, and voxel size. The combined effects of efficiency-improving techniques in egs_brachy lead to short calculation times: simulations approximating prostate and breast permanent implant (both with (2 mm)3 voxels) and eye plaque (with (1 mm)3 voxels) treatments take between 13 and 39 s, on a single 2.5 GHz Intel Xeon E5-2680 v3 processor core, to achieve 2% average statistical uncertainty on doses within the PTV. egs_brachy will be released as free and open source software to the research community.

SU-E-T-169: Evaluation of Oncentra TPS for Nasopharynx Brachy Using Patient Specific Voxel Phantom and EGSnrc

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hadad, K; Zoherhvand, M; Faghihi, R

2014-06-01

Purpose: Nasopharnx carcinoma (NPC) treatment is being carried out using Ir-192 HDR seeds in Mehdieh Hospital in Hamadan, Iran. The Oncentra™ TPS is based on optimized TG-43 formalism which disregards heterogeneity in the treatment area. Due to abundant heterogeneity in head and neck, comparison of the Oncentra™ TPS dose evaluation and an accurate dose calculation method in NPC brachytherapy is the objective of this study. Methods: CT DICOMs of a patient with NPC obtained from Mehdieh Hospital used to create 3D voxel phantom with CTCREATE utility of EGSnrc code package. The voxel phantom together with Ir-192 HDR brachytherapy source weremore » the input to DOSXYZnrc to calculate the 3D dose distribution. The sources were incorporate with type 6 source in DOSXYZnrc and their dwell times were taken into account in final dose calculations. Results: The direct comparison between isodoses as well as DVHs for the GTV, PTV and CTV obtained by Oncentra™ and EGSnrc Monte Carlo code are made. EGSnrc results are obtained using 5×10{sup 9} histories to reduce the statistical error below 1% in GTV and 5% in 5% dose areas. The standard ICRP700 cross section library is employed in DOSXYZnrc dose calculation. Conclusion: A direct relationship between increased dose differences and increased material density (hence heterogeneity) is observed when isodoses contours of the TPS and DOSXYZnrc are compared. Regarding the point dose calculations, the differences range from 1.2% in PTV to 5.6% for cavity region and 7.8% for bone regions. While Oncentra™ TPS overestimates the dose in cavities, it tends to underestimate dose depositions within bones.« less

egs_brachy: a versatile and fast Monte Carlo code for brachytherapy.

PubMed

Chamberland, Marc J P; Taylor, Randle E P; Rogers, D W O; Thomson, Rowan M

2016-12-07

egs_brachy is a versatile and fast Monte Carlo (MC) code for brachytherapy applications. It is based on the EGSnrc code system, enabling simulation of photons and electrons. Complex geometries are modelled using the EGSnrc C++ class library and egs_brachy includes a library of geometry models for many brachytherapy sources, in addition to eye plaques and applicators. Several simulation efficiency enhancing features are implemented in the code. egs_brachy is benchmarked by comparing TG-43 source parameters of three source models to previously published values. 3D dose distributions calculated with egs_brachy are also compared to ones obtained with the BrachyDose code. Well-defined simulations are used to characterize the effectiveness of many efficiency improving techniques, both as an indication of the usefulness of each technique and to find optimal strategies. Efficiencies and calculation times are characterized through single source simulations and simulations of idealized and typical treatments using various efficiency improving techniques. In general, egs_brachy shows agreement within uncertainties with previously published TG-43 source parameter values. 3D dose distributions from egs_brachy and BrachyDose agree at the sub-percent level. Efficiencies vary with radionuclide and source type, number of sources, phantom media, and voxel size. The combined effects of efficiency-improving techniques in egs_brachy lead to short calculation times: simulations approximating prostate and breast permanent implant (both with (2 mm) 3 voxels) and eye plaque (with (1 mm) 3 voxels) treatments take between 13 and 39 s, on a single 2.5 GHz Intel Xeon E5-2680 v3 processor core, to achieve 2% average statistical uncertainty on doses within the PTV. egs_brachy will be released as free and open source software to the research community.

Use of the GEANT4 Monte Carlo to determine three-dimensional dose factors for radionuclide dosimetry

NASA Astrophysics Data System (ADS)

Amato, Ernesto; Italiano, Antonio; Minutoli, Fabio; Baldari, Sergio

2013-04-01

The voxel-level dosimetry is the most simple and common approach to internal dosimetry of nonuniform distributions of activity within the human body. Aim of this work was to obtain the dose "S" factors (mGy/MBqs) at the voxel level for eight beta and beta-gamma emitting radionuclides commonly used in nuclear medicine diagnostic and therapeutic procedures. We developed a Monte Carlo simulation in GEANT4 of a region of soft tissue as defined by the ICRP, divided into 11×11×11 cubic voxels, 3 mm in side. The simulation used the parameterizations of the electromagnetic interaction optimized for low energy (EEDL, EPDL). The decay of each radionuclide (32P, 90Y, 99mTc, 177Lu, 131I, 153Sm, 186Re, 188Re) were simulated homogeneously distributed within the central voxel (0,0,0), and the energy deposited in the surrounding voxels was mediated on the 8 octants of the three dimensional space, for reasons of symmetry. The results obtained were compared with those available in the literature. While the iodine deviations remain within 16%, for phosphorus, a pure beta emitter, the agreement is very good for self-dose (0,0,0) and good for the dose to first neighbors, while differences are observed ranging from -60% to +100% for voxels far distant from the source. The existence of significant differences in the percentage calculation of the voxel S factors, especially for pure beta emitters such as 32P or 90Y, has already been highlighted by other authors. These data can usefully extend the dosimetric approach based on the voxel to other radionuclides not covered in the available literature.

Clusters of Low (18)F-Fluorodeoxyglucose Uptake Voxels in Combat Veterans with Traumatic Brain Injury and Post-Traumatic Stress Disorder.

PubMed

Buchsbaum, Monte S; Simmons, Alan N; DeCastro, Alex; Farid, Nikdokht; Matthews, Scott C

2015-11-15

Individuals with mild traumatic brain injury (TBI) show diminished metabolic activity when studied with positron emission tomography (PET) with (18)F-fluorodeoxyglucose (FDG). Since blast injury may not be localized in the same specific anatomical areas in every patient or may be diffuse, significance probability mapping may be vulnerable to false-negative detection of abnormalities. To address this problem, we used an anatomically independent measure to assess PET scans: increased numbers of contiguous voxels that are 2 standard deviations below values found in an uninjured control group. We examined this in three age-matched groups of male patients: 16 veterans with a history of mild TBI, 17 veterans with both mild TBI and post-traumatic stress disorder (PTSD), and 15 veterans without either condition. After FDG administration, subjects performed a modified version of the California Verbal Learning Task. Clusters of low uptake voxels were identified by computing the mean and standard deviation for each voxel in the healthy combat veteran group and then determining the voxel-based z-score for the patient groups. Abnormal clusters were defined as those that contained contiguous voxels with a z-score <-2. Patients with mild TBI alone and patients with TBI+PTSD had larger clusters of low uptake voxels, and cluster size significantly differentiated the mild TBI groups from combat controls. Clusters were more irregular in shape in patients, and patients also had a larger number of low-activity voxels throughout the brain. In mild TBI and TBI+PTSD patients, but not healthy subjects, cluster volume was significantly correlated with verbal learning during FDG uptake.

An evaluation of volume-based morphometry for prediction of mild cognitive impairment and Alzheimer's disease

PubMed Central

Schmitter, Daniel; Roche, Alexis; Maréchal, Bénédicte; Ribes, Delphine; Abdulkadir, Ahmed; Bach-Cuadra, Meritxell; Daducci, Alessandro; Granziera, Cristina; Klöppel, Stefan; Maeder, Philippe; Meuli, Reto; Krueger, Gunnar

2014-01-01

Voxel-based morphometry from conventional T1-weighted images has proved effective to quantify Alzheimer's disease (AD) related brain atrophy and to enable fairly accurate automated classification of AD patients, mild cognitive impaired patients (MCI) and elderly controls. Little is known, however, about the classification power of volume-based morphometry, where features of interest consist of a few brain structure volumes (e.g. hippocampi, lobes, ventricles) as opposed to hundreds of thousands of voxel-wise gray matter concentrations. In this work, we experimentally evaluate two distinct volume-based morphometry algorithms (FreeSurfer and an in-house algorithm called MorphoBox) for automatic disease classification on a standardized data set from the Alzheimer's Disease Neuroimaging Initiative. Results indicate that both algorithms achieve classification accuracy comparable to the conventional whole-brain voxel-based morphometry pipeline using SPM for AD vs elderly controls and MCI vs controls, and higher accuracy for classification of AD vs MCI and early vs late AD converters, thereby demonstrating the potential of volume-based morphometry to assist diagnosis of mild cognitive impairment and Alzheimer's disease. PMID:25429357

Rotating and translating anthropomorphic head voxel models to establish an horizontal Frankfort plane for dental CBCT Monte Carlo simulations: a dose comparison study

NASA Astrophysics Data System (ADS)

Stratis, A.; Zhang, G.; Jacobs, R.; Bogaerts, R.; Bosmans, H.

2016-12-01

In order to carry out Monte Carlo (MC) dosimetry studies, voxel phantoms, modeling human anatomy, and organ-based segmentation of CT image data sets are applied to simulation frameworks. The resulting voxel phantoms preserve patient CT acquisition geometry; in the case of head voxel models built upon head CT images, the head support with which CT scanners are equipped introduces an inclination to the head, and hence to the head voxel model. In dental cone beam CT (CBCT) imaging, patients are always positioned in such a way that the Frankfort line is horizontal, implying that there is no head inclination. The orientation of the head is important, as it influences the distance of critical radiosensitive organs like the thyroid and the esophagus from the x-ray tube. This work aims to propose a procedure to adjust head voxel phantom orientation, and to investigate the impact of head inclination on organ doses in dental CBCT MC dosimetry studies. The female adult ICRP, and three in-house-built paediatric voxel phantoms were in this study. An EGSnrc MC framework was employed to simulate two commonly used protocols; a Morita Accuitomo 170 dental CBCT scanner (FOVs: 60  ×  60 mm2 and 80  ×  80 mm2, standard resolution), and a 3D Teeth protocol (FOV: 100  ×  90 mm2) in a Planmeca Promax 3D MAX scanner. Result analysis revealed large absorbed organ dose differences in radiosensitive organs between the original and the geometrically corrected voxel models of this study, ranging from  -45.6% to 39.3%. Therefore, accurate dental CBCT MC dose calculations require geometrical adjustments to be applied to head voxel models.

MAX meets ADAM: a dosimetric comparison between a voxel-based and a mathematical model for external exposure to photons

NASA Astrophysics Data System (ADS)

Kramer, R.; Vieira, J. W.; Khoury, H. J.; Lima, F. de Andrade

2004-03-01

The International Commission on Radiological Protection intends to revise the organ and tissue equivalent dose conversion coefficients published in various reports. For this purpose the mathematical human medical internal radiation dose (MIRD) phantoms, actually in use, have to be replaced by recently developed voxel-based phantoms. This study investigates the dosimetric consequences, especially with respect to the effective male dose, if not only a MIRD phantom is replaced by a voxel phantom, but also if the tissue compositions and the radiation transport codes are changed. This task will be resolved by systematically replacing in the mathematical ADAM/GSF exposure model, first the radiation transport code, then the tissue composition and finally the phantom anatomy, in order to arrive at the voxel-based MAX/EGS4 exposure model. The results show that the combined effect of these replacements can decrease the effective male dose by up to 25% for external exposures to photons for incident energies above 30 keV for different field geometries, mainly because of increased shielding by a heterogeneous skeleton and by the overlying adipose and muscle tissue, and also because of the positions internal organs have in a realistically designed human body compared to their positions in the mathematically constructed phantom.

- and Graph-Based Point Cloud Segmentation of 3d Scenes Using Perceptual Grouping Laws

NASA Astrophysics Data System (ADS)

Xu, Y.; Hoegner, L.; Tuttas, S.; Stilla, U.

2017-05-01

Segmentation is the fundamental step for recognizing and extracting objects from point clouds of 3D scene. In this paper, we present a strategy for point cloud segmentation using voxel structure and graph-based clustering with perceptual grouping laws, which allows a learning-free and completely automatic but parametric solution for segmenting 3D point cloud. To speak precisely, two segmentation methods utilizing voxel and supervoxel structures are reported and tested. The voxel-based data structure can increase efficiency and robustness of the segmentation process, suppressing the negative effect of noise, outliers, and uneven points densities. The clustering of voxels and supervoxel is carried out using graph theory on the basis of the local contextual information, which commonly conducted utilizing merely pairwise information in conventional clustering algorithms. By the use of perceptual laws, our method conducts the segmentation in a pure geometric way avoiding the use of RGB color and intensity information, so that it can be applied to more general applications. Experiments using different datasets have demonstrated that our proposed methods can achieve good results, especially for complex scenes and nonplanar surfaces of objects. Quantitative comparisons between our methods and other representative segmentation methods also confirms the effectiveness and efficiency of our proposals.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Klüter, Sebastian, E-mail: sebastian.klueter@med.uni-heidelberg.de; Schubert, Kai; Lissner, Steffen

Purpose: The dosimetric verification of treatment plans in helical tomotherapy usually is carried out via verification measurements. In this study, a method for independent dose calculation of tomotherapy treatment plans is presented, that uses a conventional treatment planning system with a pencil kernel dose calculation algorithm for generation of verification dose distributions based on patient CT data. Methods: A pencil beam algorithm that directly uses measured beam data was configured for dose calculation for a tomotherapy machine. Tomotherapy treatment plans were converted into a format readable by an in-house treatment planning system by assigning each projection to one static treatmentmore » field and shifting the calculation isocenter for each field in order to account for the couch movement. The modulation of the fluence for each projection is read out of the delivery sinogram, and with the kernel-based dose calculation, this information can directly be used for dose calculation without the need for decomposition of the sinogram. The sinogram values are only corrected for leaf output and leaf latency. Using the converted treatment plans, dose was recalculated with the independent treatment planning system. Multiple treatment plans ranging from simple static fields to real patient treatment plans were calculated using the new approach and either compared to actual measurements or the 3D dose distribution calculated by the tomotherapy treatment planning system. In addition, dose–volume histograms were calculated for the patient plans. Results: Except for minor deviations at the maximum field size, the pencil beam dose calculation for static beams agreed with measurements in a water tank within 2%/2 mm. A mean deviation to point dose measurements in the cheese phantom of 0.89% ± 0.81% was found for unmodulated helical plans. A mean voxel-based deviation of −0.67% ± 1.11% for all voxels in the respective high dose region (dose values >80%), and a mean local voxel-based deviation of −2.41% ± 0.75% for all voxels with dose values >20% were found for 11 modulated plans in the cheese phantom. Averaged over nine patient plans, the deviations amounted to −0.14% ± 1.97% (voxels >80%) and −0.95% ± 2.27% (>20%, local deviations). For a lung case, mean voxel-based deviations of more than 4% were found, while for all other patient plans, all mean voxel-based deviations were within ±2.4%. Conclusions: The presented method is suitable for independent dose calculation for helical tomotherapy within the known limitations of the pencil beam algorithm. It can serve as verification of the primary dose calculation and thereby reduce the need for time-consuming measurements. By using the patient anatomy and generating full 3D dose data, and combined with measurements of additional machine parameters, it can substantially contribute to overall patient safety.« less

Detecting subject-specific activations using fuzzy clustering

PubMed Central

Seghier, Mohamed L.; Friston, Karl J.; Price, Cathy J.

2007-01-01

Inter-subject variability in evoked brain responses is attracting attention because it may reflect important variability in structure–function relationships over subjects. This variability could be a signature of degenerate (many-to-one) structure–function mappings in normal subjects or reflect changes that are disclosed by brain damage. In this paper, we describe a non-iterative fuzzy clustering algorithm (FCP: fuzzy clustering with fixed prototypes) for characterizing inter-subject variability in between-subject or second-level analyses of fMRI data. The approach identifies the contribution of each subject to response profiles in voxels surviving a classical F-statistic criterion. The output identifies subjects who drive activation in specific cortical regions (local effects) or in voxels distributed across neural systems (global effects). The sensitivity of the approach was assessed in 38 normal subjects performing an overt naming task. FCP revealed that several subjects had either abnormally high or abnormally low responses. FCP may be particularly useful for characterizing outlier responses in rare patients or heterogeneous populations. In these cases, atypical activations may not be detected by standard tests, under parametric assumptions. The advantage of using FCP is that it searches all voxels systematically and can identify atypical activation patterns in a quantitative and unsupervised manner. PMID:17478103

A wavelet-based estimator of the degrees of freedom in denoised fMRI time series for probabilistic testing of functional connectivity and brain graphs.

PubMed

Patel, Ameera X; Bullmore, Edward T

2016-11-15

Connectome mapping using techniques such as functional magnetic resonance imaging (fMRI) has become a focus of systems neuroscience. There remain many statistical challenges in analysis of functional connectivity and network architecture from BOLD fMRI multivariate time series. One key statistic for any time series is its (effective) degrees of freedom, df, which will generally be less than the number of time points (or nominal degrees of freedom, N). If we know the df, then probabilistic inference on other fMRI statistics, such as the correlation between two voxel or regional time series, is feasible. However, we currently lack good estimators of df in fMRI time series, especially after the degrees of freedom of the "raw" data have been modified substantially by denoising algorithms for head movement. Here, we used a wavelet-based method both to denoise fMRI data and to estimate the (effective) df of the denoised process. We show that seed voxel correlations corrected for locally variable df could be tested for false positive connectivity with better control over Type I error and greater specificity of anatomical mapping than probabilistic connectivity maps using the nominal degrees of freedom. We also show that wavelet despiked statistics can be used to estimate all pairwise correlations between a set of regional nodes, assign a P value to each edge, and then iteratively add edges to the graph in order of increasing P. These probabilistically thresholded graphs are likely more robust to regional variation in head movement effects than comparable graphs constructed by thresholding correlations. Finally, we show that time-windowed estimates of df can be used for probabilistic connectivity testing or dynamic network analysis so that apparent changes in the functional connectome are appropriately corrected for the effects of transient noise bursts. Wavelet despiking is both an algorithm for fMRI time series denoising and an estimator of the (effective) df of denoised fMRI time series. Accurate estimation of df offers many potential advantages for probabilistically thresholding functional connectivity and network statistics tested in the context of spatially variant and non-stationary noise. Code for wavelet despiking, seed correlational testing and probabilistic graph construction is freely available to download as part of the BrainWavelet Toolbox at www.brainwavelet.org. Copyright © 2015 The Authors. Published by Elsevier Inc. All rights reserved.

An in vivo MRI Template Set for Morphometry, Tissue Segmentation, and fMRI Localization in Rats

PubMed Central

Valdés-Hernández, Pedro Antonio; Sumiyoshi, Akira; Nonaka, Hiroi; Haga, Risa; Aubert-Vásquez, Eduardo; Ogawa, Takeshi; Iturria-Medina, Yasser; Riera, Jorge J.; Kawashima, Ryuta

2011-01-01

Over the last decade, several papers have focused on the construction of highly detailed mouse high field magnetic resonance image (MRI) templates via non-linear registration to unbiased reference spaces, allowing for a variety of neuroimaging applications such as robust morphometric analyses. However, work in rats has only provided medium field MRI averages based on linear registration to biased spaces with the sole purpose of approximate functional MRI (fMRI) localization. This precludes any morphometric analysis in spite of the need of exploring in detail the neuroanatomical substrates of diseases in a recent advent of rat models. In this paper we present a new in vivo rat T2 MRI template set, comprising average images of both intensity and shape, obtained via non-linear registration. Also, unlike previous rat template sets, we include white and gray matter probabilistic segmentations, expanding its use to those applications demanding prior-based tissue segmentation, e.g., statistical parametric mapping (SPM) voxel-based morphometry. We also provide a preliminary digitalization of latest Paxinos and Watson atlas for anatomical and functional interpretations within the cerebral cortex. We confirmed that, like with previous templates, forepaw and hindpaw fMRI activations can be correctly localized in the expected atlas structure. To exemplify the use of our new MRI template set, were reported the volumes of brain tissues and cortical structures and probed their relationships with ontogenetic development. Other in vivo applications in the near future can be tensor-, deformation-, or voxel-based morphometry, morphological connectivity, and diffusion tensor-based anatomical connectivity. Our template set, freely available through the SPM extension website, could be an important tool for future longitudinal and/or functional extensive preclinical studies. PMID:22275894

Voxel-based morphometry of auditory and speech-related cortex in stutterers.

PubMed

Beal, Deryk S; Gracco, Vincent L; Lafaille, Sophie J; De Nil, Luc F

2007-08-06

Stutterers demonstrate unique functional neural activation patterns during speech production, including reduced auditory activation, relative to nonstutterers. The extent to which these functional differences are accompanied by abnormal morphology of the brain in stutterers is unclear. This study examined the neuroanatomical differences in speech-related cortex between stutterers and nonstutterers using voxel-based morphometry. Results revealed significant differences in localized grey matter and white matter densities of left and right hemisphere regions involved in auditory processing and speech production.

System and method for radiation dose calculation within sub-volumes of a monte carlo based particle transport grid

DOEpatents

Bergstrom, Paul M.; Daly, Thomas P.; Moses, Edward I.; Patterson, Jr., Ralph W.; Schach von Wittenau, Alexis E.; Garrett, Dewey N.; House, Ronald K.; Hartmann-Siantar, Christine L.; Cox, Lawrence J.; Fujino, Donald H.

2000-01-01

A system and method is disclosed for radiation dose calculation within sub-volumes of a particle transport grid. In a first step of the method voxel volumes enclosing a first portion of the target mass are received. A second step in the method defines dosel volumes which enclose a second portion of the target mass and overlap the first portion. A third step in the method calculates common volumes between the dosel volumes and the voxel volumes. A fourth step in the method identifies locations in the target mass of energy deposits. And, a fifth step in the method calculates radiation doses received by the target mass within the dosel volumes. A common volume calculation module inputs voxel volumes enclosing a first portion of the target mass, inputs voxel mass densities corresponding to a density of the target mass within each of the voxel volumes, defines dosel volumes which enclose a second portion of the target mass and overlap the first portion, and calculates common volumes between the dosel volumes and the voxel volumes. A dosel mass module, multiplies the common volumes by corresponding voxel mass densities to obtain incremental dosel masses, and adds the incremental dosel masses corresponding to the dosel volumes to obtain dosel masses. A radiation transport module identifies locations in the target mass of energy deposits. And, a dose calculation module, coupled to the common volume calculation module and the radiation transport module, for calculating radiation doses received by the target mass within the dosel volumes.

Accuracy Assessment of Three-dimensional Surface Reconstructions of In vivo Teeth from Cone-beam Computed Tomography

PubMed Central

Sang, Yan-Hui; Hu, Hong-Cheng; Lu, Song-He; Wu, Yu-Wei; Li, Wei-Ran; Tang, Zhi-Hui

2016-01-01

Background: The accuracy of three-dimensional (3D) reconstructions from cone-beam computed tomography (CBCT) has been particularly important in dentistry, which will affect the effectiveness of diagnosis, treatment plan, and outcome in clinical practice. The aims of this study were to assess the linear, volumetric, and geometric accuracy of 3D reconstructions from CBCT and to investigate the influence of voxel size and CBCT system on the reconstructions results. Methods: Fifty teeth from 18 orthodontic patients were assigned to three groups as NewTom VG 0.15 mm group (NewTom VG; voxel size: 0.15 mm; n = 17), NewTom VG 0.30 mm group (NewTom VG; voxel size: 0.30 mm; n = 16), and VATECH DCTPRO 0.30 mm group (VATECH DCTPRO; voxel size: 0.30 mm; n = 17). The 3D reconstruction models of the teeth were segmented from CBCT data manually using Mimics 18.0 (Materialise Dental, Leuven, Belgium), and the extracted teeth were scanned by 3Shape optical scanner (3Shape A/S, Denmark). Linear and volumetric deviations were separately assessed by comparing the length and volume of the 3D reconstruction model with physical measurement by paired t-test. Geometric deviations were assessed by the root mean square value of the imposed 3D reconstruction and optical models by one-sample t-test. To assess the influence of voxel size and CBCT system on 3D reconstruction, analysis of variance (ANOVA) was used (α = 0.05). Results: The linear, volumetric, and geometric deviations were −0.03 ± 0.48 mm, −5.4 ± 2.8%, and 0.117 ± 0.018 mm for NewTom VG 0.15 mm group; −0.45 ± 0.42 mm, −4.5 ± 3.4%, and 0.116 ± 0.014 mm for NewTom VG 0.30 mm group; and −0.93 ± 0.40 mm, −4.8 ± 5.1%, and 0.194 ± 0.117 mm for VATECH DCTPRO 0.30 mm group, respectively. There were statistically significant differences between groups in terms of linear measurement (P < 0.001), but no significant difference in terms of volumetric measurement (P = 0.774). No statistically significant difference were found on geometric measurement between NewTom VG 0.15 mm and NewTom VG 0.30 mm groups (P = 0.999) while a significant difference was found between VATECH DCTPRO 0.30 mm and NewTom VG 0.30 mm groups (P = 0.006). Conclusions: The 3D reconstruction from CBCT data can achieve a high linear, volumetric, and geometric accuracy. Increasing voxel resolution from 0.30 to 0.15 mm does not result in increased accuracy of 3D tooth reconstruction while different systems can affect the accuracy. PMID:27270544

A voxel-based approach to gray matter asymmetries.

PubMed

Luders, E; Gaser, C; Jancke, L; Schlaug, G

2004-06-01

Voxel-based morphometry (VBM) was used to analyze gray matter (GM) asymmetries in a large sample (n = 60) of male and female professional musicians with and without absolute pitch (AP). We chose to examine these particular groups because previous studies using traditional region-of-interest (ROI) analyses have shown differences in hemispheric asymmetry related to AP and gender. Voxel-based methods may have advantages over traditional ROI-based methods since the analysis can be performed across the whole brain with minimal user bias. After determining that the VBM method was sufficiently sensitive for the detection of differences in GM asymmetries between groups, we found that male AP musicians were more leftward lateralized in the anterior region of the planum temporale (PT) than male non-AP musicians. This confirmed the results of previous studies using ROI-based methods that showed an association between PT asymmetry and the AP phenotype. We further observed that male non-AP musicians revealed an increased leftward GM asymmetry in the postcentral gyrus compared to female non-AP musicians, again corroborating results of a previously published study using ROI-based methods. By analyzing hemispheric GM differences across our entire sample, we were able to partially confirm findings of previous studies using traditional morphometric techniques, as well as more recent, voxel-based analyses. In addition, we found some unusually pronounced GM asymmetries in our musician sample not previously detected in subjects unselected for musical training. Since we were able to validate gender- and AP-related brain asymmetries previously described using traditional ROI-based morphometric techniques, the results of our analyses support the use of VBM for examinations of GM asymmetries.

Sparse network-based models for patient classification using fMRI

PubMed Central

Rosa, Maria J.; Portugal, Liana; Hahn, Tim; Fallgatter, Andreas J.; Garrido, Marta I.; Shawe-Taylor, John; Mourao-Miranda, Janaina

2015-01-01

Pattern recognition applied to whole-brain neuroimaging data, such as functional Magnetic Resonance Imaging (fMRI), has proved successful at discriminating psychiatric patients from healthy participants. However, predictive patterns obtained from whole-brain voxel-based features are difficult to interpret in terms of the underlying neurobiology. Many psychiatric disorders, such as depression and schizophrenia, are thought to be brain connectivity disorders. Therefore, pattern recognition based on network models might provide deeper insights and potentially more powerful predictions than whole-brain voxel-based approaches. Here, we build a novel sparse network-based discriminative modeling framework, based on Gaussian graphical models and L1-norm regularized linear Support Vector Machines (SVM). In addition, the proposed framework is optimized in terms of both predictive power and reproducibility/stability of the patterns. Our approach aims to provide better pattern interpretation than voxel-based whole-brain approaches by yielding stable brain connectivity patterns that underlie discriminative changes in brain function between the groups. We illustrate our technique by classifying patients with major depressive disorder (MDD) and healthy participants, in two (event- and block-related) fMRI datasets acquired while participants performed a gender discrimination and emotional task, respectively, during the visualization of emotional valent faces. PMID:25463459

Reduction of Interhemispheric Functional Brain Connectivity in Early Blindness: A Resting-State fMRI Study

PubMed Central

2017-01-01

Objective The purpose of this study was to investigate the resting-state interhemispheric functional connectivity in early blindness by using voxel-mirrored homotopic connectivity (VMHC). Materials and Methods Sixteen early blind patients (EB group) and sixteen age- and gender-matched sighted control volunteers (SC group) were recruited in this study. We used VMHC to identify brain areas with significant differences in functional connectivity between different groups and used voxel-based morphometry (VBM) to calculate the individual gray matter volume (GMV). Results VMHC analysis showed a significantly lower connectivity in primary visual cortex, visual association cortex, and somatosensory association cortex in EB group compared to sighted controls. Additionally, VBM analysis revealed that GMV was reduced in the left lateral calcarine cortices in EB group compared to sighted controls, while it was increased in the left lateral middle occipital gyri. Statistical analysis showed the duration of blindness negatively correlated with VMHC in the bilateral middle frontal gyri, middle temporal gyri, and inferior temporal gyri. Conclusions Our findings help elucidate the pathophysiological mechanisms of EB. The interhemispheric functional connectivity was impaired in EB patients. Additionally, the middle frontal gyri, middle temporal gyri, and inferior temporal gyri may be potential target regions for rehabilitation. PMID:28656145

Comparison of organs' shapes with geometric and Zernike 3D moments.

PubMed

Broggio, D; Moignier, A; Ben Brahim, K; Gardumi, A; Grandgirard, N; Pierrat, N; Chea, M; Derreumaux, S; Desbrée, A; Boisserie, G; Aubert, B; Mazeron, J-J; Franck, D

2013-09-01

The morphological similarity of organs is studied with feature vectors based on geometric and Zernike 3D moments. It is particularly investigated if outliers and average models can be identified. For this purpose, the relative proximity to the mean feature vector is defined, principal coordinate and clustering analyses are also performed. To study the consistency and usefulness of this approach, 17 livers and 76 hearts voxel models from several sources are considered. In the liver case, models with similar morphological feature are identified. For the limited amount of studied cases, the liver of the ICRP male voxel model is identified as a better surrogate than the female one. For hearts, the clustering analysis shows that three heart shapes represent about 80% of the morphological variations. The relative proximity and clustering analysis rather consistently identify outliers and average models. For the two cases, identification of outliers and surrogate of average models is rather robust. However, deeper classification of morphological feature is subject to caution and can only be performed after cross analysis of at least two kinds of feature vectors. Finally, the Zernike moments contain all the information needed to re-construct the studied objects and thus appear as a promising tool to derive statistical organ shapes. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

Skeletal dosimetry in the MAX06 and the FAX06 phantoms for external exposure to photons based on vertebral 3D-microCT images

NASA Astrophysics Data System (ADS)

Kramer, R.; Khoury, H. J.; Vieira, J. W.; Kawrakow, I.

2006-12-01

3D-microCT images of vertebral bodies from three different individuals have been segmented into trabecular bone, bone marrow and bone surface cells (BSC), and then introduced into the spongiosa voxels of the MAX06 and the FAX06 phantoms, in order to calculate the equivalent dose to the red bone marrow (RBM) and the BSC in the marrow cavities of trabecular bone with the EGSnrc Monte Carlo code from whole-body exposure to external photon radiation. The MAX06 and the FAX06 phantoms consist of about 150 million 1.2 mm cubic voxels each, a part of which are spongiosa voxels surrounded by cortical bone. In order to use the segmented 3D-microCT images for skeletal dosimetry, spongiosa voxels in the MAX06 and the FAX06 phantom were replaced at runtime by so-called micro matrices representing segmented trabecular bone, marrow and BSC in 17.65, 30 and 60 µm cubic voxels. The 3D-microCT image-based RBM and BSC equivalent doses for external exposure to photons presented here for the first time for complete human skeletons are in agreement with the results calculated with the three correction factor method and the fluence-to-dose response functions for the same phantoms taking into account the conceptual differences between the different methods. Additionally the microCT image-based results have been compared with corresponding data from earlier studies for other human phantoms. This article is dedicated to Prof. Dr Guenter Drexler from the Laboratório de Ciências Radiológicas, State University of Rio de Janeiro, on the occasion of his 70th birthday.

Non-invasive quantification of tumour heterogeneity in water diffusivity to differentiate malignant from benign tissues of urinary bladder: a phase I study.

PubMed

Nguyen, Huyen T; Shah, Zarine K; Mortazavi, Amir; Pohar, Kamal S; Wei, Lai; Jia, Guang; Zynger, Debra L; Knopp, Michael V

2017-05-01

To quantify the heterogeneity of the tumour apparent diffusion coefficient (ADC) using voxel-based analysis to differentiate malignancy from benign wall thickening of the urinary bladder. Nineteen patients with histopathological findings of their cystectomy specimen were included. A data set of voxel-based ADC values was acquired for each patient's lesion. Histogram analysis was performed on each data set to calculate uniformity (U) and entropy (E). The k-means clustering of the voxel-wised ADC data set was implemented to measure mean intra-cluster distance (MICD) and largest inter-cluster distance (LICD). Subsequently, U, E, MICD, and LICD for malignant tumours were compared with those for benign lesions using a two-sample t-test. Eleven patients had pathological confirmation of malignancy and eight with benign wall thickening. Histogram analysis showed that malignant tumours had a significantly higher degree of ADC heterogeneity with lower U (P = 0.016) and higher E (P = 0.005) than benign lesions. In agreement with these findings, k-means clustering of voxel-wise ADC indicated that bladder malignancy presented with significantly higher MICD (P < 0.001) and higher LICD (P = 0.002) than benign wall thickening. The quantitative assessment of tumour diffusion heterogeneity using voxel-based ADC analysis has the potential to become a non-invasive tool to distinguish malignant from benign tissues of urinary bladder cancer. • Heterogeneity is an intrinsic characteristic of tumoral tissue. • Non-invasive quantification of tumour heterogeneity can provide adjunctive information to improve cancer diagnosis accuracy. • Histogram analysis and k-means clustering can quantify tumour diffusion heterogeneity. • The quantification helps differentiate malignant from benign urinary bladder tissue.

Support for Anterior Temporal Involvement in Semantic Error Production in Aphasia: New Evidence from VLSM

ERIC Educational Resources Information Center

Walker, Grant M.; Schwartz, Myrna F.; Kimberg, Daniel Y.; Faseyitan, Olufunsho; Brecher, Adelyn; Dell, Gary S.; Coslett, H. Branch

2011-01-01

Semantic errors in aphasia (e.g., naming a horse as "dog") frequently arise from faulty mapping of concepts onto lexical items. A recent study by our group used voxel-based lesion-symptom mapping (VLSM) methods with 64 patients with chronic aphasia to identify voxels that carry an association with semantic errors. The strongest associations were…

An efficient method to predict and include Bragg curve degradation due to lung-equivalent materials in Monte Carlo codes by applying a density modulation

NASA Astrophysics Data System (ADS)

Baumann, Kilian-Simon; Witt, Matthias; Weber, Uli; Engenhart-Cabillic, Rita; Zink, Klemens

2017-05-01

Sub-millimetre-sized heterogeneities such as lung parenchyma cause Bragg peak degradation which can lead to an underdose of the tumor and an overdose of healthy tissue when not accounted for in treatment planning. Since commonly used treatment-planning CTs do not resolve the fine structure of lungs, this degradation can hardly be considered. We present a mathematical model capable of predicting and describing Bragg peak degradation due to a lung-equivalent geometry consisting of sub-millimetre voxels filled with either lung tissue or air. The material characteristic ‘modulation power’ is introduced to quantify the Bragg peak degradation. A strategy was developed to transfer the modulating effects of such fine structures to rougher structures such as 2 mm thick CT voxels, which is the resolution of typically used CTs. This is done by using the modulation power to derive a density distribution applicable to these voxels. By replacing the previously used sub-millimetre voxels by 2 mm thick voxels filled with lung tissue and modulating the lung tissue’s density in each voxel individually, we were able to reproduce the Bragg peak degradation. Hence a solution is found to include Bragg curve degradation due to lung-equivalent materials in Monte Carlo-based treatment-planning systems.

Brain tumor classification and segmentation using sparse coding and dictionary learning.

PubMed

Salman Al-Shaikhli, Saif Dawood; Yang, Michael Ying; Rosenhahn, Bodo

2016-08-01

This paper presents a novel fully automatic framework for multi-class brain tumor classification and segmentation using a sparse coding and dictionary learning method. The proposed framework consists of two steps: classification and segmentation. The classification of the brain tumors is based on brain topology and texture. The segmentation is based on voxel values of the image data. Using K-SVD, two types of dictionaries are learned from the training data and their associated ground truth segmentation: feature dictionary and voxel-wise coupled dictionaries. The feature dictionary consists of global image features (topological and texture features). The coupled dictionaries consist of coupled information: gray scale voxel values of the training image data and their associated label voxel values of the ground truth segmentation of the training data. For quantitative evaluation, the proposed framework is evaluated using different metrics. The segmentation results of the brain tumor segmentation (MICCAI-BraTS-2013) database are evaluated using five different metric scores, which are computed using the online evaluation tool provided by the BraTS-2013 challenge organizers. Experimental results demonstrate that the proposed approach achieves an accurate brain tumor classification and segmentation and outperforms the state-of-the-art methods.

a Super Voxel-Based Riemannian Graph for Multi Scale Segmentation of LIDAR Point Clouds

NASA Astrophysics Data System (ADS)

Li, Minglei

2018-04-01

Automatically segmenting LiDAR points into respective independent partitions has become a topic of great importance in photogrammetry, remote sensing and computer vision. In this paper, we cast the problem of point cloud segmentation as a graph optimization problem by constructing a Riemannian graph. The scale space of the observed scene is explored by an octree-based over-segmentation with different depths. The over-segmentation produces many super voxels which restrict the structure of the scene and will be used as nodes of the graph. The Kruskal coordinates are used to compute edge weights that are proportional to the geodesic distance between nodes. Then we compute the edge-weight matrix in which the elements reflect the sectional curvatures associated with the geodesic paths between super voxel nodes on the scene surface. The final segmentation results are generated by clustering similar super voxels and cutting off the weak edges in the graph. The performance of this method was evaluated on LiDAR point clouds for both indoor and outdoor scenes. Additionally, extensive comparisons to state of the art techniques show that our algorithm outperforms on many metrics.

Semi-automated method to measure pneumonia severity in mice through computed tomography (CT) scan analysis

NASA Astrophysics Data System (ADS)

Johri, Ansh; Schimel, Daniel; Noguchi, Audrey; Hsu, Lewis L.

2010-03-01

Imaging is a crucial clinical tool for diagnosis and assessment of pneumonia, but quantitative methods are lacking. Micro-computed tomography (micro CT), designed for lab animals, provides opportunities for non-invasive radiographic endpoints for pneumonia studies. HYPOTHESIS: In vivo micro CT scans of mice with early bacterial pneumonia can be scored quantitatively by semiautomated imaging methods, with good reproducibility and correlation with bacterial dose inoculated, pneumonia survival outcome, and radiologists' scores. METHODS: Healthy mice had intratracheal inoculation of E. coli bacteria (n=24) or saline control (n=11). In vivo micro CT scans were performed 24 hours later with microCAT II (Siemens). Two independent radiologists scored the extent of airspace abnormality, on a scale of 0 (normal) to 24 (completely abnormal). Using the Amira 5.2 software (Mercury Computer Systems), a histogram distribution of voxel counts between the Hounsfield range of -510 to 0 was created and analyzed, and a segmentation procedure was devised. RESULTS: A t-test was performed to determine whether there was a significant difference in the mean voxel value of each mouse in the three experimental groups: Saline Survivors, Pneumonia Survivors, and Pneumonia Non-survivors. It was found that the voxel count method was able to statistically tell apart the Saline Survivors from the Pneumonia Survivors, the Saline Survivors from the Pneumonia Non-survivors, but not the Pneumonia Survivors vs. Pneumonia Non-survivors. The segmentation method, however, was successfully able to distinguish the two Pneumonia groups. CONCLUSION: We have pilot-tested an evaluation of early pneumonia in mice using micro CT and a semi-automated method for lung segmentation and scoring system. Statistical analysis indicates that the system is reliable and merits further evaluation.

Statistical modeling and MAP estimation for body fat quantification with MRI ratio imaging

NASA Astrophysics Data System (ADS)

Wong, Wilbur C. K.; Johnson, David H.; Wilson, David L.

2008-03-01

We are developing small animal imaging techniques to characterize the kinetics of lipid accumulation/reduction of fat depots in response to genetic/dietary factors associated with obesity and metabolic syndromes. Recently, we developed an MR ratio imaging technique that approximately yields lipid/{lipid + water}. In this work, we develop a statistical model for the ratio distribution that explicitly includes a partial volume (PV) fraction of fat and a mixture of a Rician and multiple Gaussians. Monte Carlo hypothesis testing showed that our model was valid over a wide range of coefficient of variation of the denominator distribution (c.v.: 0-0:20) and correlation coefficient among the numerator and denominator (ρ 0-0.95), which cover the typical values that we found in MRI data sets (c.v.: 0:027-0:063, ρ: 0:50-0:75). Then a maximum a posteriori (MAP) estimate for the fat percentage per voxel is proposed. Using a digital phantom with many PV voxels, we found that ratio values were not linearly related to PV fat content and that our method accurately described the histogram. In addition, the new method estimated the ground truth within +1.6% vs. +43% for an approach using an uncorrected ratio image, when we simply threshold the ratio image. On the six genetically obese rat data sets, the MAP estimate gave total fat volumes of 279 +/- 45mL, values 21% smaller than those from the uncorrected ratio images, principally due to the non-linear PV effect. We conclude that our algorithm can increase the accuracy of fat volume quantification even in regions having many PV voxels, e.g. ectopic fat depots.

Differences in Brain Morphological Findings between Narcolepsy with and without Cataplexy

PubMed Central

Nakamura, Masaki; Nishida, Shingo; Hayashida, Kenichi; Ueki, Yoichiro; Dauvilliers, Yves; Inoue, Yuichi

2013-01-01

Objective Maps of fractional anisotropy (FA) and apparent diffusion coefficient (ADC) obtained by diffusion tensor imaging (DTI) can detect microscopic axonal changes by estimating the diffusivity of water molecules using magnetic resonance imaging (MRI). We applied an MRI voxel-based statistical approach to FA and ADC maps to evaluate microstructural abnormalities in the brain in narcolepsy and to investigate differences between patients having narcolepsy with and without cataplexy. Methods Twelve patients with drug-naive narcolepsy with cataplexy (NA/CA), 12 with drug-naive narcolepsy without cataplexy (NA w/o CA) and 12 age-matched healthy normal controls (NC) were enrolled. FA and ADC maps for these 3 groups were statistically compared by using voxel-based one-way ANOVA. In addition, we investigated the correlation between FA and ADC values and clinical variables in the patient groups. Results Compared to the NC group, the NA/CA group showed higher ADC values in the left inferior frontal gyrus and left amygdala, and a lower ADC value in the left postcentral gyrus. The ADC value in the right inferior frontal gyrus and FA value in the right precuneus were higher for NA/CA group than for the NA w/o CA group. However, no significant differences were observed in FA and ADC values between the NA w/o CA and NC groups in any of the areas investigated. In addition, no correlation was found between the clinical variables and ADC and FA values of any brain areas in these patient groups. Conclusions Several microstructural changes were noted in the inferior frontal gyrus and amygdala in the NA/CA but not in the NA w/o CA group. These findings suggest that these 2 narcolepsy conditions have different pathological mechanisms: narcolepsy without cataplexy form appears to be a potentially broader condition without any significant brain imaging differences from normal controls. PMID:24312261

Reduced gray matter volume in the anterior cingulate, orbitofrontal cortex and thalamus as a function of mild depressive symptoms: a voxel-based morphometric analysis.

PubMed

Webb, C A; Weber, M; Mundy, E A; Killgore, W D S

2014-10-01

Studies investigating structural brain abnormalities in depression have typically employed a categorical rather than dimensional approach to depression [i.e., comparing subjects with Diagnostic and Statistical Manual of Mental Disorders (DSM)-defined major depressive disorder (MDD) v. healthy controls]. The National Institute of Mental Health, through their Research Domain Criteria initiative, has encouraged a dimensional approach to the study of psychopathology as opposed to an over-reliance on categorical (e.g., DSM-based) diagnostic approaches. Moreover, subthreshold levels of depressive symptoms (i.e., severity levels below DSM criteria) have been found to be associated with a range of negative outcomes, yet have been relatively neglected in neuroimaging research. To examine the extent to which depressive symptoms--even at subclinical levels--are linearly related to gray matter volume reductions in theoretically important brain regions, we employed whole-brain voxel-based morphometry in a sample of 54 participants. The severity of mild depressive symptoms, even in a subclinical population, was associated with reduced gray matter volume in the orbitofrontal cortex, anterior cingulate, thalamus, superior temporal gyrus/temporal pole and superior frontal gyrus. A conjunction analysis revealed concordance across two separate measures of depression. Reduced gray matter volume in theoretically important brain regions can be observed even in a sample that does not meet DSM criteria for MDD, but who nevertheless report relatively elevated levels of depressive symptoms. Overall, these findings highlight the need for additional research using dimensional conceptual and analytic approaches, as well as further investigation of subclinical populations.

What’s special about task in dystonia? A voxel-based morphometry and diffusion weighted imaging study

PubMed Central

Ramdhani, Ritesh A.; Kumar, Veena; Velickovic, Miodrag; Frucht, Steven J.; Tagliati, Michele; Simonyan, Kristina

2014-01-01

Background Numerous brain imaging studies have demonstrated structural changes in the basal ganglia, thalamus, sensorimotor cortex and cerebellum across different forms of primary dystonia. However, our understanding of brain abnormalities contributing to the clinically well-described phenomenon of task-specificity in dystonia remained limited. Methods We used high-resolution MRI with voxel-based morphometry and diffusion tensor imaging with tract-based spatial statistics of fractional anisotropy to examine gray and white matter organization in two task-specific dystonia forms, writer’s cramp and laryngeal dystonia, and two non-task-specific dystonia forms, cervical dystonia and blepharospasm. Results A direct comparison between the both dystonia forms revealed that characteristic gray matter volumetric changes in task-specific dystonia involve the brain regions responsible for sensorimotor control during writing and speaking, such as primary somatosensory cortex, middle frontal gyrus, superior/inferior temporal gyrus, middle/posterior cingulate cortex, occipital cortex as well as the striatum and cerebellum (lobules VI-VIIa). These gray matter changes were accompanied by white matter abnormalities in the premotor cortex, middle/inferior frontal gyrus, genu of the corpus callosum, anterior limb/genu of the internal capsule, and putamen. Conversely, gray matter volumetric changes in non-task-specific group were limited to the left cerebellum (lobule VIIa) only, while white matter alterations were found to underlie the primary sensorimotor cortex, inferior parietal lobule and middle cingulate gyrus. Conclusion Distinct microstructural patterns in task-specific and non-task-specific dystonias may represent neuroimaging markers and provide evidence that these two dystonia subclasses likely follow divergent pathophysiological mechanisms precipitated by different triggers. PMID:24925463

Influence of magnetic field strength and image registration strategy on voxel-based morphometry in a study of Alzheimer's disease.

PubMed

Marchewka, Artur; Kherif, Ferath; Krueger, Gunnar; Grabowska, Anna; Frackowiak, Richard; Draganski, Bogdan

2014-05-01

Multi-centre data repositories like the Alzheimer's Disease Neuroimaging Initiative (ADNI) offer a unique research platform, but pose questions concerning comparability of results when using a range of imaging protocols and data processing algorithms. The variability is mainly due to the non-quantitative character of the widely used structural T1-weighted magnetic resonance (MR) images. Although the stability of the main effect of Alzheimer's disease (AD) on brain structure across platforms and field strength has been addressed in previous studies using multi-site MR images, there are only sparse empirically-based recommendations for processing and analysis of pooled multi-centre structural MR data acquired at different magnetic field strengths (MFS). Aiming to minimise potential systematic bias when using ADNI data we investigate the specific contributions of spatial registration strategies and the impact of MFS on voxel-based morphometry in AD. We perform a whole-brain analysis within the framework of Statistical Parametric Mapping, testing for main effects of various diffeomorphic spatial registration strategies, of MFS and their interaction with disease status. Beyond the confirmation of medial temporal lobe volume loss in AD, we detect a significant impact of spatial registration strategy on estimation of AD related atrophy. Additionally, we report a significant effect of MFS on the assessment of brain anatomy (i) in the cerebellum, (ii) the precentral gyrus and (iii) the thalamus bilaterally, showing no interaction with the disease status. We provide empirical evidence in support of pooling data in multi-centre VBM studies irrespective of disease status or MFS. Copyright © 2013 Wiley Periodicals, Inc.

Diffusion tensor imaging and voxel based morphometry study in amyotrophic lateral sclerosis: relationships with motor disability

PubMed Central

Thivard, Lionel; Pradat, Pierre‐François; Lehéricy, Stéphane; Lacomblez, Lucette; Dormont, Didier; Chiras, Jacques; Benali, Habib; Meininger, Vincent

2007-01-01

The aim of this study was to investigate the extent of cortical and subcortical lesions in amyotrophic lateral sclerosis (ALS) using, in combination, voxel based diffusion tensor imaging (DTI) and voxel based morphometry (VBM). We included 15 patients with definite or probable ALS and 25 healthy volunteers. Patients were assessed using the revised ALS Functional Rating Scale (ALSFRS‐R). In patients, reduced fractional anisotropy was found in bilateral corticospinal tracts, the left insula/ventrolateral premotor cortex, the right parietal cortex and the thalamus, which correlated with the ALSFRS‐R. Increased mean diffusivity (MD) was found bilaterally in the motor cortex, the ventrolateral premotor cortex/insula, the hippocampal formations and the right superior temporal gyrus, which did not correlate with the ALSFRS‐R. VBM analysis showed no changes in white matter but widespread volume decreases in grey matter in several regions exhibiting MD abnormalities. In ALS patients, our results show that subcortical lesions extend beyond the corticospinal tract and are clinically relevant. PMID:17635981

Diffusion tensor imaging and voxel based morphometry study in amyotrophic lateral sclerosis: relationships with motor disability.

PubMed

Thivard, Lionel; Pradat, Pierre-François; Lehéricy, Stéphane; Lacomblez, Lucette; Dormont, Didier; Chiras, Jacques; Benali, Habib; Meininger, Vincent

2007-08-01

The aim of this study was to investigate the extent of cortical and subcortical lesions in amyotrophic lateral sclerosis (ALS) using, in combination, voxel based diffusion tensor imaging (DTI) and voxel based morphometry (VBM). We included 15 patients with definite or probable ALS and 25 healthy volunteers. Patients were assessed using the revised ALS Functional Rating Scale (ALSFRS-R). In patients, reduced fractional anisotropy was found in bilateral corticospinal tracts, the left insula/ventrolateral premotor cortex, the right parietal cortex and the thalamus, which correlated with the ALSFRS-R. Increased mean diffusivity (MD) was found bilaterally in the motor cortex, the ventrolateral premotor cortex/insula, the hippocampal formations and the right superior temporal gyrus, which did not correlate with the ALSFRS-R. VBM analysis showed no changes in white matter but widespread volume decreases in grey matter in several regions exhibiting MD abnormalities. In ALS patients, our results show that subcortical lesions extend beyond the corticospinal tract and are clinically relevant.

Brain magnetic resonance imaging CO2 stress testing in adolescent postconcussion syndrome.

PubMed

Mutch, W Alan C; Ellis, Michael J; Ryner, Lawrence N; Ruth Graham, M; Dufault, Brenden; Gregson, Brian; Hall, Thomas; Bunge, Martin; Essig, Marco; Fisher, Joseph A; Duffin, James; Mikulis, David J

2016-09-01

OBJECT A neuroimaging assessment tool to visualize global and regional impairments in cerebral blood flow (CBF) and cerebrovascular responsiveness in individual patients with concussion remains elusive. Here the authors summarize the safety, feasibility, and results of brain CO2 stress testing in adolescents with postconcussion syndrome (PCS) and healthy controls. METHODS This study was approved by the Biomedical Research Ethics Board at the University of Manitoba. Fifteen adolescents with PCS and 17 healthy control subjects underwent anatomical MRI, pseudo-continuous arterial spin labeling MRI, and brain stress testing using controlled CO2 challenge and blood oxygen level-dependent (BOLD) MRI. Post hoc processing was performed using statistical parametric mapping to determine voxel-by-voxel regional resting CBF and cerebrovascular responsiveness of the brain to the CO2 stimulus (increase in BOLD signal) or the inverse (decrease in BOLD signal). Receiver operating characteristic (ROC) curves were generated to compare voxel counts categorized by control (0) or PCS (1). RESULTS Studies were well tolerated without any serious adverse events. Anatomical MRI was normal in all study participants. No differences in CO2 stimuli were seen between the 2 participant groups. No group differences in global mean CBF were detected between PCS patients and healthy controls. Patient-specific differences in mean regional CBF and CO2 BOLD responsiveness were observed in all PCS patients. The ROC curve analysis for brain regions manifesting a voxel response greater than and less than the control atlas (that is, abnormal voxel counts) produced an area under the curve of 0.87 (p < 0.0001) and 0.80 (p = 0.0003), respectively, consistent with a clinically useful predictive model. CONCLUSIONS Adolescent PCS is associated with patient-specific abnormalities in regional mean CBF and BOLD cerebrovascular responsiveness that occur in the setting of normal global resting CBF. Future prospective studies are warranted to examine the utility of brain MRI CO2 stress testing in the longitudinal assessment of acute sports-related concussion and PCS.

Alexithymia is related to differences in gray matter volume: a voxel-based morphometry study.

PubMed

Ihme, Klas; Dannlowski, Udo; Lichev, Vladimir; Stuhrmann, Anja; Grotegerd, Dominik; Rosenberg, Nicole; Kugel, Harald; Heindel, Walter; Arolt, Volker; Kersting, Anette; Suslow, Thomas

2013-01-23

Alexithymia has been characterized as the inability to identify and describe feelings. Functional imaging studies have revealed that alexithymia is linked to reactivity changes in emotion- and face-processing-relevant brain areas. In this respect, anterior cingulate cortex (ACC), amygdala, anterior insula and fusiform gyrus (FFG) have been consistently reported. However, it remains to be clarified whether alexithymia is also associated with structural differences. Voxel-based morphometry on T1-weighted magnetic resonance images was used to investigate gray matter volume in 17 high alexithymics (HA) and 17 gender-matched low alexithymics (LA), which were selected from a sample of 161 healthy volunteers on basis of the 20-item Toronto Alexithymia Scale. Data were analyzed as statistic parametric maps for the comparisons LA>HA and HA>LA in a priori determined regions of interests (ROIs), i.e., ACC, amygdala, anterior insula and FFG. Moreover, an exploratory whole brain analysis was accomplished. For the contrast LA>HA, significant clusters were detected in the ACC, left amygdala and left anterior insula. Additionally, the whole brain analysis revealed volume differences in the left middle temporal gyrus. No significant differences were found for the comparison HA>LA. Our findings suggest that high compared to low alexithymics show less gray matter volume in several emotion-relevant brain areas. These structural differences might contribute to the functional alterations found in previous imaging studies in alexithymia. Copyright © 2012 Elsevier B.V. All rights reserved.

Neuropsychological and FDG-PET profiles in VGKC autoimmune limbic encephalitis.

PubMed

Dodich, Alessandra; Cerami, Chiara; Iannaccone, Sandro; Marcone, Alessandra; Alongi, Pierpaolo; Crespi, Chiara; Canessa, Nicola; Andreetta, Francesca; Falini, Andrea; Cappa, Stefano F; Perani, Daniela

2016-10-01

Limbic encephalitis (LE) is characterized by an acute or subacute onset with memory impairments, confusional state, behavioral disorders, variably associated with seizures and dystonic movements. It is due to inflammatory processes that selectively affect the medial temporal lobe structures. Voltage-gate potassium channel (VGKC) autoantibodies are frequently observed. In this study, we assessed at the individual level FDG-PET brain metabolic dysfunctions and neuropsychological profiles in three autoimmune LE cases seropositive for neuronal VGKC-complex autoantibodies. LGI1 and CASPR2 potassium channel complex autoantibody subtyping was performed. Cognitive abilities were evaluated with an in-depth neuropsychological battery focused on episodic memory and affective recognition/processing skills. FDG-PET data were analyzed at single-subject level according to a standardized and validated voxel-based Statistical Parametric Mapping (SPM) method. Patients showed severe episodic memory and fear recognition deficits at the neuropsychological assessment. No disorder of mentalizing processing was present. Variable patterns of increases and decreases of brain glucose metabolism emerged in the limbic structures, highlighting the pathology-driven selective vulnerability of this system. Additional involvement of cortical and subcortical regions, particularly in the sensorimotor system and basal ganglia, was found. Episodic memory and fear recognition deficits characterize the cognitive profile of LE. Commonalities and differences may occur in the brain metabolic patterns. Single-subject voxel-based analysis of FDG-PET imaging could be useful in the early detection of the metabolic correlates of cognitive and non-cognitive deficits characterizing LE condition. Copyright © 2016 Elsevier Inc. All rights reserved.

Voxel-based morphometric multisite collaborative study on schizophrenia.

PubMed

Segall, Judith M; Turner, Jessica A; van Erp, Theo G M; White, Tonya; Bockholt, H Jeremy; Gollub, Randy L; Ho, Beng C; Magnotta, Vince; Jung, Rex E; McCarley, Robert W; Schulz, S Charles; Lauriello, John; Clark, Vince P; Voyvodic, James T; Diaz, Michele T; Calhoun, Vince D

2009-01-01

Regional gray matter (GM) abnormalities are well known to exist in patients with chronic schizophrenia. Voxel-based morphometry (VBM) has been previously used on structural magnetic resonance images (MRI) data to characterize these abnormalities. Two multisite schizophrenia studies, the Functional Biomedical Informatics Research Network and the Mind Clinical Imaging Consortium, which include 9 data collection sites, are evaluating the efficacy of pooling structural imaging data across imaging centers. Such a pooling of data could yield the increased statistical power needed to elucidate effects that may not be seen with smaller samples. VBM analyses were performed to evaluate the consistency of patient versus control gray matter concentration (GMC) differences across the study sites, as well as the effects of combining multisite data. Integration of data from both studies yielded a large sample of 503 subjects, including 266 controls and 237 patients diagnosed with schizophrenia, schizoaffective or schizophreniform disorder. The data were analyzed using the combined sample, as well as analyzing each of the 2 multisite studies separately. A consistent pattern of reduced relative GMC in schizophrenia patients compared with controls was found across all study sites. Imaging center-specific effects were evaluated using a region of interest analysis. Overall, the findings support the use of VBM in combined multisite studies. This analysis of schizophrenics and controls from around the United States provides continued supporting evidence for GM deficits in the temporal lobes, anterior cingulate, and frontal regions in patients with schizophrenia spectrum disorders.

A Lidar Point Cloud Based Procedure for Vertical Canopy Structure Analysis And 3D Single Tree Modelling in Forest

PubMed Central

Wang, Yunsheng; Weinacker, Holger; Koch, Barbara

2008-01-01

A procedure for both vertical canopy structure analysis and 3D single tree modelling based on Lidar point cloud is presented in this paper. The whole area of research is segmented into small study cells by a raster net. For each cell, a normalized point cloud whose point heights represent the absolute heights of the ground objects is generated from the original Lidar raw point cloud. The main tree canopy layers and the height ranges of the layers are detected according to a statistical analysis of the height distribution probability of the normalized raw points. For the 3D modelling of individual trees, individual trees are detected and delineated not only from the top canopy layer but also from the sub canopy layer. The normalized points are resampled into a local voxel space. A series of horizontal 2D projection images at the different height levels are then generated respect to the voxel space. Tree crown regions are detected from the projection images. Individual trees are then extracted by means of a pre-order forest traversal process through all the tree crown regions at the different height levels. Finally, 3D tree crown models of the extracted individual trees are reconstructed. With further analyses on the 3D models of individual tree crowns, important parameters such as crown height range, crown volume and crown contours at the different height levels can be derived. PMID:27879916

Complex Regional Pain Syndrome Type I Affects Brain Structure in Prefrontal and Motor Cortex

PubMed Central

Pleger, Burkhard; Draganski, Bogdan; Schwenkreis, Peter; Lenz, Melanie; Nicolas, Volkmar; Maier, Christoph; Tegenthoff, Martin

2014-01-01

The complex regional pain syndrome (CRPS) is a rare but debilitating pain disorder that mostly occurs after injuries to the upper limb. A number of studies indicated altered brain function in CRPS, whereas possible influences on brain structure remain poorly investigated. We acquired structural magnetic resonance imaging data from CRPS type I patients and applied voxel-by-voxel statistics to compare white and gray matter brain segments of CRPS patients with matched controls. Patients and controls were statistically compared in two different ways: First, we applied a 2-sample ttest to compare whole brain white and gray matter structure between patients and controls. Second, we aimed to assess structural alterations specifically of the primary somatosensory (S1) and motor cortex (M1) contralateral to the CRPS affected side. To this end, MRI scans of patients with left-sided CRPS (and matched controls) were horizontally flipped before preprocessing and region-of-interest-based group comparison. The unpaired ttest of the “non-flipped” data revealed that CRPS patients presented increased gray matter density in the dorsomedial prefrontal cortex. The same test applied to the “flipped” data showed further increases in gray matter density, not in the S1, but in the M1 contralateral to the CRPS-affected limb which were inversely related to decreased white matter density of the internal capsule within the ipsilateral brain hemisphere. The gray-white matter interaction between motor cortex and internal capsule suggests compensatory mechanisms within the central motor system possibly due to motor dysfunction. Altered gray matter structure in dorsomedial prefrontal cortex may occur in response to emotional processes such as pain-related suffering or elevated analgesic top-down control. PMID:24416397

Inferring brain-computational mechanisms with models of activity measurements

PubMed Central

Diedrichsen, Jörn

2016-01-01

High-resolution functional imaging is providing increasingly rich measurements of brain activity in animals and humans. A major challenge is to leverage such data to gain insight into the brain's computational mechanisms. The first step is to define candidate brain-computational models (BCMs) that can perform the behavioural task in question. We would then like to infer which of the candidate BCMs best accounts for measured brain-activity data. Here we describe a method that complements each BCM by a measurement model (MM), which simulates the way the brain-activity measurements reflect neuronal activity (e.g. local averaging in functional magnetic resonance imaging (fMRI) voxels or sparse sampling in array recordings). The resulting generative model (BCM-MM) produces simulated measurements. To avoid having to fit the MM to predict each individual measurement channel of the brain-activity data, we compare the measured and predicted data at the level of summary statistics. We describe a novel particular implementation of this approach, called probabilistic representational similarity analysis (pRSA) with MMs, which uses representational dissimilarity matrices (RDMs) as the summary statistics. We validate this method by simulations of fMRI measurements (locally averaging voxels) based on a deep convolutional neural network for visual object recognition. Results indicate that the way the measurements sample the activity patterns strongly affects the apparent representational dissimilarities. However, modelling of the measurement process can account for these effects, and different BCMs remain distinguishable even under substantial noise. The pRSA method enables us to perform Bayesian inference on the set of BCMs and to recognize the data-generating model in each case. This article is part of the themed issue ‘Interpreting BOLD: a dialogue between cognitive and cellular neuroscience’. PMID:27574316

Disconnection as a mechanism for social cognition impairment in multiple sclerosis.

PubMed

Batista, Sonia; Alves, Carolina; d'Almeida, Otília C; Afonso, Ana; Félix-Morais, Ricardo; Pereira, João; Macário, Carmo; Sousa, Lívia; Castelo-Branco, Miguel; Santana, Isabel; Cunha, Luís

2017-07-04

To assess the contribution of microstructural normal-appearing white matter (NAWM) damage to social cognition impairment, specifically in the theory of mind (ToM), in multiple sclerosis (MS). We enrolled consecutively 60 patients with MS and 60 healthy controls (HC) matched on age, sex, and education level. All participants underwent ToM testing (Eyes Test, Videos Test) and 3T brain MRI including conventional and diffusion tensor imaging sequences. Tract-based spatial statistics (TBSS) were applied for whole-brain voxel-wise analysis of fractional anisotropy (FA) and mean diffusivity (MD) on NAWM. Patients with MS performed worse on both tasks of ToM compared to HC (Eyes Test 58.7 ± 13.8 vs 81.9 ± 10.4, p < 0.001, Hedges g -1.886; Videos Test 75.3 ± 9.3 vs 88.1 ± 7.1, p < 0.001, Hedges g -1.537). Performance on ToM tests was correlated with higher values of FA and lower values of MD across widespread white matter tracts. The largest effects (≥90% of voxels with statistical significance) for the Eyes Test were body and genu of corpus callosum, fornix, tapetum, uncinate fasciculus, and left inferior cerebellar peduncle, and for the Videos Test genu and splenium of corpus callosum, fornix, uncinate fasciculus, left tapetum, and right superior fronto-occipital fasciculus. These results indicate that a diffuse pattern of NAWM damage in MS contributes to social cognition impairment in the ToM domain, probably due to a mechanism of disconnection within the social brain network. Gray matter pathology is also expected to have an important role; thus further research is required to clarify the neural basis of social cognition impairment in MS. © 2017 American Academy of Neurology.

Neurochemical evaluation of brain function with 1H magnetic resonance spectroscopy in patients with fragile X syndrome.

PubMed

Utine, G E; Akpınar, B; Arslan, U; Kiper, P Ö Ş; Volkan-Salancı, B; Alanay, Y; Aktaş, D; Haliloğlu, G; Oğuz, K K; Boduroğlu, K; Alikaşifoğlu, M

2014-01-01

Fragile X syndrome (FXS) is the most common hereditary disorder of intellectual disability. Cognitive deficits involve executive function, attention, learning and memory. Advanced neuroimaging techniques are available, and (1)H magnetic resonance spectroscopy (MRS) can be used as a complementary method to MR imaging to understand disease processes in brain, by in vivo demonstration of brain metabolites. MRS was performed in 13 male patients with FXS full mutation, and 13 age- and sex-matched healthy controls. FXS diagnosis was based on clinical evaluation, followed by detection of FMR1 full mutation. Axial T2 TSE, sagittal T1 SE and coronal 3D MPRAGE images were obtained for both morphological imaging and voxel localization. Following evaluation of conventional images, multivoxel MRS (CSI) through supraventricular white matter and single voxel MRS (svs) with an intermediate echo time (TE:135 ms) from the cerebellar vermis were performed. Choline/Creatine (Cho/Cr), N-acetyl aspartate/Creatine (NAA/Cr), and Choline/N-acetyl aspartate (Cho/NAA) ratios were examined at right frontal (RF), left frontal (LF), right parietal (RP), left parietal (LP), and cerebellar vermian (C) white matter. Statistical analyses were done using t-test and Mann-Whitney U tests. A statistically significant difference was observed in RP Cho/NAA ratio (cell membrane marker/neuroaxonal marker), FXS patients having lower levels than controls (P = 0.016). The results should be evaluated cautiously in parallel to consequences in brain metabolism leading to alterations in neurotransmitter levels, osmoregulation, energy metabolism and oxidative stress response described in animal models. MRS may serve to define a metabolic signature and biomarkers associated with FXS. © 2013 Wiley Periodicals, Inc.

Sensitivity and Specificity of Interictal EEG-fMRI for Detecting the Ictal Onset Zone at Different Statistical Thresholds

PubMed Central

Tousseyn, Simon; Dupont, Patrick; Goffin, Karolien; Sunaert, Stefan; Van Paesschen, Wim

2014-01-01

There is currently a lack of knowledge about electroencephalography (EEG)-functional magnetic resonance imaging (fMRI) specificity. Our aim was to define sensitivity and specificity of blood oxygen level dependent (BOLD) responses to interictal epileptic spikes during EEG-fMRI for detecting the ictal onset zone (IOZ). We studied 21 refractory focal epilepsy patients who had a well-defined IOZ after a full presurgical evaluation and interictal spikes during EEG-fMRI. Areas of spike-related BOLD changes overlapping the IOZ in patients were considered as true positives; if no overlap was found, they were treated as false-negatives. Matched healthy case-controls had undergone similar EEG-fMRI in order to determine true-negative and false-positive fractions. The spike-related regressor of the patient was used in the design matrix of the healthy case-control. Suprathreshold BOLD changes in the brain of controls were considered as false positives, absence of these changes as true negatives. Sensitivity and specificity were calculated for different statistical thresholds at the voxel level combined with different cluster size thresholds and represented in receiver operating characteristic (ROC)-curves. Additionally, we calculated the ROC-curves based on the cluster containing the maximal significant activation. We achieved a combination of 100% specificity and 62% sensitivity, using a Z-threshold in the interval 3.4–3.5 and cluster size threshold of 350 voxels. We could obtain higher sensitivity at the expense of specificity. Similar performance was found when using the cluster containing the maximal significant activation. Our data provide a guideline for different EEG-fMRI settings with their respective sensitivity and specificity for detecting the IOZ. The unique cluster containing the maximal significant BOLD activation was a sensitive and specific marker of the IOZ. PMID:25101049

The application of statistical parametric mapping to 123I-FP-CIT SPECT in dementia with Lewy bodies, Alzheimer's disease and Parkinson's disease.

PubMed

Colloby, Sean J; O'Brien, John T; Fenwick, John D; Firbank, Michael J; Burn, David J; McKeith, Ian G; Williams, E David

2004-11-01

Dopaminergic loss can be visualised using (123)I-FP-CIT single photon emission computed tomography (SPECT) in several disorders including Parkinson's disease (PD) and dementia with Lewy bodies (DLB). Most previous SPECT studies have adopted region of interest (ROI) methods for analysis, which are subjective and operator-dependent. The purpose of this study was to investigate differences in striatal binding of (123)I-FP-CIT SPECT using the automated technique of statistical parametric mapping (SPM99) in subjects with DLB, Alzheimer's disease (AD), PD and healthy age-matched controls. This involved spatial normalisation of each subject's image to a customised template, followed by smoothing and intensity normalisation of each image to its corresponding mean occipital count per voxel. Group differences were assessed using a two-sample t test. Applying a height threshold of P

Correction of partial volume effect in (18)F-FDG PET brain studies using coregistered MR volumes: voxel based analysis of tracer uptake in the white matter.

PubMed

Coello, Christopher; Willoch, Frode; Selnes, Per; Gjerstad, Leif; Fladby, Tormod; Skretting, Arne

2013-05-15

A voxel-based algorithm to correct for partial volume effect in PET brain volumes is presented. This method (named LoReAn) is based on MRI based segmentation of anatomical regions and accurate measurements of the effective point spread function of the PET imaging process. The objective is to correct for the spill-out of activity from high-uptake anatomical structures (e.g. grey matter) into low-uptake anatomical structures (e.g. white matter) in order to quantify physiological uptake in the white matter. The new algorithm is presented and validated against the state of the art region-based geometric transfer matrix (GTM) method with synthetic and clinical data. Using synthetic data, both bias and coefficient of variation were improved in the white matter region using LoReAn compared to GTM. An increased number of anatomical regions doesn't affect the bias (<5%) and misregistration affects equally LoReAn and GTM algorithms. The LoReAn algorithm appears to be a simple and promising voxel-based algorithm for studying metabolism in white matter regions. Copyright © 2013 Elsevier Inc. All rights reserved.

Statistical parametric mapping of LORETA using high density EEG and individual MRI: application to mismatch negativities in schizophrenia.

PubMed

Park, Hae-Jeong; Kwon, Jun Soo; Youn, Tak; Pae, Ji Soo; Kim, Jae-Jin; Kim, Myung-Sun; Ha, Kyoo-Seob

2002-11-01

We describe a method for the statistical parametric mapping of low resolution electromagnetic tomography (LORETA) using high-density electroencephalography (EEG) and individual magnetic resonance images (MRI) to investigate the characteristics of the mismatch negativity (MMN) generators in schizophrenia. LORETA, using a realistic head model of the boundary element method derived from the individual anatomy, estimated the current density maps from the scalp topography of the 128-channel EEG. From the current density maps that covered the whole cortical gray matter (up to 20,000 points), volumetric current density images were reconstructed. Intensity normalization of the smoothed current density images was used to reduce the confounding effect of subject specific global activity. After transforming each image into a standard stereotaxic space, we carried out statistical parametric mapping of the normalized current density images. We applied this method to the source localization of MMN in schizophrenia. The MMN generators, produced by a deviant tone of 1,200 Hz (5% of 1,600 trials) under the standard tone of 1,000 Hz, 80 dB binaural stimuli with 300 msec of inter-stimulus interval, were measured in 14 right-handed schizophrenic subjects and 14 age-, gender-, and handedness-matched controls. We found that the schizophrenic group exhibited significant current density reductions of MMN in the left superior temporal gyrus and the left inferior parietal gyrus (P < 0. 0005). This study is the first voxel-by-voxel statistical mapping of current density using individual MRI and high-density EEG. Copyright 2002 Wiley-Liss, Inc.

Effects of Hormone Therapy on Brain Volumes Changes of Postmenopausal Women Revealed by Optimally-Discriminative Voxel-Based Morphometry

PubMed Central

Zhang, Tianhao; Casanova, Ramon; Resnick, Susan M.; Manson, JoAnn E.; Baker, Laura D.; Padual, Claudia B.; Kuller, Lewis H.; Bryan, R. Nick; Espeland, Mark A.; Davatzikos, Christos

2016-01-01

Backgrounds The Women's Health Initiative Memory Study Magnetic Resonance Imaging (WHIMS-MRI) provides an opportunity to evaluate how menopausal hormone therapy (HT) affects the structure of older women’s brains. Our earlier work based on region of interest (ROI) analysis demonstrated potential structural changes underlying adverse effects of HT on cognition. However, the ROI-based analysis is limited in statistical power and precision, and cannot provide fine-grained mapping of whole-brain changes. Methods We aimed to identify local structural differences between HT and placebo groups from WHIMS-MRI in a whole-brain refined level, by using a novel method, named Optimally-Discriminative Voxel-Based Analysis (ODVBA). ODVBA is a recently proposed imaging pattern analysis approach for group comparisons utilizing a spatially adaptive analysis scheme to accurately locate areas of group differences, thereby providing superior sensitivity and specificity to detect the structural brain changes over conventional methods. Results Women assigned to HT treatments had significant Gray Matter (GM) losses compared to the placebo groups in the anterior cingulate and the adjacent medial frontal gyrus, and the orbitofrontal cortex, which persisted after multiple comparison corrections. There were no regions where HT was significantly associated with larger volumes compared to placebo, although a trend of marginal significance was found in the posterior cingulate cortical area. The CEE-Alone and CEE+MPA groups, although compared with different placebo controls, demonstrated similar effects according to the spatial patterns of structural changes. Conclusions HT had adverse effects on GM volumes and risk for cognitive impairment and dementia in older women. These findings advanced our understanding of the neurobiological underpinnings of HT effects. PMID:26974440

Stability of whole brain and regional network topology within and between resting and cognitive states.

PubMed

Rzucidlo, Justyna K; Roseman, Paige L; Laurienti, Paul J; Dagenbach, Dale

2013-01-01

Graph-theory based analyses of resting state functional Magnetic Resonance Imaging (fMRI) data have been used to map the network organization of the brain. While numerous analyses of resting state brain organization exist, many questions remain unexplored. The present study examines the stability of findings based on this approach over repeated resting state and working memory state sessions within the same individuals. This allows assessment of stability of network topology within the same state for both rest and working memory, and between rest and working memory as well. fMRI scans were performed on five participants while at rest and while performing the 2-back working memory task five times each, with task state alternating while they were in the scanner. Voxel-based whole brain network analyses were performed on the resulting data along with analyses of functional connectivity in regions associated with resting state and working memory. Network topology was fairly stable across repeated sessions of the same task, but varied significantly between rest and working memory. In the whole brain analysis, local efficiency, Eloc, differed significantly between rest and working memory. Analyses of network statistics for the precuneus and dorsolateral prefrontal cortex revealed significant differences in degree as a function of task state for both regions and in local efficiency for the precuneus. Conversely, no significant differences were observed across repeated sessions of the same state. These findings suggest that network topology is fairly stable within individuals across time for the same state, but also fluid between states. Whole brain voxel-based network analyses may prove to be a valuable tool for exploring how functional connectivity changes in response to task demands.

Application of a dual-resolution voxelization scheme to compressed-sensing (CS)-based iterative reconstruction in digital tomosynthesis (DTS)

NASA Astrophysics Data System (ADS)

Park, S. Y.; Kim, G. A.; Cho, H. S.; Park, C. K.; Lee, D. Y.; Lim, H. W.; Lee, H. W.; Kim, K. S.; Kang, S. Y.; Park, J. E.; Kim, W. S.; Jeon, D. H.; Je, U. K.; Woo, T. H.; Oh, J. E.

2018-02-01

In recent digital tomosynthesis (DTS), iterative reconstruction methods are often used owing to the potential to provide multiplanar images of superior image quality to conventional filtered-backprojection (FBP)-based methods. However, they require enormous computational cost in the iterative process, which has still been an obstacle to put them to practical use. In this work, we propose a new DTS reconstruction method incorporated with a dual-resolution voxelization scheme in attempt to overcome these difficulties, in which the voxels outside a small region-of-interest (ROI) containing target diagnosis are binned by 2 × 2 × 2 while the voxels inside the ROI remain unbinned. We considered a compressed-sensing (CS)-based iterative algorithm with a dual-constraint strategy for more accurate DTS reconstruction. We implemented the proposed algorithm and performed a systematic simulation and experiment to demonstrate its viability. Our results indicate that the proposed method seems to be effective for reducing computational cost considerably in iterative DTS reconstruction, keeping the image quality inside the ROI not much degraded. A binning size of 2 × 2 × 2 required only about 31.9% computational memory and about 2.6% reconstruction time, compared to those for no binning case. The reconstruction quality was evaluated in terms of the root-mean-square error (RMSE), the contrast-to-noise ratio (CNR), and the universal-quality index (UQI).

Voxel-Based Lesion Symptom Mapping of Coarse Coding and Suppression Deficits in Patients With Right Hemisphere Damage

PubMed Central

Tompkins, Connie A.; Meigh, Kimberly M.; Prat, Chantel S.

2015-01-01

Purpose This study examined right hemisphere (RH) neuroanatomical correlates of lexical–semantic deficits that predict narrative comprehension in adults with RH brain damage. Coarse semantic coding and suppression deficits were related to lesions by voxel-based lesion symptom mapping. Method Participants were 20 adults with RH cerebrovascular accidents. Measures of coarse coding and suppression deficits were computed from lexical decision reaction times at short (175 ms) and long (1000 ms) prime-target intervals. Lesions were drawn on magnetic resonance imaging images and through normalization were registered on an age-matched brain template. Voxel-based lesion symptom mapping analysis was applied to build a general linear model at each voxel. Z score maps were generated for each deficit, and results were interpreted using automated anatomical labeling procedures. Results A deficit in coarse semantic activation was associated with lesions to the RH posterior middle temporal gyrus, dorsolateral prefrontal cortex, and lenticular nuclei. A maintenance deficit for coarsely coded representations involved the RH temporal pole and dorsolateral prefrontal cortex more medially. Ineffective suppression implicated lesions to the RH inferior frontal gyrus and subcortical regions, as hypothesized, along with the rostral temporal pole. Conclusion Beyond their scientific implications, these lesion–deficit correspondences may help inform the clinical diagnosis and enhance decisions about candidacy for deficit-focused treatment to improve narrative comprehension in individuals with RH damage. PMID:26425785

Voxel-Based Lesion Symptom Mapping of Coarse Coding and Suppression Deficits in Patients With Right Hemisphere Damage.

PubMed

Yang, Ying; Tompkins, Connie A; Meigh, Kimberly M; Prat, Chantel S

2015-11-01

This study examined right hemisphere (RH) neuroanatomical correlates of lexical-semantic deficits that predict narrative comprehension in adults with RH brain damage. Coarse semantic coding and suppression deficits were related to lesions by voxel-based lesion symptom mapping. Participants were 20 adults with RH cerebrovascular accidents. Measures of coarse coding and suppression deficits were computed from lexical decision reaction times at short (175 ms) and long (1000 ms) prime-target intervals. Lesions were drawn on magnetic resonance imaging images and through normalization were registered on an age-matched brain template. Voxel-based lesion symptom mapping analysis was applied to build a general linear model at each voxel. Z score maps were generated for each deficit, and results were interpreted using automated anatomical labeling procedures. A deficit in coarse semantic activation was associated with lesions to the RH posterior middle temporal gyrus, dorsolateral prefrontal cortex, and lenticular nuclei. A maintenance deficit for coarsely coded representations involved the RH temporal pole and dorsolateral prefrontal cortex more medially. Ineffective suppression implicated lesions to the RH inferior frontal gyrus and subcortical regions, as hypothesized, along with the rostral temporal pole. Beyond their scientific implications, these lesion-deficit correspondences may help inform the clinical diagnosis and enhance decisions about candidacy for deficit-focused treatment to improve narrative comprehension in individuals with RH damage.

A Local Fast Marching-Based Diffusion Tensor Image Registration Algorithm by Simultaneously Considering Spatial Deformation and Tensor Orientation

PubMed Central

Xue, Zhong; Li, Hai; Guo, Lei; Wong, Stephen T.C.

2010-01-01

It is a key step to spatially align diffusion tensor images (DTI) to quantitatively compare neural images obtained from different subjects or the same subject at different timepoints. Different from traditional scalar or multi-channel image registration methods, tensor orientation should be considered in DTI registration. Recently, several DTI registration methods have been proposed in the literature, but deformation fields are purely dependent on the tensor features not the whole tensor information. Other methods, such as the piece-wise affine transformation and the diffeomorphic non-linear registration algorithms, use analytical gradients of the registration objective functions by simultaneously considering the reorientation and deformation of tensors during the registration. However, only relatively local tensor information such as voxel-wise tensor-similarity, is utilized. This paper proposes a new DTI image registration algorithm, called local fast marching (FM)-based simultaneous registration. The algorithm not only considers the orientation of tensors during registration but also utilizes the neighborhood tensor information of each voxel to drive the deformation, and such neighborhood tensor information is extracted from a local fast marching algorithm around the voxels of interest. These local fast marching-based tensor features efficiently reflect the diffusion patterns around each voxel within a spherical neighborhood and can capture relatively distinctive features of the anatomical structures. Using simulated and real DTI human brain data the experimental results show that the proposed algorithm is more accurate compared with the FA-based registration and is more efficient than its counterpart, the neighborhood tensor similarity-based registration. PMID:20382233

An Evolutionary Computation Approach to Examine Functional Brain Plasticity.

PubMed

Roy, Arnab; Campbell, Colin; Bernier, Rachel A; Hillary, Frank G

2016-01-01

One common research goal in systems neurosciences is to understand how the functional relationship between a pair of regions of interest (ROIs) evolves over time. Examining neural connectivity in this way is well-suited for the study of developmental processes, learning, and even in recovery or treatment designs in response to injury. For most fMRI based studies, the strength of the functional relationship between two ROIs is defined as the correlation between the average signal representing each region. The drawback to this approach is that much information is lost due to averaging heterogeneous voxels, and therefore, the functional relationship between a ROI-pair that evolve at a spatial scale much finer than the ROIs remain undetected. To address this shortcoming, we introduce a novel evolutionary computation (EC) based voxel-level procedure to examine functional plasticity between an investigator defined ROI-pair by simultaneously using subject-specific BOLD-fMRI data collected from two sessions seperated by finite duration of time. This data-driven procedure detects a sub-region composed of spatially connected voxels from each ROI (a so-called sub-regional-pair) such that the pair shows a significant gain/loss of functional relationship strength across the two time points. The procedure is recursive and iteratively finds all statistically significant sub-regional-pairs within the ROIs. Using this approach, we examine functional plasticity between the default mode network (DMN) and the executive control network (ECN) during recovery from traumatic brain injury (TBI); the study includes 14 TBI and 12 healthy control subjects. We demonstrate that the EC based procedure is able to detect functional plasticity where a traditional averaging based approach fails. The subject-specific plasticity estimates obtained using the EC-procedure are highly consistent across multiple runs. Group-level analyses using these plasticity estimates showed an increase in the strength of functional relationship between DMN and ECN for TBI subjects, which is consistent with prior findings in the TBI-literature. The EC-approach also allowed us to separate sub-regional-pairs contributing to positive and negative plasticity; the detected sub-regional-pairs significantly overlap across runs thus highlighting the reliability of the EC-approach. These sub-regional-pairs may be useful in performing nuanced analyses of brain-behavior relationships during recovery from TBI.

Modeling parameterized geometry in GPU-based Monte Carlo particle transport simulation for radiotherapy.

PubMed

Chi, Yujie; Tian, Zhen; Jia, Xun

2016-08-07

Monte Carlo (MC) particle transport simulation on a graphics-processing unit (GPU) platform has been extensively studied recently due to the efficiency advantage achieved via massive parallelization. Almost all of the existing GPU-based MC packages were developed for voxelized geometry. This limited application scope of these packages. The purpose of this paper is to develop a module to model parametric geometry and integrate it in GPU-based MC simulations. In our module, each continuous region was defined by its bounding surfaces that were parameterized by quadratic functions. Particle navigation functions in this geometry were developed. The module was incorporated to two previously developed GPU-based MC packages and was tested in two example problems: (1) low energy photon transport simulation in a brachytherapy case with a shielded cylinder applicator and (2) MeV coupled photon/electron transport simulation in a phantom containing several inserts of different shapes. In both cases, the calculated dose distributions agreed well with those calculated in the corresponding voxelized geometry. The averaged dose differences were 1.03% and 0.29%, respectively. We also used the developed package to perform simulations of a Varian VS 2000 brachytherapy source and generated a phase-space file. The computation time under the parameterized geometry depended on the memory location storing the geometry data. When the data was stored in GPU's shared memory, the highest computational speed was achieved. Incorporation of parameterized geometry yielded a computation time that was ~3 times of that in the corresponding voxelized geometry. We also developed a strategy to use an auxiliary index array to reduce frequency of geometry calculations and hence improve efficiency. With this strategy, the computational time ranged in 1.75-2.03 times of the voxelized geometry for coupled photon/electron transport depending on the voxel dimension of the auxiliary index array, and in 0.69-1.23 times for photon only transport.

A Voxel-Based Approach for Imaging Voids in Three-Dimensional Point Clouds

NASA Astrophysics Data System (ADS)

Salvaggio, Katie N.

Geographically accurate scene models have enormous potential beyond that of just simple visualizations in regard to automated scene generation. In recent years, thanks to ever increasing computational efficiencies, there has been significant growth in both the computer vision and photogrammetry communities pertaining to automatic scene reconstruction from multiple-view imagery. The result of these algorithms is a three-dimensional (3D) point cloud which can be used to derive a final model using surface reconstruction techniques. However, the fidelity of these point clouds has not been well studied, and voids often exist within the point cloud. Voids exist in texturally difficult areas, as well as areas where multiple views were not obtained during collection, constant occlusion existed due to collection angles or overlapping scene geometry, or in regions that failed to triangulate accurately. It may be possible to fill in small voids in the scene using surface reconstruction or hole-filling techniques, but this is not the case with larger more complex voids, and attempting to reconstruct them using only the knowledge of the incomplete point cloud is neither accurate nor aesthetically pleasing. A method is presented for identifying voids in point clouds by using a voxel-based approach to partition the 3D space. By using collection geometry and information derived from the point cloud, it is possible to detect unsampled voxels such that voids can be identified. This analysis takes into account the location of the camera and the 3D points themselves to capitalize on the idea of free space, such that voxels that lie on the ray between the camera and point are devoid of obstruction, as a clear line of sight is a necessary requirement for reconstruction. Using this approach, voxels are classified into three categories: occupied (contains points from the point cloud), free (rays from the camera to the point passed through the voxel), and unsampled (does not contain points and no rays passed through the area). Voids in the voxel space are manifested as unsampled voxels. A similar line-of-sight analysis can then be used to pinpoint locations at aircraft altitude at which the voids in the point clouds could theoretically be imaged. This work is based on the assumption that inclusion of more images of the void areas in the 3D reconstruction process will reduce the number of voids in the point cloud that were a result of lack of coverage. Voids resulting from texturally difficult areas will not benefit from more imagery in the reconstruction process, and thus are identified and removed prior to the determination of future potential imaging locations.

Accelerated prompt gamma estimation for clinical proton therapy simulations.

PubMed

Huisman, Brent F B; Létang, J M; Testa, É; Sarrut, D

2016-11-07

There is interest in the particle therapy community in using prompt gammas (PGs), a natural byproduct of particle treatment, for range verification and eventually dose control. However, PG production is a rare process and therefore estimation of PGs exiting a patient during a proton treatment plan executed by a Monte Carlo (MC) simulation converges slowly. Recently, different approaches to accelerating the estimation of PG yield have been presented. Sterpin et al (2015 Phys. Med. Biol. 60 4915-46) described a fast analytic method, which is still sensitive to heterogeneities. El Kanawati et al (2015 Phys. Med. Biol. 60 8067-86) described a variance reduction method (pgTLE) that accelerates the PG estimation by precomputing PG production probabilities as a function of energy and target materials, but has as a drawback that the proposed method is limited to analytical phantoms. We present a two-stage variance reduction method, named voxelized pgTLE (vpgTLE), that extends pgTLE to voxelized volumes. As a preliminary step, PG production probabilities are precomputed once and stored in a database. In stage 1, we simulate the interactions between the treatment plan and the patient CT with low statistic MC to obtain the spatial and spectral distribution of the PGs. As primary particles are propagated throughout the patient CT, the PG yields are computed in each voxel from the initial database, as a function of the current energy of the primary, the material in the voxel and the step length. The result is a voxelized image of PG yield, normalized to a single primary. The second stage uses this intermediate PG image as a source to generate and propagate the number of PGs throughout the rest of the scene geometry, e.g. into a detection device, corresponding to the number of primaries desired. We achieved a gain of around 10 3 for both a geometrical heterogeneous phantom and a complete patient CT treatment plan with respect to analog MC, at a convergence level of 2% relative uncertainty in the 90% yield region. The method agrees with reference analog MC simulations to within 10 -4 , with negligible bias. Gains per voxel range from 10 2 to 10 4 . The presented generic PG yield estimator is drop-in usable with any geometry and beam configuration. We showed a gain of three orders of magnitude compared to analog MC. With a large number of voxels and materials, memory consumption may be a concern and we discuss the consequences and possible tradeoffs. The method is available as part of Gate 7.2.

Accelerated prompt gamma estimation for clinical proton therapy simulations

NASA Astrophysics Data System (ADS)

Huisman, Brent F. B.; Létang, J. M.; Testa, É.; Sarrut, D.

2016-11-01

There is interest in the particle therapy community in using prompt gammas (PGs), a natural byproduct of particle treatment, for range verification and eventually dose control. However, PG production is a rare process and therefore estimation of PGs exiting a patient during a proton treatment plan executed by a Monte Carlo (MC) simulation converges slowly. Recently, different approaches to accelerating the estimation of PG yield have been presented. Sterpin et al (2015 Phys. Med. Biol. 60 4915-46) described a fast analytic method, which is still sensitive to heterogeneities. El Kanawati et al (2015 Phys. Med. Biol. 60 8067-86) described a variance reduction method (pgTLE) that accelerates the PG estimation by precomputing PG production probabilities as a function of energy and target materials, but has as a drawback that the proposed method is limited to analytical phantoms. We present a two-stage variance reduction method, named voxelized pgTLE (vpgTLE), that extends pgTLE to voxelized volumes. As a preliminary step, PG production probabilities are precomputed once and stored in a database. In stage 1, we simulate the interactions between the treatment plan and the patient CT with low statistic MC to obtain the spatial and spectral distribution of the PGs. As primary particles are propagated throughout the patient CT, the PG yields are computed in each voxel from the initial database, as a function of the current energy of the primary, the material in the voxel and the step length. The result is a voxelized image of PG yield, normalized to a single primary. The second stage uses this intermediate PG image as a source to generate and propagate the number of PGs throughout the rest of the scene geometry, e.g. into a detection device, corresponding to the number of primaries desired. We achieved a gain of around 103 for both a geometrical heterogeneous phantom and a complete patient CT treatment plan with respect to analog MC, at a convergence level of 2% relative uncertainty in the 90% yield region. The method agrees with reference analog MC simulations to within 10-4, with negligible bias. Gains per voxel range from 102 to 104. The presented generic PG yield estimator is drop-in usable with any geometry and beam configuration. We showed a gain of three orders of magnitude compared to analog MC. With a large number of voxels and materials, memory consumption may be a concern and we discuss the consequences and possible tradeoffs. The method is available as part of Gate 7.2.

TU-H-CAMPUS-JeP2-03: Machine-Learning-Based Delineation Framework of GTV Regions of Solid and Ground Glass Opacity Lung Tumors at Datasets of Planning CT and PET/CT Images

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ikushima, K; Arimura, H; Jin, Z

Purpose: In radiation treatment planning, delineation of gross tumor volume (GTV) is very important, because the GTVs affect the accuracies of radiation therapy procedure. To assist radiation oncologists in the delineation of GTV regions while treatment planning for lung cancer, we have proposed a machine-learning-based delineation framework of GTV regions of solid and ground glass opacity (GGO) lung tumors following by optimum contour selection (OCS) method. Methods: Our basic idea was to feed voxel-based image features around GTV contours determined by radiation oncologists into a machine learning classifier in the training step, after which the classifier produced the degree ofmore » GTV for each voxel in the testing step. Ten data sets of planning CT and PET/CT images were selected for this study. The support vector machine (SVM), which learned voxel-based features which include voxel value and magnitudes of image gradient vector that obtained from each voxel in the planning CT and PET/CT images, extracted initial GTV regions. The final GTV regions were determined using the OCS method that was able to select a global optimum object contour based on multiple active delineations with a level set method around the GTV. To evaluate the results of proposed framework for ten cases (solid:6, GGO:4), we used the three-dimensional Dice similarity coefficient (DSC), which denoted the degree of region similarity between the GTVs delineated by radiation oncologists and the proposed framework. Results: The proposed method achieved an average three-dimensional DSC of 0.81 for ten lung cancer patients, while a standardized uptake value-based method segmented GTV regions with the DSC of 0.43. The average DSCs for solid and GGO were 0.84 and 0.76, respectively, obtained by the proposed framework. Conclusion: The proposed framework with the support vector machine may be useful for assisting radiation oncologists in delineating solid and GGO lung tumors.« less

All about FAX: a Female Adult voXel phantom for Monte Carlo calculation in radiation protection dosimetry.

PubMed

Kramer, R; Khoury, H J; Vieira, J W; Loureiro, E C M; Lima, V J M; Lima, F R A; Hoff, G

2004-12-07

The International Commission on Radiological Protection (ICRP) has created a task group on dose calculations, which, among other objectives, should replace the currently used mathematical MIRD phantoms by voxel phantoms. Voxel phantoms are based on digital images recorded from scanning of real persons by computed tomography or magnetic resonance imaging (MRI). Compared to the mathematical MIRD phantoms, voxel phantoms are true to the natural representations of a human body. Connected to a radiation transport code, voxel phantoms serve as virtual humans for which equivalent dose to organs and tissues from exposure to ionizing radiation can be calculated. The principal database for the construction of the FAX (Female Adult voXel) phantom consisted of 151 CT images recorded from scanning of trunk and head of a female patient, whose body weight and height were close to the corresponding data recommended by the ICRP in Publication 89. All 22 organs and tissues at risk, except for the red bone marrow and the osteogenic cells on the endosteal surface of bone ('bone surface'), have been segmented manually with a technique recently developed at the Departamento de Energia Nuclear of the UFPE in Recife, Brazil. After segmentation the volumes of the organs and tissues have been adjusted to agree with the organ and tissue masses recommended by ICRP for the Reference Adult Female in Publication 89. Comparisons have been made with the organ and tissue masses of the mathematical EVA phantom, as well as with the corresponding data for other female voxel phantoms. The three-dimensional matrix of the segmented images has eventually been connected to the EGS4 Monte Carlo code. Effective dose conversion coefficients have been calculated for exposures to photons, and compared to data determined for the mathematical MIRD-type phantoms, as well as for other voxel phantoms.

Single-voxel and multi-voxel spectroscopy yield comparable results in the normal juvenile canine brain when using 3 Tesla magnetic resonance imaging.

PubMed

Lee, Alison M; Beasley, Michaela J; Barrett, Emerald D; James, Judy R; Gambino, Jennifer M

2018-06-10

Conventional magnetic resonance imaging (MRI) characteristics of canine brain diseases are often nonspecific. Single- and multi-voxel spectroscopy techniques allow quantification of chemical biomarkers for tissues of interest and may help to improve diagnostic specificity. However, published information is currently lacking for the in vivo performance of these two techniques in dogs. The aim of this prospective, methods comparison study was to compare the performance of single- and multi-voxel spectroscopy in the brains of eight healthy, juvenile dogs using 3 Tesla MRI. Ipsilateral regions of single- and multi-voxel spectroscopy were performed in symmetric regions of interest of each brain in the parietal (n = 3), thalamic (n = 2), and piriform lobes (n = 3). In vivo single-voxel spectroscopy and multi-voxel spectroscopy metabolite ratios from the same size and multi-voxel spectroscopy ratios from different sized regions of interest were compared. No significant difference was seen between single-voxel spectroscopy and multi-voxel spectroscopy metabolite ratios for any lobe when regions of interest were similar in size and shape. Significant lobar single-voxel spectroscopy and multi-voxel spectroscopy differences were seen between the parietal lobe and thalamus (P = 0.047) for the choline to N-acetyl aspartase ratios when large multi-voxel spectroscopy regions of interest were compared to very small multi-voxel spectroscopy regions of interest within the same lobe; and for the N-acetyl aspartase to creatine ratios in all lobes when single-voxel spectroscopy was compared to combined (pooled) multi-voxel spectroscopy datasets. Findings from this preliminary study indicated that single- and multi-voxel spectroscopy techniques using 3T MRI yield comparable results for similar sized regions of interest in the normal canine brain. Findings also supported using the contralateral side as an internal control for dogs with brain lesions. © 2018 American College of Veterinary Radiology.

The distance discordance metric - A novel approach to quantifying spatial uncertainties in intra- and inter-patient deformable image registration

PubMed Central

Saleh, Ziad H.; Apte, Aditya P.; Sharp, Gregory C.; Shusharina, Nadezhda P.; Wang, Ya; Veeraraghavan, Harini; Thor, Maria; Muren, Ludvig P.; Rao, Shyam S.; Lee, Nancy Y.; Deasy, Joseph O.

2014-01-01

Previous methods to estimate the inherent accuracy of deformable image registration (DIR) have typically been performed relative to a known ground truth, such as tracking of anatomic landmarks or known deformations in a physical or virtual phantom. In this study, we propose a new approach to estimate the spatial geometric uncertainty of DIR using statistical sampling techniques that can be applied to the resulting deformation vector fields (DVFs) for a given registration. The proposed DIR performance metric, the distance discordance metric (DDM), is based on the variability in the distance between corresponding voxels from different images, which are co-registered to the same voxel at location (X) in an arbitrarily chosen “reference” image. The DDM value, at location (X) in the reference image, represents the mean dispersion between voxels, when these images are registered to other images in the image set. The method requires at least four registered images to estimate the uncertainty of the DIRs, both for inter-and intra-patient DIR. To validate the proposed method, we generated an image set by deforming a software phantom with known DVFs. The registration error was computed at each voxel in the “reference” phantom and then compared to DDM, inverse consistency error (ICE), and transitivity error (TE) over the entire phantom. The DDM showed a higher Pearson correlation (Rp) with the actual error (Rp ranged from 0.6 to 0.9) in comparison with ICE and TE (Rp ranged from 0.2 to 0.8). In the resulting spatial DDM map, regions with distinct intensity gradients had a lower discordance and therefore, less variability relative to regions with uniform intensity. Subsequently, we applied DDM for intra-patient DIR in an image set of 10 longitudinal computed tomography (CT) scans of one prostate cancer patient and for inter-patient DIR in an image set of 10 planning CT scans of different head and neck cancer patients. For both intra- and inter-patient DIR, the spatial DDM map showed large variation over the volume of interest (the pelvis for the prostate patient and the head for the head and neck patients). The highest discordance was observed in the soft tissues, such as the brain, bladder, and rectum, due to higher variability in the registration. The smallest DDM values were observed in the bony structures in the pelvis and the base of the skull. The proposed metric, DDM, provides a quantitative tool to evaluate the performance of DIR when a set of images is available. Therefore, DDM can be used to estimate and visualize the uncertainty of intra- and/or inter-patient DIR based on the variability of the registration rather than the absolute registration error. PMID:24440838

New hybrid voxelized/analytical primitive in Monte Carlo simulations for medical applications

NASA Astrophysics Data System (ADS)

Bert, Julien; Lemaréchal, Yannick; Visvikis, Dimitris

2016-05-01

Monte Carlo simulations (MCS) applied in particle physics play a key role in medical imaging and particle therapy. In such simulations, particles are transported through voxelized phantoms derived from predominantly patient CT images. However, such voxelized object representation limits the incorporation of fine elements, such as artificial implants from CAD modeling or anatomical and functional details extracted from other imaging modalities. In this work we propose a new hYbrid Voxelized/ANalytical primitive (YVAN) that combines both voxelized and analytical object descriptions within the same MCS, without the need to simultaneously run two parallel simulations, which is the current gold standard methodology. Given that YVAN is simply a new primitive object, it does not require any modifications on the underlying MC navigation code. The new proposed primitive was assessed through a first simple MCS. Results from the YVAN primitive were compared against an MCS using a pure analytical geometry and the layer mass geometry concept. A perfect agreement was found between these simulations, leading to the conclusion that the new hybrid primitive is able to accurately and efficiently handle phantoms defined by a mixture of voxelized and analytical objects. In addition, two application-based evaluation studies in coronary angiography and intra-operative radiotherapy showed that the use of YVAN was 6.5% and 12.2% faster than the layered mass geometry method, respectively, without any associated loss of accuracy. However, the simplification advantages and differences in computational time improvements obtained with YVAN depend on the relative proportion of the analytical and voxelized structures used in the simulation as well as the size and number of triangles used in the description of the analytical object meshes.

Voxel size dependency, reproducibility and sensitivity of an in vivo bone loading estimation algorithm

PubMed Central

Christen, Patrik; Schulte, Friederike A.; Zwahlen, Alexander; van Rietbergen, Bert; Boutroy, Stephanie; Melton, L. Joseph; Amin, Shreyasee; Khosla, Sundeep; Goldhahn, Jörg; Müller, Ralph

2016-01-01

A bone loading estimation algorithm was previously developed that provides in vivo loading conditions required for in vivo bone remodelling simulations. The algorithm derives a bone's loading history from its microstructure as assessed by high-resolution (HR) computed tomography (CT). This reverse engineering approach showed accurate and realistic results based on micro-CT and HR-peripheral quantitative CT images. However, its voxel size dependency, reproducibility and sensitivity still need to be investigated, which is the purpose of this study. Voxel size dependency was tested on cadaveric distal radii with micro-CT images scanned at 25 µm and downscaled to 50, 61, 75, 82, 100, 125 and 150 µm. Reproducibility was calculated with repeated in vitro as well as in vivo HR-pQCT measurements at 82 µm. Sensitivity was defined using HR-pQCT images from women with fracture versus non-fracture, and low versus high bone volume fraction, expecting similar and different loading histories, respectively. Our results indicate that the algorithm is voxel size independent within an average (maximum) error of 8.2% (32.9%) at 61 µm, but that the dependency increases considerably at voxel sizes bigger than 82 µm. In vitro and in vivo reproducibility are up to 4.5% and 10.2%, respectively, which is comparable to other in vitro studies and slightly higher than in other in vivo studies. Subjects with different bone volume fraction were clearly distinguished but not subjects with and without fracture. This is in agreement with bone adapting to customary loading but not to fall loads. We conclude that the in vivo bone loading estimation algorithm provides reproducible, sensitive and fairly voxel size independent results at up to 82 µm, but that smaller voxel sizes would be advantageous. PMID:26790999

Single-digit arithmetic processing—anatomical evidence from statistical voxel-based lesion analysis

PubMed Central

Mihulowicz, Urszula; Willmes, Klaus; Karnath, Hans-Otto; Klein, Elise

2014-01-01

Different specific mechanisms have been suggested for solving single-digit arithmetic operations. However, the neural correlates underlying basic arithmetic (multiplication, addition, subtraction) are still under debate. In the present study, we systematically assessed single-digit arithmetic in a group of acute stroke patients (n = 45) with circumscribed left- or right-hemispheric brain lesions. Lesion sites significantly related to impaired performance were found only in the left-hemisphere damaged (LHD) group. Deficits in multiplication and addition were related to subcortical/white matter brain regions differing from those for subtraction tasks, corroborating the notion of distinct processing pathways for different arithmetic tasks. Additionally, our results further point to the importance of investigating fiber pathways in numerical cognition. PMID:24847238

NOTE: On the need to revise the arm structure in stylized anthropomorphic phantoms in lateral photon irradiation geometry

NASA Astrophysics Data System (ADS)

Lee, Choonsik; Lee, Choonik; Lee, Jai-Ki

2006-11-01

Distributions of radiation absorbed dose within human anatomy have been estimated through Monte Carlo radiation transport techniques implemented for two different classes of computational anthropomorphic phantoms: (1) mathematical equation-based stylized phantoms and (2) tomographic image-based voxel phantoms. Voxel phantoms constructed from tomographic images of real human anatomy have been actively developed since the late 1980s to overcome the anatomical approximations necessary with stylized phantoms, which themselves have been utilized since the mid 1960s. However, revisions of stylized phantoms have also been pursued in parallel to the development of voxel phantoms since voxel phantoms (1) are initially restricted to the individual-specific anatomy of the person originally imaged, (2) must be restructured on an organ-by-organ basis to conform to reference individual anatomy and (3) cannot easily represent very fine anatomical structures and tissue layers that are thinner than the voxel dimensions of the overall phantom. Although efforts have been made to improve the anatomic realism of stylized phantoms, most of these efforts have been limited to attempts to alter internal organ structures. Aside from the internal organs, the exterior shapes, and especially the arm structures, of stylized phantoms are also far from realistic descriptions of human anatomy, and may cause dosimetry errors in the calculation of organ-absorbed doses for external irradiation scenarios. The present study was intended to highlight the need to revise the existing arm structure within stylized phantoms by comparing organ doses of stylized adult phantoms with those from three adult voxel phantoms in the lateral photon irradiation geometry. The representative stylized phantom, the adult phantom of the Oak Ridge National Laboratory (ORNL) series and two adult male voxel phantoms, KTMAN-2 and VOXTISS8, were employed for Monte Carlo dose calculation, and data from another voxel phantom, VIP-Man, were obtained from literature sources. The absorbed doses for lungs, oesophagus, liver and kidneys that could be affected by arm structures in the lateral irradiation geometry were obtained for both classes of phantoms in lateral monoenergetic photon irradiation geometries. As expected, those organs in the ORNL phantoms received apparently higher absorbed doses than those in the voxel phantoms. The overestimation is mainly attributed to the relatively poor representation of the arm structure in the ORNL phantom in which the arm bones are embedded within the regions describing the phantom's torso. The results of this study suggest that the overestimation of organ doses, due to unrealistic arm representation, should be taken into account when stylized phantoms are employed for equivalent or effective dose estimates, especially in the case of an irradiation scenario with dominating lateral exposure. For such a reason, the stylized phantom arm structure definition should be revised in order to obtain more realistic evaluations.

Automated diagnosis of Alzheimer's disease with multi-atlas based whole brain segmentations

NASA Astrophysics Data System (ADS)

Luo, Yuan; Tang, Xiaoying

2017-03-01

Voxel-based analysis is widely used in quantitative analysis of structural brain magnetic resonance imaging (MRI) and automated disease detection, such as Alzheimer's disease (AD). However, noise at the voxel level may cause low sensitivity to AD-induced structural abnormalities. This can be addressed with the use of a whole brain structural segmentation approach which greatly reduces the dimension of features (the number of voxels). In this paper, we propose an automatic AD diagnosis system that combines such whole brain segmen- tations with advanced machine learning methods. We used a multi-atlas segmentation technique to parcellate T1-weighted images into 54 distinct brain regions and extract their structural volumes to serve as the features for principal-component-analysis-based dimension reduction and support-vector-machine-based classification. The relationship between the number of retained principal components (PCs) and the diagnosis accuracy was systematically evaluated, in a leave-one-out fashion, based on 28 AD subjects and 23 age-matched healthy subjects. Our approach yielded pretty good classification results with 96.08% overall accuracy being achieved using the three foremost PCs. In addition, our approach yielded 96.43% specificity, 100% sensitivity, and 0.9891 area under the receiver operating characteristic curve.

Classification of pulmonary emphysema from chest CT scans using integral geometry descriptors

NASA Astrophysics Data System (ADS)

van Rikxoort, E. M.; Goldin, J. G.; Galperin-Aizenberg, M.; Brown, M. S.

2011-03-01

To gain insight into the underlying pathways of emphysema and monitor the effect of treatment, methods to quantify and phenotype the different types of emphysema from chest CT scans are of crucial importance. Current standard measures rely on density thresholds for individual voxels, which is influenced by inspiration level and does not take into account the spatial relationship between voxels. Measures based on texture analysis do take the interrelation between voxels into account and therefore might be useful for distinguishing different types of emphysema. In this study, we propose to use Minkowski functionals combined with rotation invariant Gaussian features to distinguish between healthy and emphysematous tissue and classify three different types of emphysema. Minkowski functionals characterize binary images in terms of geometry and topology. In 3D, four Minkowski functionals are defined. By varying the threshold and size of neighborhood around a voxel, a set of Minkowski functionals can be defined for each voxel. Ten chest CT scans with 1810 annotated regions were used to train the method. A set of 108 features was calculated for each training sample from which 10 features were selected to be most informative. A linear discriminant classifier was trained to classify each voxel in the lungs into a subtype of emphysema or normal lung. The method was applied to an independent test set of 30 chest CT scans with varying amounts and types of emphysema with 4347 annotated regions of interest. The method is shown to perform well, with an overall accuracy of 95%.

Efficient Skeletonization of Volumetric Objects.

PubMed

Zhou, Yong; Toga, Arthur W

1999-07-01

Skeletonization promises to become a powerful tool for compact shape description, path planning, and other applications. However, current techniques can seldom efficiently process real, complicated 3D data sets, such as MRI and CT data of human organs. In this paper, we present an efficient voxel-coding based algorithm for Skeletonization of 3D voxelized objects. The skeletons are interpreted as connected centerlines. consisting of sequences of medial points of consecutive clusters. These centerlines are initially extracted as paths of voxels, followed by medial point replacement, refinement, smoothness, and connection operations. The voxel-coding techniques have been proposed for each of these operations in a uniform and systematic fashion. In addition to preserving basic connectivity and centeredness, the algorithm is characterized by straightforward computation, no sensitivity to object boundary complexity, explicit extraction of ready-to-parameterize and branch-controlled skeletons, and efficient object hole detection. These issues are rarely discussed in traditional methods. A range of 3D medical MRI and CT data sets were used for testing the algorithm, demonstrating its utility.

NOTE: Development of modified voxel phantoms for the numerical dosimetric reconstruction of radiological accidents involving external sources: implementation in SESAME tool

NASA Astrophysics Data System (ADS)

Courageot, Estelle; Sayah, Rima; Huet, Christelle

2010-05-01

Estimating the dose distribution in a victim's body is a relevant indicator in assessing biological damage from exposure in the event of a radiological accident caused by an external source. When the dose distribution is evaluated with a numerical anthropomorphic model, the posture and morphology of the victim have to be reproduced as realistically as possible. Several years ago, IRSN developed a specific software application, called the simulation of external source accident with medical images (SESAME), for the dosimetric reconstruction of radiological accidents by numerical simulation. This tool combines voxel geometry and the MCNP(X) Monte Carlo computer code for radiation-material interaction. This note presents a new functionality in this software that enables the modelling of a victim's posture and morphology based on non-uniform rational B-spline (NURBS) surfaces. The procedure for constructing the modified voxel phantoms is described, along with a numerical validation of this new functionality using a voxel phantom of the RANDO tissue-equivalent physical model.

Development of modified voxel phantoms for the numerical dosimetric reconstruction of radiological accidents involving external sources: implementation in SESAME tool.

PubMed

Courageot, Estelle; Sayah, Rima; Huet, Christelle

2010-05-07

Estimating the dose distribution in a victim's body is a relevant indicator in assessing biological damage from exposure in the event of a radiological accident caused by an external source. When the dose distribution is evaluated with a numerical anthropomorphic model, the posture and morphology of the victim have to be reproduced as realistically as possible. Several years ago, IRSN developed a specific software application, called the simulation of external source accident with medical images (SESAME), for the dosimetric reconstruction of radiological accidents by numerical simulation. This tool combines voxel geometry and the MCNP(X) Monte Carlo computer code for radiation-material interaction. This note presents a new functionality in this software that enables the modelling of a victim's posture and morphology based on non-uniform rational B-spline (NURBS) surfaces. The procedure for constructing the modified voxel phantoms is described, along with a numerical validation of this new functionality using a voxel phantom of the RANDO tissue-equivalent physical model.

Novel cardiac magnetic resonance biomarkers: native T1 and extracellular volume myocardial mapping.

PubMed

Cannaò, Paola Maria; Altabella, Luisa; Petrini, Marcello; Alì, Marco; Secchi, Francesco; Sardanelli, Francesco

2016-04-28

Cardiac magnetic resonance (CMR) is a non-invasive diagnostic tool playing a key role in the assessment of cardiac morphology and function as well as in tissue characterization. Late gadolinium enhancement is a fundamental CMR technique for detecting focal or regional abnormalities such as scar tissue, replacement fibrosis, or inflammation using qualitative, semi-quantitative, or quantitative methods, but not allowing for evaluating the whole myocardium in the presence of diffuse disease. The novel T1 mapping approach permits a quantitative assessment of the entire myocardium providing a voxel-by-voxel map of native T1 relaxation time, obtained before the intravenous administration of gadolinium-based contrast material. Combining T1 data obtained before and after contrast injection, it is also possible to calculate the voxel-by-voxel extracellular volume (ECV), resulting in another myocardial parametric map. This article describes technical challenges and clinical perspectives of these two novel CMR biomarkers: myocardial native T1 and ECV mapping.

Estimation of urinary stone composition by automated processing of CT images.

PubMed

Chevreau, Grégoire; Troccaz, Jocelyne; Conort, Pierre; Renard-Penna, Raphaëlle; Mallet, Alain; Daudon, Michel; Mozer, Pierre

2009-10-01

The objective of this article was developing an automated tool for routine clinical practice to estimate urinary stone composition from CT images based on the density of all constituent voxels. A total of 118 stones for which the composition had been determined by infrared spectroscopy were placed in a helical CT scanner. A standard acquisition, low-dose and high-dose acquisitions were performed. All voxels constituting each stone were automatically selected. A dissimilarity index evaluating variations of density around each voxel was created in order to minimize partial volume effects: stone composition was established on the basis of voxel density of homogeneous zones. Stone composition was determined in 52% of cases. Sensitivities for each compound were: uric acid: 65%, struvite: 19%, cystine: 78%, carbapatite: 33.5%, calcium oxalate dihydrate: 57%, calcium oxalate monohydrate: 66.5%, brushite: 75%. Low-dose acquisition did not lower the performances (P < 0.05). This entirely automated approach eliminates manual intervention on the images by the radiologist while providing identical performances including for low-dose protocols.

Impact of tumour motion compensation and delineation methods on FDG PET-based dose painting plan quality for NSCLC radiation therapy.

PubMed

Thomas, Hannah Mary; Kinahan, Paul E; Samuel, James Jebaseelan E; Bowen, Stephen R

2018-02-01

To quantitatively estimate the impact of different methods for both boost volume delineation and respiratory motion compensation of [18F] FDG PET/CT images on the fidelity of planned non-uniform 'dose painting' plans to the prescribed boost dose distribution. Six locally advanced non-small cell lung cancer (NSCLC) patients were retrospectively reviewed. To assess the impact of respiratory motion, time-averaged (3D AVG), respiratory phase-gated (4D GATED) and motion-encompassing (4D MIP) PET images were used. The boost volumes were defined using manual contour (MANUAL), fixed threshold (FIXED) and gradient search algorithm (GRADIENT). The dose painting prescription of 60 Gy base dose to the planning target volume and an integral dose of 14 Gy (total 74 Gy) was discretized into seven treatment planning substructures and linearly redistributed according to the relative SUV at every voxel in the boost volume. Fifty-four dose painting plan combinations were generated and conformity was evaluated using quality index VQ0.95-1.05, which represents the sum of planned dose voxels within 5% deviation from the prescribed dose. Trends in plan quality and magnitude of achievable dose escalation were recorded. Different segmentation techniques produced statistically significant variations in maximum planned dose (P < 0.02), as well as plan quality between segmentation methods for 4D GATED and 4D MIP PET images (P < 0.05). No statistically significant differences in plan quality and maximum dose were observed between motion-compensated PET-based plans (P > 0.75). Low variability in plan quality was observed for FIXED threshold plans, while MANUAL and GRADIENT plans achieved higher dose with lower plan quality indices. The dose painting plans were more sensitive to segmentation of boost volumes than PET motion compensation in this study sample. Careful consideration of boost target delineation and motion compensation strategies should guide the design of NSCLC dose painting trials. © 2017 The Royal Australian and New Zealand College of Radiologists.

Stimulus-related independent component and voxel-wise analysis of human brain activity during free viewing of a feature film.

PubMed

Lahnakoski, Juha M; Salmi, Juha; Jääskeläinen, Iiro P; Lampinen, Jouko; Glerean, Enrico; Tikka, Pia; Sams, Mikko

2012-01-01

Understanding how the brain processes stimuli in a rich natural environment is a fundamental goal of neuroscience. Here, we showed a feature film to 10 healthy volunteers during functional magnetic resonance imaging (fMRI) of hemodynamic brain activity. We then annotated auditory and visual features of the motion picture to inform analysis of the hemodynamic data. The annotations were fitted to both voxel-wise data and brain network time courses extracted by independent component analysis (ICA). Auditory annotations correlated with two independent components (IC) disclosing two functional networks, one responding to variety of auditory stimulation and another responding preferentially to speech but parts of the network also responding to non-verbal communication. Visual feature annotations correlated with four ICs delineating visual areas according to their sensitivity to different visual stimulus features. In comparison, a separate voxel-wise general linear model based analysis disclosed brain areas preferentially responding to sound energy, speech, music, visual contrast edges, body motion and hand motion which largely overlapped the results revealed by ICA. Differences between the results of IC- and voxel-based analyses demonstrate that thorough analysis of voxel time courses is important for understanding the activity of specific sub-areas of the functional networks, while ICA is a valuable tool for revealing novel information about functional connectivity which need not be explained by the predefined model. Our results encourage the use of naturalistic stimuli and tasks in cognitive neuroimaging to study how the brain processes stimuli in rich natural environments.

Stimulus-Related Independent Component and Voxel-Wise Analysis of Human Brain Activity during Free Viewing of a Feature Film

PubMed Central

Lahnakoski, Juha M.; Salmi, Juha; Jääskeläinen, Iiro P.; Lampinen, Jouko; Glerean, Enrico; Tikka, Pia; Sams, Mikko

2012-01-01

Understanding how the brain processes stimuli in a rich natural environment is a fundamental goal of neuroscience. Here, we showed a feature film to 10 healthy volunteers during functional magnetic resonance imaging (fMRI) of hemodynamic brain activity. We then annotated auditory and visual features of the motion picture to inform analysis of the hemodynamic data. The annotations were fitted to both voxel-wise data and brain network time courses extracted by independent component analysis (ICA). Auditory annotations correlated with two independent components (IC) disclosing two functional networks, one responding to variety of auditory stimulation and another responding preferentially to speech but parts of the network also responding to non-verbal communication. Visual feature annotations correlated with four ICs delineating visual areas according to their sensitivity to different visual stimulus features. In comparison, a separate voxel-wise general linear model based analysis disclosed brain areas preferentially responding to sound energy, speech, music, visual contrast edges, body motion and hand motion which largely overlapped the results revealed by ICA. Differences between the results of IC- and voxel-based analyses demonstrate that thorough analysis of voxel time courses is important for understanding the activity of specific sub-areas of the functional networks, while ICA is a valuable tool for revealing novel information about functional connectivity which need not be explained by the predefined model. Our results encourage the use of naturalistic stimuli and tasks in cognitive neuroimaging to study how the brain processes stimuli in rich natural environments. PMID:22496909

Effects of RF pulse profile and intra-voxel phase dispersion on MR fingerprinting with balanced SSFP readout.

PubMed

Chiu, Su-Chin; Lin, Te-Ming; Lin, Jyh-Miin; Chung, Hsiao-Wen; Ko, Cheng-Wen; Büchert, Martin; Bock, Michael

2017-09-01

To investigate possible errors in T1 and T2 quantification via MR fingerprinting with balanced steady-state free precession readout in the presence of intra-voxel phase dispersion and RF pulse profile imperfections, using computer simulations based on Bloch equations. A pulse sequence with TR changing in a Perlin noise pattern and a nearly sinusoidal pattern of flip angle following an initial 180-degree inversion pulse was employed. Gaussian distributions of off-resonance frequency were assumed for intra-voxel phase dispersion effects. Slice profiles of sinc-shaped RF pulses were computed to investigate flip angle profile influences. Following identification of the best fit between the acquisition signals and those established in the dictionary based on known parameters, estimation errors were reported. In vivo experiments were performed at 3T to examine the results. Slight intra-voxel phase dispersion with standard deviations from 1 to 3Hz resulted in prominent T2 under-estimations, particularly at large T2 values. T1 and off-resonance frequencies were relatively unaffected. Slice profile imperfections led to under-estimations of T1, which became greater as regional off-resonance frequencies increased, but could be corrected by including slice profile effects in the dictionary. Results from brain imaging experiments in vivo agreed with the simulation results qualitatively. MR fingerprinting using balanced SSFP readout in the presence of intra-voxel phase dispersion and imperfect slice profile leads to inaccuracies in quantitative estimations of the relaxation times. Copyright © 2017 Elsevier Inc. All rights reserved.

Automated Coarse Registration of Point Clouds in 3d Urban Scenes Using Voxel Based Plane Constraint

NASA Astrophysics Data System (ADS)

Xu, Y.; Boerner, R.; Yao, W.; Hoegner, L.; Stilla, U.

2017-09-01

For obtaining a full coverage of 3D scans in a large-scale urban area, the registration between point clouds acquired via terrestrial laser scanning (TLS) is normally mandatory. However, due to the complex urban environment, the automatic registration of different scans is still a challenging problem. In this work, we propose an automatic marker free method for fast and coarse registration between point clouds using the geometric constrains of planar patches under a voxel structure. Our proposed method consists of four major steps: the voxelization of the point cloud, the approximation of planar patches, the matching of corresponding patches, and the estimation of transformation parameters. In the voxelization step, the point cloud of each scan is organized with a 3D voxel structure, by which the entire point cloud is partitioned into small individual patches. In the following step, we represent points of each voxel with the approximated plane function, and select those patches resembling planar surfaces. Afterwards, for matching the corresponding patches, a RANSAC-based strategy is applied. Among all the planar patches of a scan, we randomly select a planar patches set of three planar surfaces, in order to build a coordinate frame via their normal vectors and their intersection points. The transformation parameters between scans are calculated from these two coordinate frames. The planar patches set with its transformation parameters owning the largest number of coplanar patches are identified as the optimal candidate set for estimating the correct transformation parameters. The experimental results using TLS datasets of different scenes reveal that our proposed method can be both effective and efficient for the coarse registration task. Especially, for the fast orientation between scans, our proposed method can achieve a registration error of less than around 2 degrees using the testing datasets, and much more efficient than the classical baseline methods.

A study of brain metabolism in fibromyalgia by positron emission tomography.

PubMed

Usui, Chie; Soma, Tsutomu; Hatta, Kotaro; Aratani, Satoko; Fujita, Hidetoshi; Nishioka, Kenya; Machida, Yutaka; Kuroiwa, Yoshiyuki; Nakajima, Toshihiro; Nishioka, Kusuki

2017-04-03

The aim of the present study was to determine the brain regions with altered metabolism in patients with treatment-naïve fibromyalgia (FM). We used [ 18 F] fluoro-d-glucose positron emission tomography to examine a total of 18 treatment-naïve FM patients and 18 age- and sex-matched healthy controls not suffering from pain. A voxel-by-voxel group analysis was performed using statistical parametric mapping. No significant voxel (peak)-level results were detected in this study; however, some regions were detected as significant-size clusters. There were no significant differences in brain metabolism between FM patients and controls. However, the right thalamus and left lentiform nucleus were hypermetabolic areas in FM patients with poor prognosis compared to the healthy controls. In contrast, the left insula and left lentiform nucleus were hypometabolic areas in FM patients with good prognosis compared to the healthy controls. Compared to FM patients with good prognosis, FM patients with poor prognosis showed significant hypermetabolism in the left thalamus, bilateral lentiform nucleus, and right parahippocampal gyrus. The present findings suggest an association between the metabolism in the thalamus, lentiform nucleus, and parahippocampal gyrus and prognosis in FM patients. Further study with a larger number of patients is required to confirm this association. Copyright © 2017 Elsevier Inc. All rights reserved.

Supratentorial lesions contribute to trigeminal neuralgia in multiple sclerosis.

PubMed

Fröhlich, Kilian; Winder, Klemens; Linker, Ralf A; Engelhorn, Tobias; Dörfler, Arnd; Lee, De-Hyung; Hilz, Max J; Schwab, Stefan; Seifert, Frank

2018-06-01

Background It has been proposed that multiple sclerosis lesions afflicting the pontine trigeminal afferents contribute to trigeminal neuralgia in multiple sclerosis. So far, there are no imaging studies that have evaluated interactions between supratentorial lesions and trigeminal neuralgia in multiple sclerosis patients. Methods We conducted a retrospective study and sought multiple sclerosis patients with trigeminal neuralgia and controls in a local database. Multiple sclerosis lesions were manually outlined and transformed into stereotaxic space. We determined the lesion overlap and performed a voxel-wise subtraction analysis. Secondly, we conducted a voxel-wise non-parametric analysis using the Liebermeister test. Results From 12,210 multiple sclerosis patient records screened, we identified 41 patients with trigeminal neuralgia. The voxel-wise subtraction analysis yielded associations between trigeminal neuralgia and multiple sclerosis lesions in the pontine trigeminal afferents, as well as larger supratentorial lesion clusters in the contralateral insula and hippocampus. The non-parametric statistical analysis using the Liebermeister test yielded similar areas to be associated with multiple sclerosis-related trigeminal neuralgia. Conclusions Our study confirms previous data on associations between multiple sclerosis-related trigeminal neuralgia and pontine lesions, and showed for the first time an association with lesions in the insular region, a region involved in pain processing and endogenous pain modulation.

Automated three-dimensional quantification of myocardial perfusion and brain SPECT.

PubMed

Slomka, P J; Radau, P; Hurwitz, G A; Dey, D

2001-01-01

To allow automated and objective reading of nuclear medicine tomography, we have developed a set of tools for clinical analysis of myocardial perfusion tomography (PERFIT) and Brain SPECT/PET (BRASS). We exploit algorithms for image registration and use three-dimensional (3D) "normal models" for individual patient comparisons to composite datasets on a "voxel-by-voxel basis" in order to automatically determine the statistically significant abnormalities. A multistage, 3D iterative inter-subject registration of patient images to normal templates is applied, including automated masking of the external activity before final fit. In separate projects, the software has been applied to the analysis of myocardial perfusion SPECT, as well as brain SPECT and PET data. Automatic reading was consistent with visual analysis; it can be applied to the whole spectrum of clinical images, and aid physicians in the daily interpretation of tomographic nuclear medicine images.

Spectral embedding based active contour (SEAC) for lesion segmentation on breast dynamic contrast enhanced magnetic resonance imaging.

PubMed

Agner, Shannon C; Xu, Jun; Madabhushi, Anant

2013-03-01

Segmentation of breast lesions on dynamic contrast enhanced (DCE) magnetic resonance imaging (MRI) is the first step in lesion diagnosis in a computer-aided diagnosis framework. Because manual segmentation of such lesions is both time consuming and highly susceptible to human error and issues of reproducibility, an automated lesion segmentation method is highly desirable. Traditional automated image segmentation methods such as boundary-based active contour (AC) models require a strong gradient at the lesion boundary. Even when region-based terms are introduced to an AC model, grayscale image intensities often do not allow for clear definition of foreground and background region statistics. Thus, there is a need to find alternative image representations that might provide (1) strong gradients at the margin of the object of interest (OOI); and (2) larger separation between intensity distributions and region statistics for the foreground and background, which are necessary to halt evolution of the AC model upon reaching the border of the OOI. In this paper, the authors introduce a spectral embedding (SE) based AC (SEAC) for lesion segmentation on breast DCE-MRI. SE, a nonlinear dimensionality reduction scheme, is applied to the DCE time series in a voxelwise fashion to reduce several time point images to a single parametric image where every voxel is characterized by the three dominant eigenvectors. This parametric eigenvector image (PrEIm) representation allows for better capture of image region statistics and stronger gradients for use with a hybrid AC model, which is driven by both boundary and region information. They compare SEAC to ACs that employ fuzzy c-means (FCM) and principal component analysis (PCA) as alternative image representations. Segmentation performance was evaluated by boundary and region metrics as well as comparing lesion classification using morphological features from SEAC, PCA+AC, and FCM+AC. On a cohort of 50 breast DCE-MRI studies, PrEIm yielded overall better region and boundary-based statistics compared to the original DCE-MR image, FCM, and PCA based image representations. Additionally, SEAC outperformed a hybrid AC applied to both PCA and FCM image representations. Mean dice similarity coefficient (DSC) for SEAC was significantly better (DSC = 0.74 ± 0.21) than FCM+AC (DSC = 0.50 ± 0.32) and similar to PCA+AC (DSC = 0.73 ± 0.22). Boundary-based metrics of mean absolute difference and Hausdorff distance followed the same trends. Of the automated segmentation methods, breast lesion classification based on morphologic features derived from SEAC segmentation using a support vector machine classifier also performed better (AUC = 0.67 ± 0.05; p < 0.05) than FCM+AC (AUC = 0.50 ± 0.07), and PCA+AC (AUC = 0.49 ± 0.07). In this work, we presented SEAC, an accurate, general purpose AC segmentation tool that could be applied to any imaging domain that employs time series data. SE allows for projection of time series data into a PrEIm representation so that every voxel is characterized by the dominant eigenvectors, capturing the global and local time-intensity curve similarities in the data. This PrEIm allows for the calculation of strong tensor gradients and better region statistics than the original image intensities or alternative image representations such as PCA and FCM. The PrEIm also allows for building a more accurate hybrid AC scheme.

Automated voxel classification used with atlas-guided diffuse optical tomography for assessment of functional brain networks in young and older adults.

PubMed

Li, Lin; Cazzell, Mary; Babawale, Olajide; Liu, Hanli

2016-10-01

Atlas-guided diffuse optical tomography (atlas-DOT) is a computational means to image changes in cortical hemodynamic signals during human brain activities. Graph theory analysis (GTA) is a network analysis tool commonly used in functional neuroimaging to study brain networks. Atlas-DOT has not been analyzed with GTA to derive large-scale brain connectivity/networks based on near-infrared spectroscopy (NIRS) measurements. We introduced an automated voxel classification (AVC) method that facilitated the use of GTA with atlas-DOT images by grouping unequal-sized finite element voxels into anatomically meaningful regions of interest within the human brain. The overall approach included volume segmentation, AVC, and cross-correlation. To demonstrate the usefulness of AVC, we applied reproducibility analysis to resting-state functional connectivity measurements conducted from 15 young adults in a two-week period. We also quantified and compared changes in several brain network metrics between young and older adults, which were in agreement with those reported by a previous positron emission tomography study. Overall, this study demonstrated that AVC is a useful means for facilitating integration or combination of atlas-DOT with GTA and thus for quantifying NIRS-based, voxel-wise resting-state functional brain networks.

Comparison of low-contrast detectability between two CT reconstruction algorithms using voxel-based 3D printed textured phantoms.

PubMed

Solomon, Justin; Ba, Alexandre; Bochud, François; Samei, Ehsan

2016-12-01

To use novel voxel-based 3D printed textured phantoms in order to compare low-contrast detectability between two reconstruction algorithms, FBP (filtered-backprojection) and SAFIRE (sinogram affirmed iterative reconstruction) and determine what impact background texture (i.e., anatomical noise) has on estimating the dose reduction potential of SAFIRE. Liver volumes were segmented from 23 abdominal CT cases. The volumes were characterized in terms of texture features from gray-level co-occurrence and run-length matrices. Using a 3D clustered lumpy background (CLB) model, a fitting technique based on a genetic optimization algorithm was used to find CLB textures that were reflective of the liver textures, accounting for CT system factors of spatial blurring and noise. With the modeled background texture as a guide, four cylindrical phantoms (Textures A-C and uniform, 165 mm in diameter, and 30 mm height) were designed, each containing 20 low-contrast spherical signals (6 mm diameter at nominal contrast levels of ∼3.2, 5.2, 7.2, 10, and 14 HU with four repeats per signal). The phantoms were voxelized and input into a commercial multimaterial 3D printer (Object Connex 350), with custom software for voxel-based printing (using principles of digital dithering). Images of the textured phantoms and a corresponding uniform phantom were acquired at six radiation dose levels (SOMATOM Flash, Siemens Healthcare) and observer model detection performance (detectability index of a multislice channelized Hotelling observer) was estimated for each condition (5 contrasts × 6 doses × 2 reconstructions × 4 backgrounds = 240 total conditions). A multivariate generalized regression analysis was performed (linear terms, no interactions, random error term, log link function) to assess whether dose, reconstruction algorithm, signal contrast, and background type have statistically significant effects on detectability. Also, fitted curves of detectability (averaged across contrast levels) as a function of dose were constructed for each reconstruction algorithm and background texture. FBP and SAFIRE were compared for each background type to determine the improvement in detectability at a given dose, and the reduced dose at which SAFIRE had equivalent performance compared to FBP at 100% dose. Detectability increased with increasing radiation dose (P = 2.7 × 10 -59 ) and contrast level (P = 2.2 × 10 -86 ) and was higher in the uniform phantom compared to the textured phantoms (P = 6.9 × 10 -51 ). Overall, SAFIRE had higher d' compared to FBP (P = 0.02). The estimated dose reduction potential of SAFIRE was found to be 8%, 10%, 27%, and 8% for Texture-A, Texture-B, Texture-C and uniform phantoms. In all background types, detectability was higher with SAFIRE compared to FBP. However, the relative improvement observed from SAFIRE was highly dependent on the complexity of the background texture. Iterative algorithms such as SAFIRE should be assessed in the most realistic context possible.

Application of Quantitative MRI for Brain Tissue Segmentation at 1.5 T and 3.0 T Field Strengths

PubMed Central

West, Janne; Blystad, Ida; Engström, Maria; Warntjes, Jan B. M.; Lundberg, Peter

2013-01-01

Background Brain tissue segmentation of white matter (WM), grey matter (GM), and cerebrospinal fluid (CSF) are important in neuroradiological applications. Quantitative Mri (qMRI) allows segmentation based on physical tissue properties, and the dependencies on MR scanner settings are removed. Brain tissue groups into clusters in the three dimensional space formed by the qMRI parameters R1, R2 and PD, and partial volume voxels are intermediate in this space. The qMRI parameters, however, depend on the main magnetic field strength. Therefore, longitudinal studies can be seriously limited by system upgrades. The aim of this work was to apply one recently described brain tissue segmentation method, based on qMRI, at both 1.5 T and 3.0 T field strengths, and to investigate similarities and differences. Methods In vivo qMRI measurements were performed on 10 healthy subjects using both 1.5 T and 3.0 T MR scanners. The brain tissue segmentation method was applied for both 1.5 T and 3.0 T and volumes of WM, GM, CSF and brain parenchymal fraction (BPF) were calculated on both field strengths. Repeatability was calculated for each scanner and a General Linear Model was used to examine the effect of field strength. Voxel-wise t-tests were also performed to evaluate regional differences. Results Statistically significant differences were found between 1.5 T and 3.0 T for WM, GM, CSF and BPF (p<0.001). Analyses of main effects showed that WM was underestimated, while GM and CSF were overestimated on 1.5 T compared to 3.0 T. The mean differences between 1.5 T and 3.0 T were -66 mL WM, 40 mL GM, 29 mL CSF and -1.99% BPF. Voxel-wise t-tests revealed regional differences of WM and GM in deep brain structures, cerebellum and brain stem. Conclusions Most of the brain was identically classified at the two field strengths, although some regional differences were observed. PMID:24066153

Brain structural changes and their correlation with vascular disease in type 2 diabetes mellitus patients: a voxel-based morphometric study.

PubMed

Wang, Chunxia; Fu, Kailiang; Liu, Huaijun; Xing, Fei; Zhang, Songyun

2014-08-15

Voxel-based morphometry has been used in the study of alterations in brain structure in type 1 diabetes mellitus patients. These changes are associated with clinical indices. The age at onset, pathogenesis, and treatment of type 1 diabetes mellitus are different from those for type 2 diabetes mellitus. Thus, type 1 and type 2 diabetes mellitus may have different impacts on brain structure. Only a few studies of the alterations in brain structure in type 2 diabetes mellitus patients using voxel-based morphometry have been conducted, with inconsistent results. We detected subtle changes in the brain structure of 23 cases of type 2 diabetes mellitus, and demonstrated that there was no significant difference between the total volume of gray and white matter of the brain of type 2 diabetes mellitus patients and that in controls. Regional atrophy of gray matter mainly occurred in the right temporal and left occipital cortex, while regional atrophy of white matter involved the right temporal lobe and the right cerebellar hemisphere. The ankle-brachial index in patients with type 2 diabetes mellitus strongly correlated with the volume of brain regions in the default mode network. The ankle-brachial index, followed by the level of glycosylated hemoglobin, most strongly correlated with the volume of gray matter in the right temporal lobe. These data suggest that voxel-based morphometry could detect small structural changes in patients with type 2 diabetes mellitus. Early macrovascular atherosclerosis may play a crucial role in subtle brain atrophy in type 2 diabetes mellitus patients, with chronic hyperglycemia playing a lesser role.

The Monte Carlo code MCPTV--Monte Carlo dose calculation in radiation therapy with carbon ions.

PubMed

Karg, Juergen; Speer, Stefan; Schmidt, Manfred; Mueller, Reinhold

2010-07-07

The Monte Carlo code MCPTV is presented. MCPTV is designed for dose calculation in treatment planning in radiation therapy with particles and especially carbon ions. MCPTV has a voxel-based concept and can perform a fast calculation of the dose distribution on patient CT data. Material and density information from CT are taken into account. Electromagnetic and nuclear interactions are implemented. Furthermore the algorithm gives information about the particle spectra and the energy deposition in each voxel. This can be used to calculate the relative biological effectiveness (RBE) for each voxel. Depth dose distributions are compared to experimental data giving good agreement. A clinical example is shown to demonstrate the capabilities of the MCPTV dose calculation.

Monte Carlo simulation of portal dosimetry on a rectilinear voxel geometry: a variable gantry angle solution.

PubMed

Chin, P W; Spezi, E; Lewis, D G

2003-08-21

A software solution has been developed to carry out Monte Carlo simulations of portal dosimetry using the BEAMnrc/DOSXYZnrc code at oblique gantry angles. The solution is based on an integrated phantom, whereby the effect of incident beam obliquity was included using geometric transformations. Geometric transformations are accurate within +/- 1 mm and +/- 1 degrees with respect to exact values calculated using trigonometry. An application in portal image prediction of an inhomogeneous phantom demonstrated good agreement with measured data, where the root-mean-square of the difference was under 2% within the field. Thus, we achieved a dose model framework capable of handling arbitrary gantry angles, voxel-by-voxel phantom description and realistic particle transport throughout the geometry.

An Expanded Multi-scale Monte Carlo Simulation Method for Personalized Radiobiological Effect Estimation in Radiotherapy: a feasibility study

NASA Astrophysics Data System (ADS)

Zhang, Ying; Feng, Yuanming; Wang, Wei; Yang, Chengwen; Wang, Ping

2017-03-01

A novel and versatile “bottom-up” approach is developed to estimate the radiobiological effect of clinic radiotherapy. The model consists of multi-scale Monte Carlo simulations from organ to cell levels. At cellular level, accumulated damages are computed using a spectrum-based accumulation algorithm and predefined cellular damage database. The damage repair mechanism is modeled by an expanded reaction-rate two-lesion kinetic model, which were calibrated through replicating a radiobiological experiment. Multi-scale modeling is then performed on a lung cancer patient under conventional fractionated irradiation. The cell killing effects of two representative voxels (isocenter and peripheral voxel of the tumor) are computed and compared. At microscopic level, the nucleus dose and damage yields vary among all nucleuses within the voxels. Slightly larger percentage of cDSB yield is observed for the peripheral voxel (55.0%) compared to the isocenter one (52.5%). For isocenter voxel, survival fraction increase monotonically at reduced oxygen environment. Under an extreme anoxic condition (0.001%), survival fraction is calculated to be 80% and the hypoxia reduction factor reaches a maximum value of 2.24. In conclusion, with biological-related variations, the proposed multi-scale approach is more versatile than the existing approaches for evaluating personalized radiobiological effects in radiotherapy.

A new method for photon transport in Monte Carlo simulation

NASA Astrophysics Data System (ADS)

Sato, T.; Ogawa, K.

1999-12-01

Monte Carlo methods are used to evaluate data methods such as scatter and attenuation compensation in single photon emission CT (SPECT), treatment planning in radiation therapy, and in many industrial applications. In Monte Carlo simulation, photon transport requires calculating the distance from the location of the emitted photon to the nearest boundary of each uniform attenuating medium along its path of travel, and comparing this distance with the length of its path generated at emission. Here, the authors propose a new method that omits the calculation of the location of the exit point of the photon from each voxel and of the distance between the exit point and the original position. The method only checks the medium of each voxel along the photon's path. If the medium differs from that in the voxel from which the photon was emitted, the authors calculate the location of the entry point in the voxel, and the length of the path is compared with the mean free path length generated by a random number. Simulations using the MCAT phantom show that the ratios of the calculation time were 1.0 for the voxel-based method, and 0.51 for the proposed method with a 256/spl times/256/spl times/256 matrix image, thereby confirming the effectiveness of the algorithm.

Voxel2MCNP: a framework for modeling, simulation and evaluation of radiation transport scenarios for Monte Carlo codes.

PubMed

Pölz, Stefan; Laubersheimer, Sven; Eberhardt, Jakob S; Harrendorf, Marco A; Keck, Thomas; Benzler, Andreas; Breustedt, Bastian

2013-08-21

The basic idea of Voxel2MCNP is to provide a framework supporting users in modeling radiation transport scenarios using voxel phantoms and other geometric models, generating corresponding input for the Monte Carlo code MCNPX, and evaluating simulation output. Applications at Karlsruhe Institute of Technology are primarily whole and partial body counter calibration and calculation of dose conversion coefficients. A new generic data model describing data related to radiation transport, including phantom and detector geometries and their properties, sources, tallies and materials, has been developed. It is modular and generally independent of the targeted Monte Carlo code. The data model has been implemented as an XML-based file format to facilitate data exchange, and integrated with Voxel2MCNP to provide a common interface for modeling, visualization, and evaluation of data. Also, extensions to allow compatibility with several file formats, such as ENSDF for nuclear structure properties and radioactive decay data, SimpleGeo for solid geometry modeling, ImageJ for voxel lattices, and MCNPX's MCTAL for simulation results have been added. The framework is presented and discussed in this paper and example workflows for body counter calibration and calculation of dose conversion coefficients is given to illustrate its application.

Multi-Atlas Based Segmentation of Brainstem Nuclei from MR Images by Deep Hyper-Graph Learning.

PubMed

Dong, Pei; Guo, Yangrong; Gao, Yue; Liang, Peipeng; Shi, Yonghong; Wang, Qian; Shen, Dinggang; Wu, Guorong

2016-10-01

Accurate segmentation of brainstem nuclei (red nucleus and substantia nigra) is very important in various neuroimaging applications such as deep brain stimulation and the investigation of imaging biomarkers for Parkinson's disease (PD). Due to iron deposition during aging, image contrast in the brainstem is very low in Magnetic Resonance (MR) images. Hence, the ambiguity of patch-wise similarity makes the recently successful multi-atlas patch-based label fusion methods have difficulty to perform as competitive as segmenting cortical and sub-cortical regions from MR images. To address this challenge, we propose a novel multi-atlas brainstem nuclei segmentation method using deep hyper-graph learning. Specifically, we achieve this goal in three-fold. First , we employ hyper-graph to combine the advantage of maintaining spatial coherence from graph-based segmentation approaches and the benefit of harnessing population priors from multi-atlas based framework. Second , besides using low-level image appearance, we also extract high-level context features to measure the complex patch-wise relationship. Since the context features are calculated on a tentatively estimated label probability map, we eventually turn our hyper-graph learning based label propagation into a deep and self-refining model. Third , since anatomical labels on some voxels (usually located in uniform regions) can be identified much more reliably than other voxels (usually located at the boundary between two regions), we allow these reliable voxels to propagate their labels to the nearby difficult-to-label voxels. Such hierarchical strategy makes our proposed label fusion method deep and dynamic. We evaluate our proposed label fusion method in segmenting substantia nigra (SN) and red nucleus (RN) from 3.0 T MR images, where our proposed method achieves significant improvement over the state-of-the-art label fusion methods.

A methodological assessment of studies that use voxel-based morphometry to study neural changes in tinnitus patients.

PubMed

Scott-Wittenborn, Nicholas; Karadaghy, Omar A; Piccirillo, Jay F; Peelle, Jonathan E

2017-11-01

The scientific understanding of tinnitus and its etiology has transitioned from thinking of tinnitus as solely a peripheral auditory problem to an increasing awareness that cortical networks may play a critical role in tinnitus percept or bother. With this change, studies that seek to use structural brain imaging techniques to better characterize tinnitus patients have become more common. These studies include using voxel-based morphometry (VBM) to determine if there are differences in regional gray matter volume in individuals who suffer from tinnitus and those who do not. However, studies using VBM in patients with tinnitus have produced inconsistent and sometimes contradictory results. This paper is a systematic review of all of the studies to date that have used VBM to study regional gray matter volume in people with tinnitus, and explores ways in which methodological differences in these studies may account for their heterogeneous results. We also aim to provide guidance on how to conduct future studies using VBM to produce more reproducible results to further our understanding of disease processes such as tinnitus. Studies about tinnitus and VBM were searched for using PubMed and Embase. These returned 15 and 25 results respectively. Of these, nine met the study criteria and were included for review. An additional 5 studies were identified in the literature as pertinent to the topic at hand and were added to the review, for a total of 13 studies. There was significant heterogeneity among the studies in several areas, including inclusion and exclusion criteria, software programs, and statistical analysis. We were not able to find publicly shared data or code for any study. The differences in study design, software analysis, and statistical methodology make direct comparisons between the different studies difficult. Especially problematic are the differences in the inclusion and exclusion criteria of the study, and the statistical design of the studies, both of which could radically alter findings. Thus, heterogeneity has complicated efforts to explore the etiology of tinnitus using structural MRI. There is a pressing need to standardize the use of VBM when evaluating tinnitus patients. While some heterogeneity is expected given the rapid advances in the field, more can be done to ensure that there is internal validity between studies. Copyright © 2017 Elsevier B.V. All rights reserved.

Cortical hypometabolism and hypoperfusion in Parkinson's disease is extensive: probably even at early disease stages.

PubMed

Borghammer, Per; Chakravarty, Mallar; Jonsdottir, Kristjana Yr; Sato, Noriko; Matsuda, Hiroshi; Ito, Kengo; Arahata, Yutaka; Kato, Takashi; Gjedde, Albert

2010-05-01

Recent cerebral blood flow (CBF) and glucose consumption (CMRglc) studies of Parkinson's disease (PD) revealed conflicting results. Using simulated data, we previously demonstrated that the often-reported subcortical hypermetabolism in PD could be explained as an artifact of biased global mean (GM) normalization, and that low-magnitude, extensive cortical hypometabolism is best detected by alternative data-driven normalization methods. Thus, we hypothesized that PD is characterized by extensive cortical hypometabolism but no concurrent widespread subcortical hypermetabolism and tested it on three independent samples of PD patients. We compared SPECT CBF images of 32 early-stage and 33 late-stage PD patients with that of 60 matched controls. We also compared PET FDG images from 23 late-stage PD patients with that of 13 controls. Three different normalization methods were compared: (1) GM normalization, (2) cerebellum normalization, (3) reference cluster normalization (Yakushev et al.). We employed standard voxel-based statistics (fMRIstat) and principal component analysis (SSM). Additionally, we performed a meta-analysis of all quantitative CBF and CMRglc studies in the literature to investigate whether the global mean (GM) values in PD are decreased. Voxel-based analysis with GM normalization and the SSM method performed similarly, i.e., both detected decreases in small cortical clusters and concomitant increases in extensive subcortical regions. Cerebellum normalization revealed more widespread cortical decreases but no subcortical increase. In all comparisons, the Yakushev method detected nearly identical patterns of very extensive cortical hypometabolism. Lastly, the meta-analyses demonstrated that global CBF and CMRglc values are decreased in PD. Based on the results, we conclude that PD most likely has widespread cortical hypometabolism, even at early disease stages. In contrast, extensive subcortical hypermetabolism is probably not a feature of PD.

Striatal dopamine (D2) receptor availability predicts socially desirable responding.

PubMed

Reeves, Suzanne J; Mehta, Mitul A; Montgomery, Andrew J; Amiras, Dimitri; Egerton, Alice; Howard, Robert J; Grasby, Paul M

2007-02-15

Research in non-human primates has implicated striatal dopamine (D2) receptor function in the expression of social dominance--a fundamental component of social extraversion. We predicted that trait extraversion - indexed by the revised Eysenck Personality Questionnaire (EPQ-R) - would correlate with striatal DA (D2) receptor measures - indexed by [(11)C]-Raclopride binding potential (BP) - in 28 healthy post-menopausal females (mean age=75 years; range=58-91 years). Region of interest (ROI) and voxel-based statistical parametric mapping (SPM) analyses were performed, using a reference tissue model for [(11)C]-Raclopride. ROI analysis showed moderately significant negative correlations between extraversion and BP measures in the left caudate and between psychoticism scores and BP in the right putamen. Unexpectedly, scores on the Lie scale, a measure of socially desirable responding, were significantly and negatively correlated with BP measures in the putamen and survived Bonferroni correction on the right side. After controlling for the potential confounding of self-report bias in high Lie scorers, only the correlation between Lie scores and BP measures in the right putamen remained significant. Voxel-based analysis showed only Lie scores to be significantly and negatively correlated with BP measures in the right putamen. We explored this association further by applying an ROI-based approach to data on a previously scanned sample of young adults (n=13) and found a similar pattern of association, which achieved trend level significance in the right putamen. Although unanticipated, the relationship observed between BP measures in the right putamen and Lie scores is consistent with dopaminergic involvement in socially rewarding behaviour. How this relates to dopaminergic tone will need to be further explored.

Detection of infarct lesions from single MRI modality using inconsistency between voxel intensity and spatial location--a 3-D automatic approach.

PubMed

Shen, Shan; Szameitat, André J; Sterr, Annette

2008-07-01

Detection of infarct lesions using traditional segmentation methods is always problematic due to intensity similarity between lesions and normal tissues, so that multispectral MRI modalities were often employed for this purpose. However, the high costs of MRI scan and the severity of patient conditions restrict the collection of multiple images. Therefore, in this paper, a new 3-D automatic lesion detection approach was proposed, which required only a single type of anatomical MRI scan. It was developed on a theory that, when lesions were present, the voxel-intensity-based segmentation and the spatial-location-based tissue distribution should be inconsistent in the regions of lesions. The degree of this inconsistency was calculated, which indicated the likelihood of tissue abnormality. Lesions were identified when the inconsistency exceeded a defined threshold. In this approach, the intensity-based segmentation was implemented by the conventional fuzzy c-mean (FCM) algorithm, while the spatial location of tissues was provided by prior tissue probability maps. The use of simulated MRI lesions allowed us to quantitatively evaluate the performance of the proposed method, as the size and location of lesions were prespecified. The results showed that our method effectively detected lesions with 40-80% signal reduction compared to normal tissues (similarity index > 0.7). The capability of the proposed method in practice was also demonstrated on real infarct lesions from 15 stroke patients, where the lesions detected were in broad agreement with true lesions. Furthermore, a comparison to a statistical segmentation approach presented in the literature suggested that our 3-D lesion detection approach was more reliable. Future work will focus on adapting the current method to multiple sclerosis lesion detection.

Multi-region statistical shape model for cochlear implantation

NASA Astrophysics Data System (ADS)

Romera, Jordi; Kjer, H. Martin; Piella, Gemma; Ceresa, Mario; González Ballester, Miguel A.

2016-03-01

Statistical shape models are commonly used to analyze the variability between similar anatomical structures and their use is established as a tool for analysis and segmentation of medical images. However, using a global model to capture the variability of complex structures is not enough to achieve the best results. The complexity of a proper global model increases even more when the amount of data available is limited to a small number of datasets. Typically, the anatomical variability between structures is associated to the variability of their physiological regions. In this paper, a complete pipeline is proposed for building a multi-region statistical shape model to study the entire variability from locally identified physiological regions of the inner ear. The proposed model, which is based on an extension of the Point Distribution Model (PDM), is built for a training set of 17 high-resolution images (24.5 μm voxels) of the inner ear. The model is evaluated according to its generalization ability and specificity. The results are compared with the ones of a global model built directly using the standard PDM approach. The evaluation results suggest that better accuracy can be achieved using a regional modeling of the inner ear.

Voxel-Based Morphometry for Separation of Schizophrenia From Other Types of Psychosis in First-Episode Psychosis: Diagnostic Test Review.

PubMed

Palaniyappan, Lena; Maayan, Nicola; Bergman, Hanna; Davenport, Clare; Adams, Clive E; Soares-Weiser, Karla

2016-03-01

Subtle but widespread deficit in the cortical and subcortical grey matter is a consistent neuroimaging observation in schizophrenia. Several studies have used voxel based morphometry (VBM) to investigate the nature of this structural deficit. We conducted a diagnostic test review to explore the diagnostic potential of VBM in differentiating schizophrenia from other types of first-episode psychoses. © The Author 2016. Published by Oxford University Press on behalf of the Maryland Psychiatric Research Center. All rights reserved. For permissions, please email: journals.permissions@oup.com.

Construction of anthropomorphic hybrid, dual-lattice voxel models for optimizing image quality and dose in radiography

NASA Astrophysics Data System (ADS)

Petoussi-Henss, Nina; Becker, Janine; Greiter, Matthias; Schlattl, Helmut; Zankl, Maria; Hoeschen, Christoph

2014-03-01

In radiography there is generally a conflict between the best image quality and the lowest possible patient dose. A proven method of dosimetry is the simulation of radiation transport in virtual human models (i.e. phantoms). However, while the resolution of these voxel models is adequate for most dosimetric purposes, they cannot provide the required organ fine structures necessary for the assessment of the imaging quality. The aim of this work is to develop hybrid/dual-lattice voxel models (called also phantoms) as well as simulation methods by which patient dose and image quality for typical radiographic procedures can be determined. The results will provide a basis to investigate by means of simulations the relationships between patient dose and image quality for various imaging parameters and develop methods for their optimization. A hybrid model, based on NURBS (Non Linear Uniform Rational B-Spline) and PM (Polygon Mesh) surfaces, was constructed from an existing voxel model of a female patient. The organs of the hybrid model can be then scaled and deformed in a non-uniform way i.e. organ by organ; they can be, thus, adapted to patient characteristics without losing their anatomical realism. Furthermore, the left lobe of the lung was substituted by a high resolution lung voxel model, resulting in a dual-lattice geometry model. "Dual lattice" means in this context the combination of voxel models with different resolution. Monte Carlo simulations of radiographic imaging were performed with the code EGS4nrc, modified such as to perform dual lattice transport. Results are presented for a thorax examination.

Clinical evaluation of semi-automatic open-source algorithmic software segmentation of the mandibular bone: Practical feasibility and assessment of a new course of action.

PubMed

Wallner, Jürgen; Hochegger, Kerstin; Chen, Xiaojun; Mischak, Irene; Reinbacher, Knut; Pau, Mauro; Zrnc, Tomislav; Schwenzer-Zimmerer, Katja; Zemann, Wolfgang; Schmalstieg, Dieter; Egger, Jan

2018-01-01

Computer assisted technologies based on algorithmic software segmentation are an increasing topic of interest in complex surgical cases. However-due to functional instability, time consuming software processes, personnel resources or licensed-based financial costs many segmentation processes are often outsourced from clinical centers to third parties and the industry. Therefore, the aim of this trial was to assess the practical feasibility of an easy available, functional stable and licensed-free segmentation approach to be used in the clinical practice. In this retrospective, randomized, controlled trail the accuracy and accordance of the open-source based segmentation algorithm GrowCut was assessed through the comparison to the manually generated ground truth of the same anatomy using 10 CT lower jaw data-sets from the clinical routine. Assessment parameters were the segmentation time, the volume, the voxel number, the Dice Score and the Hausdorff distance. Overall semi-automatic GrowCut segmentation times were about one minute. Mean Dice Score values of over 85% and Hausdorff Distances below 33.5 voxel could be achieved between the algorithmic GrowCut-based segmentations and the manual generated ground truth schemes. Statistical differences between the assessment parameters were not significant (p<0.05) and correlation coefficients were close to the value one (r > 0.94) for any of the comparison made between the two groups. Complete functional stable and time saving segmentations with high accuracy and high positive correlation could be performed by the presented interactive open-source based approach. In the cranio-maxillofacial complex the used method could represent an algorithmic alternative for image-based segmentation in the clinical practice for e.g. surgical treatment planning or visualization of postoperative results and offers several advantages. Due to an open-source basis the used method could be further developed by other groups or specialists. Systematic comparisons to other segmentation approaches or with a greater data amount are areas of future works.

MIRD Pamphlet No. 23: Quantitative SPECT for Patient-Specific 3-Dimensional Dosimetry in Internal Radionuclide Therapy

PubMed Central

Dewaraja, Yuni K.; Frey, Eric C.; Sgouros, George; Brill, A. Bertrand; Roberson, Peter; Zanzonico, Pat B.; Ljungberg, Michael

2012-01-01

In internal radionuclide therapy, a growing interest in voxel-level estimates of tissue-absorbed dose has been driven by the desire to report radiobiologic quantities that account for the biologic consequences of both spatial and temporal nonuniformities in these dose estimates. This report presents an overview of 3-dimensional SPECT methods and requirements for internal dosimetry at both regional and voxel levels. Combined SPECT/CT image-based methods are emphasized, because the CT-derived anatomic information allows one to address multiple technical factors that affect SPECT quantification while facilitating the patient-specific voxel-level dosimetry calculation itself. SPECT imaging and reconstruction techniques for quantification in radionuclide therapy are not necessarily the same as those designed to optimize diagnostic imaging quality. The current overview is intended as an introduction to an upcoming series of MIRD pamphlets with detailed radionuclide-specific recommendations intended to provide best-practice SPECT quantification–based guidance for radionuclide dosimetry. PMID:22743252

Predicting drought propagation within peat layers using a three dimensionally explicit voxel based model

NASA Astrophysics Data System (ADS)

Condro, A. A.; Pawitan, H.; Risdiyanto, I.

2018-05-01

Peatlands are very vulnerable to widespread fires during dry seasons, due to availability of aboveground fuel biomass on the surface and belowground fuel biomass on the sub-surface. Hence, understanding drought propagation occurring within peat layers is crucial with regards to disaster mitigation activities on peatlands. Using a three dimensionally explicit voxel-based model of peatland hydrology, this study predicted drought propagation time lags into sub-surface peat layers after drought events occurrence on the surface of about 1 month during La-Nina and 2.5 months during El-Nino. The study was carried out on a high-conservation-value area of oil palm plantation in West Kalimantan. Validity of the model was evaluated and its applicability for disaster mitigation was discussed. The animations of simulated voxels are available at: goo.gl/HDRMYN (El-Nino 2015 episode) and goo.gl/g1sXPl (La-Nina 2016 episode). The model is available at: goo.gl/RiuMQz.

Intelligence and EEG current density using low-resolution electromagnetic tomography (LORETA).

PubMed

Thatcher, R W; North, D; Biver, C

2007-02-01

The purpose of this study was to compare EEG current source densities in high IQ subjects vs. low IQ subjects. Resting eyes closed EEG was recorded from 19 scalp locations with a linked ears reference from 442 subjects ages 5 to 52 years. The Wechsler Intelligence Test was administered and subjects were divided into low IQ (< or =90), middle IQ (>90 to <120) and high IQ (> or =120) groups. Low-resolution electromagnetic tomographic current densities (LORETA) from 2,394 cortical gray matter voxels were computed from 1-30 Hz based on each subject's EEG. Differences in current densities using t tests, multivariate analyses of covariance, and regression analyses were used to evaluate the relationships between IQ and current density in Brodmann area groupings of cortical gray matter voxels. Frontal, temporal, parietal, and occipital regions of interest (ROIs) consistently exhibited a direct relationship between LORETA current density and IQ. Maximal t test differences were present at 4 Hz, 9 Hz, 13 Hz, 18 Hz, and 30 Hz with different anatomical regions showing different maxima. Linear regression fits from low to high IQ groups were statistically significant (P < 0.0001). Intelligence is directly related to a general level of arousal and to the synchrony of neural populations driven by thalamo-cortical resonances. A traveling frame model of sequential microstates is hypothesized to explain the results.

Sensitivity estimation in time-of-flight list-mode positron emission tomography

DOE Office of Scientific and Technical Information (OSTI.GOV)

Herraiz, J. L.; Sitek, A., E-mail: sarkadiu@gmail.com

Purpose: An accurate quantification of the images in positron emission tomography (PET) requires knowing the actual sensitivity at each voxel, which represents the probability that a positron emitted in that voxel is finally detected as a coincidence of two gamma rays in a pair of detectors in the PET scanner. This sensitivity depends on the characteristics of the acquisition, as it is affected by the attenuation of the annihilation gamma rays in the body, and possible variations of the sensitivity of the scanner detectors. In this work, the authors propose a new approach to handle time-of-flight (TOF) list-mode PET data,more » which allows performing either or both, a self-attenuation correction, and self-normalization correction based on emission data only. Methods: The authors derive the theory using a fully Bayesian statistical model of complete data. The authors perform an initial evaluation of algorithms derived from that theory and proposed in this work using numerical 2D list-mode simulations with different TOF resolutions and total number of detected coincidences. Effects of randoms and scatter are not simulated. Results: The authors found that proposed algorithms successfully correct for unknown attenuation and scanner normalization for simulated 2D list-mode TOF-PET data. Conclusions: A new method is presented that can be used for corrections for attenuation and normalization (sensitivity) using TOF list-mode data.« less

Intrathoracic Fat Measurements Using Multidetector Computed Tomography (MDCT): Feasibility and Reproducibility

PubMed Central

Stojanovska, Jadranka; Ibrahim, El-Sayed H.; Chughtai, Aamer R.; Jackson, Elizabeth A.; Gross, Barry H.; Jacobson, Jon A.; Tsodikov, Alexander; Daneshvar, Brian; Long, Benjamin D.; Chenevert, Thomas L.; Kazerooni, Ella A.

2017-01-01

Intrathoracic fat volume, more specifically, epicardial fat volume, is an emerging imaging biomarker of adverse cardiovascular events. The purpose of this work is to show the feasibility and reproducibility of intrathoracic fat volume measurement applied to contrast-enhanced multidetector computed tomography images. A retrospective cohort study of 62 subjects free of cardiovascular disease (55% females, age = 49 ± 11 years) conducted from 2008 to 2011 formed the study group. Intrathoracic fat volume was defined as all fat voxels measuring −50 to −250 Hounsfield Unit within the intrathoracic cavity from the level of the pulmonary artery bifurcation to the heart apex. The intrathoracic fat was separated into epicardial and extrapericardial fat by tracing the pericardium. The measurements were obtained by 2 readers and compared for interrater reproducibility. The fat volume measurements for the study group were 141 ± 72 cm3 for intrathoracic fat, 58 ± 27 cm3 for epicardial fat, and 84 ± 50 cm3 for extrapericardial fat. There was no statistically significant difference in intrathoracic fat volume measurements between the 2 readers, with correlation coefficients of 0.88 (P = .55) for intrathoracic fat volume and −0.12 (P = .33) for epicardial fat volume. Voxel-based measurement of intrathoracic fat, including the separation into epicardial and extrapericardial fat, is feasible and highly reproducible from multidetector computed tomography scans. PMID:28626797

Lifetime use of cannabis from longitudinal assessments, cannabinoid receptor (CNR1) variation, and reduced volume of the right anterior cingulate

PubMed Central

Hill, Shirley Y.; Sharma, Vinod; Jones, Bobby L.

2016-01-01

Lifetime measures of cannabis use and co-occurring exposures were obtained from a longitudinal cohort followed an average of 13 years at the time they received a structural MRI scan. MRI scans were analyzed for 88 participants (mean age=25.9 years), 34 of whom were regular users of cannabis. Whole brain voxel based morphometry analyses (SPM8) were conducted using 50 voxel clusters at p=0.005. Controlling for age, familial risk, and gender, we found reduced volume in Regular Users compared to Non-Users, in the lingual gyrus, anterior cingulum (right and left), and the rolandic operculum (right). The right anterior cingulum reached family-wise error statistical significance at p=0.001, controlling for personal lifetime use of alcohol and cigarettes and any prenatal exposures. CNR1 haplotypes were formed from four CNR1 SNPs (rs806368, rs1049353, rs2023239, and rs6454674) and tested with level of cannabis exposure to assess their interactive effects on the lingual gyrus, cingulum (right and left) and rolandic operculum, regions showing cannabis exposure effects in the SPM8 analyses. These analyses used mixed model analyses (SPSS) to control for multiple potentially confounding variables. Level of cannabis exposure was associated with decreased volume of the right anterior cingulum and showed interaction effects with haplotype variation. PMID:27500453

Quantifying intermediate-frequency heterogeneities of SOFC electrodes using X-ray computed tomography

DOE Office of Scientific and Technical Information (OSTI.GOV)

Epting, William K.; Mansley, Zachary; Menasche, David B.

2017-03-03

The electrodes in solid oxide fuel cells (SOFCs) consist of three phases interconnected in three dimensions. The volume needed to describe quantitatively such microstructures depends on several lengths scales, which are functions of materials properties and fabrication methods. This work focuses on quantifying the volume needed to represent “intermediate frequency” heterogeneities in electrodes of a commercial SOFC using X-ray computed tomography (CT) over two different length scales. Electrode volumes of 150 x 150 x 9 μm 3 were extracted from a synchrotron-based micro-CT data set, with 13 μm 3 voxels. 13.6 x 19.8 x 19.4 μm 3 of the cathodemore » and 26.3 x 24.8 x 15.7 μm 3 of the anode were extracted from laboratory nano-CT data sets, both with 65 3 nm 3 voxels. After comparing the variation across sub-regions for the greyscale values from the micro-CT, and for the phase fractions and triple phase boundary densities from the nano-CT, it was found that the sub-region length scales needed to yield statistically similar average values were an order of magnitude larger than those expected to capture the “high frequency” heterogeneity related to the discrete nature of the three phases in electrodes. In conclusion, the challenge of quantifying such electrodes using available experimental methods is discussed.« less

From blood oxygenation level dependent (BOLD) signals to brain temperature maps.

PubMed

Sotero, Roberto C; Iturria-Medina, Yasser

2011-11-01

A theoretical framework is presented for converting Blood Oxygenation Level Dependent (BOLD) images to brain temperature maps, based on the idea that disproportional local changes in cerebral blood flow (CBF) as compared with cerebral metabolic rate of oxygen consumption (CMRO₂) during functional brain activity, lead to both brain temperature changes and the BOLD effect. Using an oxygen limitation model and a BOLD signal model, we obtain a transcendental equation relating CBF and CMRO₂ changes with the corresponding BOLD signal, which is solved in terms of the Lambert W function. Inserting this result in the dynamic bioheat equation describing the rate of temperature changes in the brain, we obtain a nonautonomous ordinary differential equation that depends on the BOLD response, which is solved numerically for each brain voxel. Temperature maps obtained from a real BOLD dataset registered in an attention to visual motion experiment were calculated, obtaining temperature variations in the range: (-0.15, 0.1) which is consistent with experimental results. The statistical analysis revealed that significant temperature activations have a similar distribution pattern than BOLD activations. An interesting difference was the activation of the precuneus in temperature maps, a region involved in visuospatial processing, an effect that was not observed on BOLD maps. Furthermore, temperature maps were more localized to gray matter regions than the original BOLD maps, showing less activated voxels in white matter and cerebrospinal fluid.

Sensitivity estimation in time-of-flight list-mode positron emission tomography.

PubMed

Herraiz, J L; Sitek, A

2015-11-01

An accurate quantification of the images in positron emission tomography (PET) requires knowing the actual sensitivity at each voxel, which represents the probability that a positron emitted in that voxel is finally detected as a coincidence of two gamma rays in a pair of detectors in the PET scanner. This sensitivity depends on the characteristics of the acquisition, as it is affected by the attenuation of the annihilation gamma rays in the body, and possible variations of the sensitivity of the scanner detectors. In this work, the authors propose a new approach to handle time-of-flight (TOF) list-mode PET data, which allows performing either or both, a self-attenuation correction, and self-normalization correction based on emission data only. The authors derive the theory using a fully Bayesian statistical model of complete data. The authors perform an initial evaluation of algorithms derived from that theory and proposed in this work using numerical 2D list-mode simulations with different TOF resolutions and total number of detected coincidences. Effects of randoms and scatter are not simulated. The authors found that proposed algorithms successfully correct for unknown attenuation and scanner normalization for simulated 2D list-mode TOF-PET data. A new method is presented that can be used for corrections for attenuation and normalization (sensitivity) using TOF list-mode data.

Voxel-based statistical analysis of thalamic glucose metabolism in traumatic brain injury: relationship with consciousness and cognition.

PubMed

Lull, Nuria; Noé, Enrique; Lull, Juan José; García-Panach, Javier; Chirivella, Javier; Ferri, Joan; López-Aznar, Diego; Sopena, Pablo; Robles, Montse

2010-01-01

To study the relationship between thalamic glucose metabolism and neurological outcome after severe traumatic brain injury (TBI). Forty-nine patients with severe and closed TBI and 10 healthy control subjects with (18)F-FDG PET were studied. Patients were divided into three groups: MCS&VS group (n = 17), patients in a vegetative or a minimally conscious state; In-PTA group (n = 12), patients in a state of post-traumatic amnesia (PTA); and Out-PTA group (n = 20), patients who had emerged from PTA. SPM5 software implemented in MATLAB 7 was used to determine the quantitative differences between patients and controls. FDG-PET images were spatially normalized and an automated thalamic ROI mask was generated. Group differences were analysed with two sample voxel-wise t-tests. Thalamic hypometabolism was the most prominent in patients with low consciousness (MCS&VS group) and the thalamic hypometabolism in the In-PTA group was more prominent than that in the Out-PTA group. Healthy control subjects showed the greatest thalamic metabolism. These differences in metabolism were more pronounced in the internal regions of the thalamus. The results confirm the vulnerability of the thalamus to suffer the effect of the dynamic forces generated during a TBI. Patients with thalamic hypometabolism could represent a sub-set of subjects that are highly vulnerable to neurological disability after TBI.

Statistical segmentation of multidimensional brain datasets

NASA Astrophysics Data System (ADS)

Desco, Manuel; Gispert, Juan D.; Reig, Santiago; Santos, Andres; Pascau, Javier; Malpica, Norberto; Garcia-Barreno, Pedro

2001-07-01

This paper presents an automatic segmentation procedure for MRI neuroimages that overcomes part of the problems involved in multidimensional clustering techniques like partial volume effects (PVE), processing speed and difficulty of incorporating a priori knowledge. The method is a three-stage procedure: 1) Exclusion of background and skull voxels using threshold-based region growing techniques with fully automated seed selection. 2) Expectation Maximization algorithms are used to estimate the probability density function (PDF) of the remaining pixels, which are assumed to be mixtures of gaussians. These pixels can then be classified into cerebrospinal fluid (CSF), white matter and grey matter. Using this procedure, our method takes advantage of using the full covariance matrix (instead of the diagonal) for the joint PDF estimation. On the other hand, logistic discrimination techniques are more robust against violation of multi-gaussian assumptions. 3) A priori knowledge is added using Markov Random Field techniques. The algorithm has been tested with a dataset of 30 brain MRI studies (co-registered T1 and T2 MRI). Our method was compared with clustering techniques and with template-based statistical segmentation, using manual segmentation as a gold-standard. Our results were more robust and closer to the gold-standard.

Bone marrow fat quantification in the presence of trabecular bone: initial comparison between water-fat imaging and single-voxel MRS

PubMed Central

Karampinos, Dimitrios C.; Melkus, Gerd; Baum, Thomas; Bauer, Jan S.; Rummeny, Ernst J.; Krug, Roland

2013-01-01

Purpose The purpose of the present study was to test the relative performance of chemical shift-based water-fat imaging in measuring bone marrow fat fraction in the presence of trabecular bone, having as reference standard the single-voxel magnetic resonance spectroscopy (MRS). Methods Six-echo gradient echo imaging and single-voxel MRS measurements were performed on the proximal femur of seven healthy volunteers. The bone marrow fat spectrum was characterized based on the magnitude of measurable fat peaks and an a priori knowledge of the chemical structure of triglycerides, in order to accurately extract the water peak from the overlapping broad fat peaks in MRS. The imaging-based fat fraction results were then compared to the MRS-based results both without and with taking into consideration the presence of short T2* water components in MRS. Results There was a significant underestimation of the fat fraction using the MRS model not accounting for short T2* species with respect to the imaging-based water fraction. A good equivalency was observed between the fat fraction using the MRS model accounting for short T2* species and the imaging-based fat fraction (R2=0.87). Conclusion The consideration of the short T2* water species effect on bone marrow fat quantification is essential when comparing MRS-based and imaging-based fat fraction results. PMID:23657998

Characterization of the Spatial Structure of Local Functional Connectivity Using Multidistance Average Correlation Measures.

PubMed

Macià, Dídac; Pujol, Jesus; Blanco-Hinojo, Laura; Martínez-Vilavella, Gerard; Martín-Santos, Rocío; Deus, Joan

2018-06-01

There is ample evidence from basic research in neuroscience of the importance of local corticocortical networks. Millimetric resolution is achievable with current functional magnetic resonance imaging (fMRI) scanners and sequences, and consequently a number of "local" activity similarity measures have been defined to describe patterns of segregation and integration at this spatial scale. We have introduced the use of IsoDistant Average Correlation (IDAC), easily defined as the average fMRI temporal correlation of a given voxel with other voxels placed at increasingly separated isodistant intervals, to characterize the curve of local fMRI signal similarities. IDAC curves can be statistically compared using parametric multivariate statistics. Furthermore, by using red-green-blue color coding to display jointly IDAC values belonging to three different distance lags, IDAC curves can also be displayed as multidistance IDAC maps. We applied IDAC analysis to a sample of 41 subjects scanned under two different conditions, a resting state and an auditory-visual continuous stimulation. Multidistance IDAC mapping was able to discriminate between gross anatomofunctional cortical areas and, moreover, was sensitive to modulation between the two brain conditions in areas known to activate and deactivate during audiovisual tasks. Unlike previous fMRI local similarity measures already in use, our approach draws special attention to the continuous smooth pattern of local functional connectivity.

Cerebral morphology and dopamine D2/D3receptor distribution in humans: A combined [18F]fallypride and voxel-based morphometry study

PubMed Central

Woodward, Neil D.; Zald, David H.; Ding, Zhaohua; Riccardi, Patrizia; Ansari, M. Sib; Baldwin, Ronald M.; Cowan, Ronald L.; Li, Rui; Kessler, Robert M.

2009-01-01

The relationship between cerebral morphology and the expression of dopamine receptors has not been extensively studied in humans. Elucidation of such relationships may have important methodological implications for clinical studies of dopamine receptor ligand binding differences between control and patient groups. The association between cerebral morphology and dopamine receptor distribution was examined in 45 healthy subjects who completed T1-weighted structural MRI and PET scanning with the D2/D3 ligand [18F]fallypride. Optimized voxel-based morphometry was used to create grey matter volume and density images. Grey matter volume and density images were correlated with binding potential (BPND) images on a voxel-by-voxel basis using the Biological Parametric Mapping toolbox. Associations between cerebral morphology and BPND were also examined for selected regions-of-interest (ROIs) after spatial normalization. Voxel-wise analyses indicated that grey matter volume and density positively correlated with BPND throughout the midbrain, including the substantia nigra. Positive correlations were observed in medial cortical areas, including anterior cingulate and medial prefrontal cortex, and circumscribed regions of the temporal, frontal, and parietal lobes. ROI analyses revealed significant positive correlations between BPND and cerebral morphology in the caudate, thalamus, and amygdala. Few negative correlations between morphology and BPND were observed. Overall, grey matter density appeared more strongly correlated with BPND than grey matter volume. Cerebral morphology, particularly grey matter density, correlates with [18F]fallypride BPND in a regionally specific manner. Clinical studies comparing dopamine receptor availability between clinical and control groups may benefit by accounting for potential differences in cerebral morphology that exist even after spatial normalization. PMID:19457373

Cerebral morphology and dopamine D2/D3 receptor distribution in humans: a combined [18F]fallypride and voxel-based morphometry study.

PubMed

Woodward, Neil D; Zald, David H; Ding, Zhaohua; Riccardi, Patrizia; Ansari, M Sib; Baldwin, Ronald M; Cowan, Ronald L; Li, Rui; Kessler, Robert M

2009-05-15

The relationship between cerebral morphology and the expression of dopamine receptors has not been extensively studied in humans. Elucidation of such relationships may have important methodological implications for clinical studies of dopamine receptor ligand binding differences between control and patient groups. The association between cerebral morphology and dopamine receptor distribution was examined in 45 healthy subjects who completed T1-weighted structural MRI and PET scanning with the D(2)/D(3) ligand [(18)F]fallypride. Optimized voxel-based morphometry was used to create grey matter volume and density images. Grey matter volume and density images were correlated with binding potential (BP(ND)) images on a voxel-by-voxel basis using the Biological Parametric Mapping toolbox. Associations between cerebral morphology and BP(ND) were also examined for selected regions-of-interest (ROIs) after spatial normalization. Voxel-wise analyses indicated that grey matter volume and density positively correlated with BP(ND) throughout the midbrain, including the substantia nigra. Positive correlations were observed in medial cortical areas, including anterior cingulate and medial prefrontal cortex, and circumscribed regions of the temporal, frontal, and parietal lobes. ROI analyses revealed significant positive correlations between BP(ND) and cerebral morphology in the caudate, thalamus, and amygdala. Few negative correlations between morphology and BP(ND) were observed. Overall, grey matter density appeared more strongly correlated with BP(ND) than grey matter volume. Cerebral morphology, particularly grey matter density, correlates with [(18)F]fallypride BP(ND) in a regionally specific manner. Clinical studies comparing dopamine receptor availability between clinical and control groups may benefit by accounting for potential differences in cerebral morphology that exist even after spatial normalization.

Assessing the Regional Frequency, Intensity, and Spatial Extent of Tropical Cyclone Rainfall

NASA Astrophysics Data System (ADS)

Bosma, C.; Wright, D.; Nguyen, P.

2017-12-01

While the strength of a hurricane is generally classified based on its wind speed, the unprecedented rainfall-driven flooding experienced in southeastern Texas during Hurricane Harvey clearly highlights the need for better understanding of the hazards associated with extreme rainfall from hurricanes and other tropical systems. In this study, we seek to develop a framework for describing the joint probabilistic and spatio-temporal properties of extreme rainfall from hurricanes and other tropical systems. Furthermore, we argue that commonly-used terminology - such as the "500-year storm" - fail to convey the true properties of tropical cyclone rainfall occurrences in the United States. To quantify the magnitude and spatial extent of these storms, a database consisting of hundreds of unique rainfall volumetric shapes (or "voxels") was created. Each voxel is a four-dimensional object, created by connecting, in both space and time, gridded rainfall observations from the daily, gauge-based NOAA CPC-Unified precipitation dataset. Individual voxels were then associated with concurrent tropical cyclone tracks from NOAA's HURDAT-2 archive, to create distinct representations of the rainfall associated with every Atlantic tropical system making landfall over (or passing near) the United States since 1948. Using these voxels, a series of threshold-excess extreme value models were created to estimate the recurrence intervals of extreme tropical cyclone rainfall, both nationally and locally, for single and multi-day timescales. This voxel database also allows for the "indexing" of past events, placing recent extremes - such as the 50+ inches of rain observed during Hurricane Harvey - into a national context and emphasizing how rainfall totals that are rare at the point scale may be more frequent from a regional perspective.

SU-F-T-118: Characterization of Change in Fractional Anisotropy After Radiation Therapy: Does Nearby Disruption Predict for White Matter Damage?

DOE Office of Scientific and Technical Information (OSTI.GOV)

Pettersson, N; Karunamuni, R; Connor, M

Purpose: We investigated predictors of fractional anisotropy (FA) change in the corticospinal white matter tract (CST) following radiation therapy (RT). Methods: Diffusion tensor imaging (DTI) is a non-invasive modality which models water diffusion properties. FA quantifies the extent of directional bias—a decrease indicates disrupted white matter integrity. Fifteen patients with high-grade glioma underwent DTI scans before, and ten months after RT to 59.4–60 Gy. The CST was segmented using an automated atlas-based algorithm on all DTI images. Treatment planning CT and DTI images were aligned using non-linear registration allowing for baseline FA, follow-up FA, and absorbed dose to be determinedmore » in each voxel. Relative FA change was dichotomized into a binary outcome using 25% decrease as cutoff. Three metrics were assessed as predictors: voxel dose, distance from the voxel to the center of the CST (Rc), and the number of neighboring voxels (Nadj from 0 to 26) with ≥25% decrease in FA. Logistic regression and the area under the receiver-operating characteristics curve (AUC) analysis were performed for each patient. Results: Median age of the cohort was 59 years (range: 40–85). The average number of voxels in the CST amongst all patients was 1181 (±172, SD). In logistic regression, the probability of FA change was highly associated with Nadj in all 15 patients with corresponding AUCs between 0.81 and 0.97. With all three metrics included in the logistic regression models, Nadj was highly significant (p<0.001) in all patients, voxel dose significant (p<0.05) in 3/15 patients, and Rc significant in 12/15 patients (p<0.05). Conclusion: The number of neighboring voxels with change in FA was the dominant predictor of FA change at any given voxel. This suggests that the microenvironment of surrounding white matter disruption after radiation therapy may drive local effects along a white matter tract. Pettersson and Cervino are funded by a Varian Medical Systems grant.« less

Calculation of Dose Deposition in 3D Voxels by Heavy Ions and Simulation of gamma-H2AX Experiments

NASA Technical Reports Server (NTRS)

Plante, I.; Ponomarev, A. L.; Wang, M.; Cucinotta, F. A.

2011-01-01

The biological response to high-LET radiation is different from low-LET radiation due to several factors, notably difference in energy deposition and formation of radiolytic species. Of particular importance in radiobiology is the formation of double-strand breaks (DSB), which can be detected by -H2AX foci experiments. These experiments has revealed important differences in the spatial distribution of DSB induced by low- and high-LET radiations [1,2]. To simulate -H2AX experiments, models based on amorphous track with radial dose are often combined with random walk chromosome models [3,4]. In this work, a new approach using the Monte-Carlo track structure code RITRACKS [5] and chromosome models have been used to simulate DSB formation. At first, RITRACKS have been used to simulate the irradiation of a cubic volume of 5 m by 1) 450 1H+ ions of 300 MeV (LET 0.3 keV/ m) and 2) by 1 56Fe26+ ion of 1 GeV/amu (LET 150 keV/ m). All energy deposition events are recorded to calculate dose in voxels of 20 m. The dose voxels are distributed randomly and scattered uniformly within the volume irradiated by low-LET radiation. Many differences are found in the spatial distribution of dose voxels for the 56Fe26+ ion. The track structure can be distinguished, and voxels with very high dose are found in the region corresponding to the track "core". These high-dose voxels are not found in the low-LET irradiation simulation and indicate clustered energy deposition, which may be responsible for complex DSB. In the second step, assuming that DSB will be found only in voxels where energy is deposited by the radiation, the intersection points between voxels with dose > 0 and simulated chromosomes were obtained. The spatial distribution of the intersection points is similar to -H2AX foci experiments. These preliminary results suggest that combining stochastic track structure and chromosome models could be a good approach to understand radiation-induced DSB and chromosome aberrations.

Distributed abnormalities of brain white matter architecture in patients with dominant optic atrophy and OPA1 mutations.

PubMed

Rocca, Maria A; Bianchi-Marzoli, Stefania; Messina, Roberta; Cascavilla, Maria Lucia; Zeviani, Massimo; Lamperti, Costanza; Milesi, Jacopo; Carta, Arturo; Cammarata, Gabriella; Leocani, Letizia; Lamantea, Eleonora; Bandello, Francesco; Comi, Giancarlo; Falini, Andrea; Filippi, Massimo

2015-05-01

Using advanced MRI techniques, we investigated the presence and topographical distribution of brain grey matter (GM) and white matter (WM) alterations in dominant optic atrophy (DOA) patients with genetically proven OPA1 mutation as well as their correlation with clinical and neuro-ophthalmologic findings. Nineteen DOA patients underwent neurological, neuro-ophthalmologic and brainstem auditory evoked potentials (BAEP) evaluations. Voxel-wise methods were applied to assess regional GM and WM abnormalities in patients compared to 20 healthy controls. Visual acuity was reduced in 16 patients. Six DOA patients (4 with missense mutations) had an abnormal I peripheral component (auditory nerve) at BAEP. Compared to controls, DOA patients had significant atrophy of the optic nerves (p < 0.0001). Voxel-based morphometry (VBM) analysis showed that, compared to controls, DOA patients had significant WM atrophy of the chiasm and optic tracts; whereas no areas of GM atrophy were found. Tract-based spatial statistics (TBSS) analysis showed that compared to controls, DOA patients had significantly lower mean diffusivity, axial and radial diffusivity in the WM of the cerebellum, brainstem, thalamus, fronto-occipital-temporal lobes, including the cingulum, corpus callosum, corticospinal tract and optic radiation bilaterally. No abnormalities of fractional anisotropy were detected. No correlations were found between volumetric and diffusivity abnormalities quantified with MRI and clinical and neuro-ophthalmologic measures of disease severity. Consistently with pathological studies, tissue loss in DOA patients is limited to anterior optic pathways reflecting retinal ganglion cell degeneration. Distributed abnormalities of diffusivity indexes might reflect abnormal intracellular mitochondrial morphology as well as alteration of protein levels due to OPA1 mutations.

An Multivariate Distance-Based Analytic Framework for Connectome-Wide Association Studies

PubMed Central

Shehzad, Zarrar; Kelly, Clare; Reiss, Philip T.; Craddock, R. Cameron; Emerson, John W.; McMahon, Katie; Copland, David A.; Castellanos, F. Xavier; Milham, Michael P.

2014-01-01

The identification of phenotypic associations in high-dimensional brain connectivity data represents the next frontier in the neuroimaging connectomics era. Exploration of brain-phenotype relationships remains limited by statistical approaches that are computationally intensive, depend on a priori hypotheses, or require stringent correction for multiple comparisons. Here, we propose a computationally efficient, data-driven technique for connectome-wide association studies (CWAS) that provides a comprehensive voxel-wise survey of brain-behavior relationships across the connectome; the approach identifies voxels whose whole-brain connectivity patterns vary significantly with a phenotypic variable. Using resting state fMRI data, we demonstrate the utility of our analytic framework by identifying significant connectivity-phenotype relationships for full-scale IQ and assessing their overlap with existent neuroimaging findings, as synthesized by openly available automated meta-analysis (www.neurosynth.org). The results appeared to be robust to the removal of nuisance covariates (i.e., mean connectivity, global signal, and motion) and varying brain resolution (i.e., voxelwise results are highly similar to results using 800 parcellations). We show that CWAS findings can be used to guide subsequent seed-based correlation analyses. Finally, we demonstrate the applicability of the approach by examining CWAS for three additional datasets, each encompassing a distinct phenotypic variable: neurotypical development, Attention-Deficit/Hyperactivity Disorder diagnostic status, and L-dopa pharmacological manipulation. For each phenotype, our approach to CWAS identified distinct connectome-wide association profiles, not previously attainable in a single study utilizing traditional univariate approaches. As a computationally efficient, extensible, and scalable method, our CWAS framework can accelerate the discovery of brain-behavior relationships in the connectome. PMID:24583255

Relationship between grey matter integrity and executive abilities in aging.

PubMed

Manard, Marine; Bahri, Mohamed Ali; Salmon, Eric; Collette, Fabienne

2016-07-01

This cross-sectional study was designed to investigate grey matter changes that occur in healthy aging and the relationship between grey matter characteristics and executive functioning. Thirty-six young adults (18-30 years old) and 43 seniors (60-75 years old) were included. A general executive score was derived from a large battery of neuropsychological tests assessing three major aspects of executive functioning (inhibition, updating and shifting). Age-related grey matter changes were investigated by comparing young and older adults using voxel-based morphometry and voxel-based cortical thickness methods. A widespread difference in grey matter volume was found across many brain regions, whereas cortical thinning was mainly restricted to central areas. Multivariate analyses showed age-related changes in relatively similar brain regions to the respective univariate analyses but appeared more limited. Finally, in the older adult sample, a significant relationship between global executive performance and decreased grey matter volume in anterior (i.e. frontal, insular and cingulate cortex) but also some posterior brain areas (i.e. temporal and parietal cortices) as well as subcortical structures was observed. Results of this study highlight the distribution of age-related effects on grey matter volume and show that cortical atrophy does not appear primarily in "frontal" brain regions. From a cognitive viewpoint, age-related executive functioning seems to be related to grey matter volume but not to cortical thickness. Therefore, our results also highlight the influence of methodological aspects (from preprocessing to statistical analysis) on the pattern of results, which could explain the lack of consensus in literature. Copyright © 2016 Elsevier B.V. All rights reserved.

Neural substrates of lower extremity motor, balance, and gait function after supratentorial stroke using voxel-based lesion symptom mapping.

PubMed

Moon, Hyun Im; Pyun, Sung-Bom; Tae, Woo-Suk; Kwon, Hee Kyu

2016-07-01

Stroke impairs motor, balance, and gait function and influences activities of daily living. Understanding the relationship between brain lesions and deficits can help clinicians set goals during rehabilitation. We sought to elucidate the neural substrates of lower extremity motor, balance, and ambulation function using voxel-based lesion symptom mapping (VLSM) in supratentorial stroke patients. We retrospectively screened patients who met the following criteria: first-ever stroke, supratentorial lesion, and available brain magnetic resonance imaging (MRI) data. MRIs of 133 stroke patients were selected for VLSM analysis. We generated statistical maps of lesions related to lower extremity motor (lower extremity Fugl-Meyer assessment, LEFM), balance (Berg Balance Scale, BBS), and gait (Functional Ambulation Category, FAC) using VLSM. VLSM revealed that lower LEFM scores were associated with damage to the bilateral basal ganglia, insula, internal capsule, and subgyral white matter adjacent to the corona radiata. The lesions were more widely distributed in the left than in the right hemisphere, representing motor and praxis function necessary for performing tasks. However, no associations between lesion maps and balance and gait function were established. Motor impairment of the lower extremities was associated with lesions in the basal ganglia, insula, internal capsule, and white matter adjacent to the corona radiata. However, VLSM revealed no specific lesion locations with regard to balance and gait function. This might be because balance and gait are complex skills that require spatial and temporal integration of sensory input and execution of movement patterns. For more accurate prediction, factors other than lesion location need to be investigated.

Common and distinct patterns of grey-matter volume alteration in major depression and bipolar disorder: evidence from voxel-based meta-analysis.

PubMed

Wise, T; Radua, J; Via, E; Cardoner, N; Abe, O; Adams, T M; Amico, F; Cheng, Y; Cole, J H; de Azevedo Marques Périco, C; Dickstein, D P; Farrow, T F D; Frodl, T; Wagner, G; Gotlib, I H; Gruber, O; Ham, B J; Job, D E; Kempton, M J; Kim, M J; Koolschijn, P C M P; Malhi, G S; Mataix-Cols, D; McIntosh, A M; Nugent, A C; O'Brien, J T; Pezzoli, S; Phillips, M L; Sachdev, P S; Salvadore, G; Selvaraj, S; Stanfield, A C; Thomas, A J; van Tol, M J; van der Wee, N J A; Veltman, D J; Young, A H; Fu, C H; Cleare, A J; Arnone, D

2017-10-01

Finding robust brain substrates of mood disorders is an important target for research. The degree to which major depression (MDD) and bipolar disorder (BD) are associated with common and/or distinct patterns of volumetric changes is nevertheless unclear. Furthermore, the extant literature is heterogeneous with respect to the nature of these changes. We report a meta-analysis of voxel-based morphometry (VBM) studies in MDD and BD. We identified studies published up to January 2015 that compared grey matter in MDD (50 data sets including 4101 individuals) and BD (36 data sets including 2407 individuals) using whole-brain VBM. We used statistical maps from the studies included where available and reported peak coordinates otherwise. Group comparisons and conjunction analyses identified regions in which the disorders showed common and distinct patterns of volumetric alteration. Both disorders were associated with lower grey-matter volume relative to healthy individuals in a number of areas. Conjunction analysis showed smaller volumes in both disorders in clusters in the dorsomedial and ventromedial prefrontal cortex, including the anterior cingulate cortex and bilateral insula. Group comparisons indicated that findings of smaller grey-matter volumes relative to controls in the right dorsolateral prefrontal cortex and left hippocampus, along with cerebellar, temporal and parietal regions were more substantial in major depression. These results suggest that MDD and BD are characterised by both common and distinct patterns of grey-matter volume changes. This combination of differences and similarities has the potential to inform the development of diagnostic biomarkers for these conditions.

Quantification of the impact of a confounding variable on functional connectivity confirms anti-correlated networks in the resting-state.

PubMed

Carbonell, F; Bellec, P; Shmuel, A

2014-02-01

The effect of regressing out the global average signal (GAS) in resting state fMRI data has become a concern for interpreting functional connectivity analyses. It is not clear whether the reported anti-correlations between the Default Mode and the Dorsal Attention Networks are intrinsic to the brain, or are artificially created by regressing out the GAS. Here we introduce a concept, Impact of the Global Average on Functional Connectivity (IGAFC), for quantifying the sensitivity of seed-based correlation analyses to the regression of the GAS. This voxel-wise IGAFC index is defined as the product of two correlation coefficients: the correlation between the GAS and the fMRI time course of a voxel, times the correlation between the GAS and the seed time course. This definition enables the calculation of a threshold at which the impact of regressing-out the GAS would be large enough to introduce spurious negative correlations. It also yields a post-hoc impact correction procedure via thresholding, which eliminates spurious correlations introduced by regressing out the GAS. In addition, we introduce an Artificial Negative Correlation Index (ANCI), defined as the absolute difference between the IGAFC index and the impact threshold. The ANCI allows a graded confidence scale for ranking voxels according to their likelihood of showing artificial correlations. By applying this method, we observed regions in the Default Mode and Dorsal Attention Networks that were anti-correlated. These findings confirm that the previously reported negative correlations between the Dorsal Attention and Default Mode Networks are intrinsic to the brain and not the result of statistical manipulations. Our proposed quantification of the impact that a confound may have on functional connectivity can be generalized to global effect estimators other than the GAS. It can be readily applied to other confounds, such as systemic physiological or head movement interferences, in order to quantify their impact on functional connectivity in the resting state. © 2013.

Evaluation of brain perfusion in specific Brodmann areas in Frontotemporal dementia and Alzheimer disease using automated 3-D voxel based analysis

NASA Astrophysics Data System (ADS)

Valotassiou, V.; Papatriantafyllou, J.; Sifakis, N.; Karageorgiou, C.; Tsougos, I.; Tzavara, C.; Zerva, C.; Georgoulias, P.

2009-05-01

Introduction. Brain perfusion studies with single-photon emission computed tomography (SPECT) have been applied in demented patients to provide better discrimination between frontotemporal dementia (FTD) and Alzheimer's disease (AD). Aim. To assess the perfusion of specific Brodmann (Br) areas of the brain cortex in FTD and AD patients, using NeuroGam processing program to provide 3D voxel-by-voxel cerebral SPECT analysis. Material and methods. We studied 34 consecutive patients. We used the established criteria for the diagnosis of dementia and the specific established criteria for the diagnosis of FTD and AD. All the patients had a neuropsychological evaluation with a battery of tests including the mini-mental state examination (MMSE).Twenty-six patients (16 males, 10 females, mean age 68.76±6.51 years, education 11.81±4.25 years, MMSE 16.69±9.89) received the diagnosis of FTD and 8 patients (all females, mean age 71.25±10.48 years, education 10±4.6 years, MMSE 12.5±3.89) the diagnosis of AD. All the patients underwent a brain SPECT. We applied the NeuroGam Software for the evaluation of brain perfusion in specific Br areas in the left (L) and right (R) hemispheres. Results. Statistically significant hypoperfusion in FTD compared to AD patients, was found in the following Br areas: 11L (p<0.0001), 11R, 20L, 20R, 32L, 38L, 38R, 44L (p<0.001), 32R, 36L, 36R, 45L, 45R, 47R (p<0.01), 9L, 21L, 39R, 44R, 46R, 47L (p<0.05). On the contrary, AD patients presented significant (p<0.05) hypoperfusion in 7R and 39R Br areas. Conclusion. NeuroGam processing program of brain perfusion SPECT could result in enhanced accuracy for the differential diagnosis between AD and FTD patients.

Rhinal hypometabolism on FDG PET in healthy APO-E4 carriers: impact on memory function and metabolic networks.

PubMed

Didic, Mira; Felician, Olivier; Gour, Natalina; Bernard, Rafaelle; Pécheux, Christophe; Mundler, Olivier; Ceccaldi, Mathieu; Guedj, Eric

2015-09-01

The ε4 allele of the apolipoprotein E (APO-E4) gene, a genetic risk factor for Alzheimer's disease (AD), also modulates brain metabolism and function in healthy subjects. The aim of the present study was to explore cerebral metabolism using FDG PET in healthy APO-E4 carriers by comparing cognitively normal APO-E4 carriers to noncarriers and to assess if patterns of metabolism are correlated with performance on cognitive tasks. Moreover, metabolic connectivity patterns were established in order to assess if the organization of neural networks is influenced by genetic factors. Whole-brain PET statistical analysis was performed at voxel-level using SPM8 with a threshold of p < 0.005, corrected for volume, with age, gender and level of education as nuisance variables. Significant hypometabolism between APO-E4 carriers (n = 11) and noncarriers (n = 30) was first determined. Mean metabolic values with clinical/neuropsychological data were extracted at the individual level, and correlations were searched using Spearman's rank test in the whole group. To evaluate metabolic connectivity from metabolic cluster(s) previously identified in the intergroup comparison, voxel-wise interregional correlation analysis (IRCA) was performed between groups of subjects. APO-E4 carriers had reduced metabolism within the left anterior medial temporal lobe (MTL), where neuropathological changes first appear in AD, including the entorhinal and perirhinal cortices. A correlation between metabolism in this area and performance on the DMS48 (delayed matching to sample-48 items) was found, in line with converging evidence involving the perirhinal cortex in object-based memory. Finally, a voxel-wise IRCA revealed stronger metabolic connectivity of the MTL cluster with neocortical frontoparietal regions in carriers than in noncarriers, suggesting compensatory metabolic networks. Exploring cerebral metabolism using FDG PET can contribute to a better understanding of the influence of genetic factors on cerebral metabolism at both the local and network levels leading to phenotypical variations of the healthy brain and selective vulnerability.

White matter pathology in ALS and lower motor neuron ALS variants: a diffusion tensor imaging study using tract-based spatial statistics.

PubMed

Prudlo, Johannes; Bißbort, Charlotte; Glass, Aenne; Grossmann, Annette; Hauenstein, Karlheinz; Benecke, Reiner; Teipel, Stefan J

2012-09-01

The aim of this work was to investigate white-matter microstructural changes within and outside the corticospinal tract in classical amyotrophic lateral sclerosis (ALS) and in lower motor neuron (LMN) ALS variants by means of diffusion tensor imaging (DTI). We investigated 22 ALS patients and 21 age-matched controls utilizing a whole-brain approach with a 1.5-T scanner for DTI. The patient group was comprised of 15 classical ALS- and seven LMN ALS-variant patients (progressive muscular atrophy, flail arm and flail leg syndrome). Disease severity was measured by the revised version of the functional rating scale. White matter fractional anisotropy (FA) was assessed using tract-based spatial statistics (TBSS) and a region of interest (ROI) approach. We found significant FA reductions in motor and extra-motor cerebral fiber tracts in classical ALS and in the LMN ALS-variant patients compared to controls. The voxel-based TBSS results were confirmed by the ROI findings. The white matter damage correlated with the disease severity in the patient group and was found in a similar distribution, but to a lesser extent, among the LMN ALS-variant subgroup. ALS and LMN ALS variants are multisystem degenerations. DTI shows the potential to determine an earlier diagnosis, particularly in LMN ALS variants. The statistically identical findings of white matter lesions in classical ALS and LMN variants as ascertained by DTI further underline that these variants should be regarded as part of the ALS spectrum.

The NUKDOS software for treatment planning in molecular radiotherapy.

PubMed

Kletting, Peter; Schimmel, Sebastian; Hänscheid, Heribert; Luster, Markus; Fernández, Maria; Nosske, Dietmar; Lassmann, Michael; Glatting, Gerhard

2015-09-01

The aim of this work was the development of a software tool for treatment planning prior to molecular radiotherapy, which comprises all functionality to objectively determine the activity to administer and the pertaining absorbed doses (including the corresponding error) based on a series of gamma camera images and one SPECT/CT or probe data. NUKDOS was developed in MATLAB. The workflow is based on the MIRD formalism For determination of the tissue or organ pharmacokinetics, gamma camera images as well as probe, urine, serum and blood activity data can be processed. To estimate the time-integrated activity coefficients (TIAC), sums of exponentials are fitted to the time activity data and integrated analytically. To obtain the TIAC on the voxel level, the voxel activity distribution from the quantitative 3D SPECT/CT (or PET/CT) is used for scaling and weighting the TIAC derived from the 2D organ data. The voxel S-values are automatically calculated based on the voxel-size of the image and the therapeutic nuclide ((90)Y, (131)I or (177)Lu). The absorbed dose coefficients are computed by convolution of the voxel TIAC and the voxel S-values. The activity to administer and the pertaining absorbed doses are determined by entering the absorbed dose for the organ at risk. The overall error of the calculated absorbed doses is determined by Gaussian error propagation. NUKDOS was tested for the operation systems Windows(®) 7 (64 Bit) and 8 (64 Bit). The results of each working step were compared to commercially available (SAAMII, OLINDA/EXM) and in-house (UlmDOS) software. The application of the software is demonstrated using examples form peptide receptor radionuclide therapy (PRRT) and from radioiodine therapy of benign thyroid diseases. For the example from PRRT, the calculated activity to administer differed by 4% comparing NUKDOS and the final result using UlmDos, SAAMII and OLINDA/EXM sequentially. The absorbed dose for the spleen and tumour differed by 7% and 8%, respectively. The results from the example from radioiodine therapy of benign thyroid diseases and the example given in the latest corresponding SOP were identical. The implemented, objective methods facilitate accurate and reproducible results. The software is freely available. Copyright © 2015. Published by Elsevier GmbH.

WE-G-18A-04: 3D Dictionary Learning Based Statistical Iterative Reconstruction for Low-Dose Cone Beam CT Imaging

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bai, T; UT Southwestern Medical Center, Dallas, TX; Yan, H

2014-06-15

Purpose: To develop a 3D dictionary learning based statistical reconstruction algorithm on graphic processing units (GPU), to improve the quality of low-dose cone beam CT (CBCT) imaging with high efficiency. Methods: A 3D dictionary containing 256 small volumes (atoms) of 3x3x3 voxels was trained from a high quality volume image. During reconstruction, we utilized a Cholesky decomposition based orthogonal matching pursuit algorithm to find a sparse representation on this dictionary basis of each patch in the reconstructed image, in order to regularize the image quality. To accelerate the time-consuming sparse coding in the 3D case, we implemented our algorithm inmore » a parallel fashion by taking advantage of the tremendous computational power of GPU. Evaluations are performed based on a head-neck patient case. FDK reconstruction with full dataset of 364 projections is used as the reference. We compared the proposed 3D dictionary learning based method with a tight frame (TF) based one using a subset data of 121 projections. The image qualities under different resolutions in z-direction, with or without statistical weighting are also studied. Results: Compared to the TF-based CBCT reconstruction, our experiments indicated that 3D dictionary learning based CBCT reconstruction is able to recover finer structures, to remove more streaking artifacts, and is less susceptible to blocky artifacts. It is also observed that statistical reconstruction approach is sensitive to inconsistency between the forward and backward projection operations in parallel computing. Using high a spatial resolution along z direction helps improving the algorithm robustness. Conclusion: 3D dictionary learning based CBCT reconstruction algorithm is able to sense the structural information while suppressing noise, and hence to achieve high quality reconstruction. The GPU realization of the whole algorithm offers a significant efficiency enhancement, making this algorithm more feasible for potential clinical application. A high zresolution is preferred to stabilize statistical iterative reconstruction. This work was supported in part by NIH(1R01CA154747-01), NSFC((No. 61172163), Research Fund for the Doctoral Program of Higher Education of China (No. 20110201110011), China Scholarship Council.« less

WE-AB-202-09: Feasibility and Quantitative Analysis of 4DCT-Based High Precision Lung Elastography

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hasse, K; Neylon, J; Low, D

2016-06-15

Purpose: The purpose of this project is to derive high precision elastography measurements from 4DCT lung scans to facilitate the implementation of elastography in a radiotherapy context. Methods: 4DCT scans of the lungs were acquired, and breathing stages were subsequently registered to each other using an optical flow DIR algorithm. The displacement of each voxel gleaned from the registration was taken to be the ground-truth deformation. These vectors, along with the 4DCT source datasets, were used to generate a GPU-based biomechanical simulation that acted as a forward model to solve the inverse elasticity problem. The lung surface displacements were appliedmore » as boundary constraints for the model-guided lung tissue elastography, while the inner voxels were allowed to deform according to the linear elastic forces within the model. A biomechanically-based anisotropic convergence magnification technique was applied to the inner voxels in order to amplify the subtleties of the interior deformation. Solving the inverse elasticity problem was accomplished by modifying the tissue elasticity and iteratively deforming the biomechanical model. Convergence occurred when each voxel was within 0.5 mm of the ground-truth deformation and 1 kPa of the ground-truth elasticity distribution. To analyze the feasibility of the model-guided approach, we present the results for regions of low ventilation, specifically, the apex. Results: The maximum apical boundary expansion was observed to be between 2 and 6 mm. Simulating this expansion within an apical lung model, it was observed that 100% of voxels converged within 0.5 mm of ground-truth deformation, while 91.8% converged within 1 kPa of the ground-truth elasticity distribution. A mean elasticity error of 0.6 kPa illustrates the high precision of our technique. Conclusion: By utilizing 4DCT lung data coupled with a biomechanical model, high precision lung elastography can be accurately performed, even in low ventilation regions of the lungs. This material is based upon work supported by the National Science Foundation Graduate Research Fellowship under Grant No. DGE-1144087.« less

A new Hessian - based approach for segmentation of CT porous media images

NASA Astrophysics Data System (ADS)

Timofey, Sizonenko; Marina, Karsanina; Dina, Gilyazetdinova; Kirill, Gerke

2017-04-01

Hessian matrix based methods are widely used in image analysis for features detection, e.g., detection of blobs, corners and edges. Hessian matrix of the imageis the matrix of 2nd order derivate around selected voxel. Most significant features give highest values of Hessian transform and lowest values are located at smoother parts of the image. Majority of conventional segmentation techniques can segment out cracks, fractures and other inhomogeneities in soils and rocks only if the rest of the image is significantly "oversigmented". To avoid this disadvantage, we propose to enhance greyscale values of voxels belonging to such specific inhomogeneities on X-ray microtomography scans. We have developed and implemented in code a two-step approach to attack the aforementioned problem. During the first step we apply a filter that enhances the image and makes outstanding features more sharply defined. During the second step we apply Hessian filter based segmentation. The values of voxels on the image to be segmented are calculated in conjunction with the values of other voxels within prescribed region. Contribution from each voxel within such region is computed by weighting according to the local Hessian matrix value. We call this approach as Hessian windowed segmentation. Hessian windowed segmentation has been tested on different porous media X-ray microtomography images, including soil, sandstones, carbonates and shales. We also compared this new method against others widely used methods such as kriging, Markov random field, converging active contours and region grow. We show that our approach is more accurate in regions containing special features such as small cracks, fractures, elongated inhomogeneities and other features with low contrast related to the background solid phase. Moreover, Hessian windowed segmentation outperforms some of these methods in computational efficiency. We further test our segmentation technique by computing permeability of segmented images and comparing them against laboratory based measurements. This work was partially supported by RFBR grant 15-34-20989 (X-ray tomography and image fusion) and RSF grant 14-17-00658 (image segmentation and pore-scale modelling).

An investigation of voxel geometries for MCNP-based radiation dose calculations.

PubMed

Zhang, Juying; Bednarz, Bryan; Xu, X George

2006-11-01

Voxelized geometry such as those obtained from medical images is increasingly used in Monte Carlo calculations of absorbed doses. One useful application of calculated absorbed dose is the determination of fluence-to-dose conversion factors for different organs. However, confusion still exists about how such a geometry is defined and how the energy deposition is best computed, especially involving a popular code, MCNP5. This study investigated two different types of geometries in the MCNP5 code, cell and lattice definitions. A 10 cm x 10 cm x 10 cm test phantom, which contained an embedded 2 cm x 2 cm x 2 cm target at its center, was considered. A planar source emitting parallel photons was also considered in the study. The results revealed that MCNP5 does not calculate total target volume for multi-voxel geometries. Therefore, tallies which involve total target volume must be divided by the user by the total number of voxels to obtain a correct dose result. Also, using planar source areas greater than the phantom size results in the same fluence-to-dose conversion factor.

Dosimetry for nonuniform activity distributions: a method for the calculation of 3D absorbed-dose distribution without the use of voxel S-values, point kernels, or Monte Carlo simulations.

PubMed

Traino, A C; Marcatili, S; Avigo, C; Sollini, M; Erba, P A; Mariani, G

2013-04-01

Nonuniform activity within the target lesions and the critical organs constitutes an important limitation for dosimetric estimates in patients treated with tumor-seeking radiopharmaceuticals. The tumor control probability and the normal tissue complication probability are affected by the distribution of the radionuclide in the treated organ/tissue. In this paper, a straightforward method for calculating the absorbed dose at the voxel level is described. This new method takes into account a nonuniform activity distribution in the target/organ. The new method is based on the macroscopic S-values (i.e., the S-values calculated for the various organs, as defined in the MIRD approach), on the definition of the number of voxels, and on the raw-count 3D array, corrected for attenuation, scatter, and collimator resolution, in the lesion/organ considered. Starting from these parameters, the only mathematical operation required is to multiply the 3D array by a scalar value, thus avoiding all the complex operations involving the 3D arrays. A comparison with the MIRD approach, fully described in the MIRD Pamphlet No. 17, using S-values at the voxel level, showed a good agreement between the two methods for (131)I and for (90)Y. Voxel dosimetry is becoming more and more important when performing therapy with tumor-seeking radiopharmaceuticals. The method presented here does not require calculating the S-values at the voxel level, and thus bypasses the mathematical problems linked to the convolution of 3D arrays and to the voxel size. In the paper, the results obtained with this new simplified method as well as the possibility of using it for other radionuclides commonly employed in therapy are discussed. The possibility of using the correct density value of the tissue/organs involved is also discussed.

SU-F-J-59: Assessment of Dose Response Distribution in Individual Human Tumor

DOE Office of Scientific and Technical Information (OSTI.GOV)

Yan, D; Chen, S; Krauss, D

Purpose: To fulfill precision radiotherapy via adaptive dose painting by number, voxel-by-voxel dose response or radio-sensitivity in individual human tumor needs to be determined in early treatment to guide treatment adaptation. In this study, multiple FDG PET images obtained pre- and weekly during the treatment course were utilized to determine the distribution/spectrum of dose response parameters in individual human tumors. Methods: FDG PET/CT images of 18 HN cancer patients were used in the study. Spatial parametric image of tumor metabolic ratio (dSUV) was created following voxel by voxel deformable image registration. Each voxel value in dSUV was a function ofmore » pre-treatment baseline SUV and treatment delivered dose, and used as a surrogate of tumor survival fraction (SF). Regression fitting with break points was performed using the LQ-model with tumor proliferation for the control and failure group of tumors separately. The distribution and spectrum of radiation sensitivity and growth in individual tumors were determined and evaluated. Results: Spectrum of tumor dose-sensitivity and proliferation in the controlled group was broad with α in tumor survival LQ-model from 0.17 to 0.8. It was proportional to the baseline SUV. Tlag was about 21∼25 days, and Tpot about 0.56∼1.67 days respectively. Commonly tumor voxels with high radio-sensitivity or larger α had small Tlag and Tpot. For the failure group, the radio-sensitivity α was low within 0.05 to 0.3, but did not show clear Tlag. In addition, tumor voxel radio-sensitivity could be estimated during the early treatment weeks. Conclusion: Dose response distribution with respect to radio-sensitivity and growth in individual human tumor can be determined using FDG PET imaging based tumor metabolic ratio measured in early treatment course. The discover is critical and provides a potential quantitative objective to implement tumor specific precision radiotherapy via adaptive dose painting by number.« less

Regional patterns of grey matter atrophy and magnetisation transfer ratio abnormalities in multiple sclerosis clinical subgroups: a voxel-based analysis study.

PubMed

Mallik, Shahrukh; Muhlert, Nils; Samson, Rebecca S; Sethi, Varun; Wheeler-Kingshott, Claudia A M; Miller, David H; Chard, Declan T

2015-04-01

In multiple sclerosis (MS), demyelination and neuro-axonal loss occur in the brain grey matter (GM). We used magnetic resonance imaging (MRI) measures of GM magnetisation transfer ratio (MTR) and volume to assess the regional localisation of reduced MTR (reflecting demyelination) and atrophy (reflecting neuro-axonal loss) in relapsing-remitting MS (RRMS), secondary progressive MS (SPMS) and primary progressive MS (PPMS). A total of 98 people with MS (51 RRMS, 28 SPMS, 19 PPMS) and 29 controls had T1-weighted volumetric and magnetisation transfer scans. SPM8 was used to undertake voxel-based analysis (VBA) of GM tissue volumes and MTR. MS subgroups were compared with controls, adjusting for age and gender. A voxel-by-voxel basis correlation analysis between MTR and volume within each subject group was performed, using biological parametric mapping. MTR reduction was more extensive than atrophy. RRMS and SPMS patients showed proportionately more atrophy in the deep GM. SPMS and PPMS patients showed proportionately greater cortical MTR reduction. RRMS patients demonstrated the most correlation of MTR reduction and atrophy in deep GM. In SPMS and PPMS patients, there was less extensive correlation. These results suggest that in the deep GM of RRMS patients, demyelination and neuro-axonal loss may be linked, while in SPMS and PPMS patients, neuro-axonal loss and demyelination may occur mostly independently. © The Author(s), 2014.

Neurofunctional maps of the 'maternal brain' and the effects of oxytocin: a multimodal voxel-based meta-analysis.

PubMed

Rocchetti, Matteo; Radua, Joaquim; Paloyelis, Yannis; Xenaki, Lida-Alkisti; Frascarelli, Marianna; Caverzasi, Edgardo; Politi, Pierluigi; Fusar-Poli, Paolo

2014-10-01

Several studies have tried to understand the possible neurobiological basis of mothering. The putative involvement of oxytocin, in this regard, has been deeply investigated. Performing a voxel-based meta-analysis, we aimed at testing the hypothesis of overlapping brain activation in functional magnetic resonance imaging (fMRI) studies investigating the mother-infant interaction and the oxytocin modulation of emotional stimuli in humans. We performed two systematic literature searches: fMRI studies investigating the neurofunctional correlates of the 'maternal brain' by employing mother-infant paradigms; and fMRI studies employing oxytocin during emotional tasks. A unimodal voxel-based meta-analysis was performed on each database, whereas a multimodal voxel-based meta-analytical tool was adopted to assess the hypothesis that the neurofunctional effects of oxytocin are detected in brain areas implicated in the 'maternal brain.' We found greater activation in the bilateral insula extending to the inferior frontal gyrus, basal ganglia and thalamus during mother-infant interaction and greater left insular activation associated with oxytocin administration versus placebo. Left insula extending to basal ganglia and frontotemporal gyri as well as bilateral thalamus and amygdala showed consistent activation across the two paradigms. Right insula also showed activation across the two paradigms, and dorsomedial frontal cortex activation in mothers but deactivation with oxytocin. Significant activation in areas involved in empathy, emotion regulation, motivation, social cognition and theory of mind emerged from our multimodal meta-analysis, supporting the need for further studies directly investigating the neurobiology of oxytocin in the mother-infant relationship. © 2014 The Authors. Psychiatry and Clinical Neurosciences © 2014 Japanese Society of Psychiatry and Neurology.

A voxel based comparative analysis using magnetization transfer imaging and T1-weighted magnetic resonance imaging in progressive supranuclear palsy

PubMed Central

Sandhya, Mangalore; Saini, Jitender; Pasha, Shaik Afsar; Yadav, Ravi; Pal, Pramod Kumar

2014-01-01

Aims: In progressive supranuclear palsy (PSP) tissue damage occurs in specific cortical and subcortical regions. Voxel based analysis using T1-weighted images depict quantitative gray matter (GM) atrophy changes. Magnetization transfer (MT) imaging depicts qualitative changes in the brain parenchyma. The purpose of our study was to investigate whether MT imaging could indicate abnormalities in PSP. Settings and Design: A total of 10 patients with PSP (9 men and 1 woman) and 8 controls (5 men and 3 women) were studied with T1-weighted magnetic resonance imaging (MRI) and 3DMT imaging. Voxel based analysis of T1-weighted MRI was performed to investigate brain atrophy while MT was used to study qualitative abnormalities in the brain tissue. We used SPM8 to investigate group differences (with two sample t-test) using the GM and white matter (WM) segmented data. Results: T1-weighted imaging and MT are equally sensitive to detect changes in GM and WM in PSP. Magnetization transfer ratio images and magnetization-prepared rapid acquisition of gradient echo revealed extensive bilateral volume and qualitative changes in the orbitofrontal, prefrontal cortex and limbic lobe and sub cortical GM. The prefrontal structures involved were the rectal gyrus, medial, inferior frontal gyrus (IFG) and middle frontal gyrus (MFG). The anterior cingulate, cingulate gyrus and lingual gyrus of limbic lobe and subcortical structures such as caudate, thalamus, insula and claustrum were also involved. Cerebellar involvement mainly of anterior lobe was also noted. Conclusions: The findings suggest that voxel based MT imaging permits a whole brain unbiased investigation of central nervous system structural integrity in PSP. PMID:25024571

Reconstruction of 3d Models from Point Clouds with Hybrid Representation

NASA Astrophysics Data System (ADS)

Hu, P.; Dong, Z.; Yuan, P.; Liang, F.; Yang, B.

2018-05-01

The three-dimensional (3D) reconstruction of urban buildings from point clouds has long been an active topic in applications related to human activities. However, due to the structures significantly differ in terms of complexity, the task of 3D reconstruction remains a challenging issue especially for the freeform surfaces. In this paper, we present a new reconstruction algorithm which allows the 3D-models of building as a combination of regular structures and irregular surfaces, where the regular structures are parameterized plane primitives and the irregular surfaces are expressed as meshes. The extraction of irregular surfaces starts with an over-segmented method for the unstructured point data, a region growing approach based the adjacent graph of super-voxels is then applied to collapse these super-voxels, and the freeform surfaces can be clustered from the voxels filtered by a thickness threshold. To achieve these regular planar primitives, the remaining voxels with a larger flatness will be further divided into multiscale super-voxels as basic units, and the final segmented planes are enriched and refined in a mutually reinforcing manner under the framework of a global energy optimization. We have implemented the proposed algorithms and mainly tested on two point clouds that differ in point density and urban characteristic, and experimental results on complex building structures illustrated the efficacy of the proposed framework.

The Attentional Field Revealed by Single-Voxel Modeling of fMRI Time Courses

PubMed Central

DeYoe, Edgar A.

2015-01-01

The spatial topography of visual attention is a distinguishing and critical feature of many theoretical models of visuospatial attention. Previous fMRI-based measurements of the topography of attention have typically been too crude to adequately test the predictions of different competing models. This study demonstrates a new technique to make detailed measurements of the topography of visuospatial attention from single-voxel, fMRI time courses. Briefly, this technique involves first estimating a voxel's population receptive field (pRF) and then “drifting” attention through the pRF such that the modulation of the voxel's fMRI time course reflects the spatial topography of attention. The topography of the attentional field (AF) is then estimated using a time-course modeling procedure. Notably, we are able to make these measurements in many visual areas including smaller, higher order areas, thus enabling a more comprehensive comparison of attentional mechanisms throughout the full hierarchy of human visual cortex. Using this technique, we show that the AF scales with eccentricity and varies across visual areas. We also show that voxels in multiple visual areas exhibit suppressive attentional effects that are well modeled by an AF having an enhancing Gaussian center with a suppressive surround. These findings provide extensive, quantitative neurophysiological data for use in modeling the psychological effects of visuospatial attention. PMID:25810532

Tree species classification in subtropical forests using small-footprint full-waveform LiDAR data

NASA Astrophysics Data System (ADS)

Cao, Lin; Coops, Nicholas C.; Innes, John L.; Dai, Jinsong; Ruan, Honghua; She, Guanghui

2016-07-01

The accurate classification of tree species is critical for the management of forest ecosystems, particularly subtropical forests, which are highly diverse and complex ecosystems. While airborne Light Detection and Ranging (LiDAR) technology offers significant potential to estimate forest structural attributes, the capacity of this new tool to classify species is less well known. In this research, full-waveform metrics were extracted by a voxel-based composite waveform approach and examined with a Random Forests classifier to discriminate six subtropical tree species (i.e., Masson pine (Pinus massoniana Lamb.)), Chinese fir (Cunninghamia lanceolata (Lamb.) Hook.), Slash pines (Pinus elliottii Engelm.), Sawtooth oak (Quercus acutissima Carruth.) and Chinese holly (Ilex chinensis Sims.) at three levels of discrimination. As part of the analysis, the optimal voxel size for modelling the composite waveforms was investigated, the most important predictor metrics for species classification assessed and the effect of scan angle on species discrimination examined. Results demonstrate that all tree species were classified with relatively high accuracy (68.6% for six classes, 75.8% for four main species and 86.2% for conifers and broadleaved trees). Full-waveform metrics (based on height of median energy, waveform distance and number of waveform peaks) demonstrated high classification importance and were stable among various voxel sizes. The results also suggest that the voxel based approach can alleviate some of the issues associated with large scan angles. In summary, the results indicate that full-waveform LIDAR data have significant potential for tree species classification in the subtropical forests.

MO-F-CAMPUS-J-01: Effect of Iodine Contrast Agent Concentration On Cerebrovascular Dose for Synchrotron Radiation Microangiography Based On a Simple Mouse Head Model and a Voxel Mouse Head Phantom

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lin, H; Jing, J; Xie, C

Purpose: To find effective setting methods to mitigate the irradiation injure in synchrotron radiation microangiography(SRA) by Monte Carlo simulation. Methods: A mouse 1-D head model and a segmented voxel mouse head phantom were simulated by EGSnrc/Dosxyznrc code to investigate the dose enhancement effect of the iodine contrast agent irradiated by a monochromatic synchrotron radiation(SR) source. The influence of, like iodine concentration (IC), vessel width and depth, with and without skull layer protection and the various incident X ray energies, were simulated. The dose enhancement effect and the absolute dose based on the segmented voxel mouse head phantom were evaluated. Results:more » The dose enhancement ratio depends little on the irradiation depth, but strongly on the IC, which is linearly increases with IC. The skull layer protection cannot be ignored in SRA, the 700µm thick skull could decrease 10% of the dose. The incident X-ray energy can significantly affact the dose. E.g. compared to the dose of 33.2keV for 50mgI/ml, the 32.7keV dose decreases 38%, whereas the dose of 33.7 keV increases 69.2%, and the variation will strengthen more with enhanced IC. The segmented voxel mouse head phantom also showed that the average dose enhancement effect and the maximal voxel dose per photon depends little on the iodine voxel volume ratio, but strongly on IC. Conclusion: To decrease dose damage in SRA, the high-Z contrast agent should be used as little as possible, and try to avoid radiating locally the injected position immediately after the contrast agent injection. The fragile vessel containing iodine should avoid closely irradiating. Avoiding irradiating through the no or thin skull region, or appending thin equivalent material from outside to protect is also a better method. As long as SRA image quality is ensured, using incident X-ray energy as low as possible.« less

Linking brain-wide multivoxel activation patterns to behaviour: Examples from language and math.

PubMed

Raizada, Rajeev D S; Tsao, Feng-Ming; Liu, Huei-Mei; Holloway, Ian D; Ansari, Daniel; Kuhl, Patricia K

2010-05-15

A key goal of cognitive neuroscience is to find simple and direct connections between brain and behaviour. However, fMRI analysis typically involves choices between many possible options, with each choice potentially biasing any brain-behaviour correlations that emerge. Standard methods of fMRI analysis assess each voxel individually, but then face the problem of selection bias when combining those voxels into a region-of-interest, or ROI. Multivariate pattern-based fMRI analysis methods use classifiers to analyse multiple voxels together, but can also introduce selection bias via data-reduction steps as feature selection of voxels, pre-selecting activated regions, or principal components analysis. We show here that strong brain-behaviour links can be revealed without any voxel selection or data reduction, using just plain linear regression as a classifier applied to the whole brain at once, i.e. treating each entire brain volume as a single multi-voxel pattern. The brain-behaviour correlations emerged despite the fact that the classifier was not provided with any information at all about subjects' behaviour, but instead was given only the neural data and its condition-labels. Surprisingly, more powerful classifiers such as a linear SVM and regularised logistic regression produce very similar results. We discuss some possible reasons why the very simple brain-wide linear regression model is able to find correlations with behaviour that are as strong as those obtained on the one hand from a specific ROI and on the other hand from more complex classifiers. In a manner which is unencumbered by arbitrary choices, our approach offers a method for investigating connections between brain and behaviour which is simple, rigorous and direct. Copyright (c) 2010 Elsevier Inc. All rights reserved.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chen, L; Shen, C; Wang, J

Purpose: To reduce cone beam CT (CBCT) imaging dose, we previously proposed a progressive dose control (PDC) scheme to employ temporal correlation between CBCT images at different fractions for image quality enhancement. A temporal non-local means (TNLM) method was developed to enhance quality of a new low-dose CBCT using existing high-quality CBCT. To enhance a voxel value, the TNLM method searches for similar voxels in a window. Due to patient deformation among the two CBCTs, a large searching window was required, reducing image quality and computational efficiency. This abstract proposes a deformation-assisted TNLM (DA-TNLM) method to solve this problem. Methods:more » For a low-dose CBCT to be enhanced using a high-quality CBCT, we first performed deformable image registration between the low-dose CBCT and the high-quality CBCT to approximately establish voxel correspondence between the two. A searching window for a voxel was then set based on the deformation vector field. Specifically, the search window for each voxel was shifted by the deformation vector. A TNLM step was then applied using only voxels within this determined window to correct image intensity at the low-dose CBCT. Results: We have tested the proposed scheme on simulated CIRS phantom data and real patient data. The CITS phantom was scanned on Varian onboard imaging CBCT system with coach shifting and dose reducing for each time. The real patient data was acquired in four fractions with dose reduced from standard CBCT dose to 12.5% of standard dose. It was found that the DA-TNLM method can reduce total dose by over 75% on average in the first four fractions. Conclusion: We have developed a PDC scheme which can enhance the quality of image scanned at low dose using a DA-TNLM method. Tests in phantom and patient studies demonstrated promising results.« less

Linking brain-wide multivoxel activation patterns to behaviour: Examples from language and math

PubMed Central

Raizada, Rajeev D.S.; Tsao, Feng-Ming; Liu, Huei-Mei; Holloway, Ian D.; Ansari, Daniel; Kuhl, Patricia K.

2010-01-01

A key goal of cognitive neuroscience is to find simple and direct connections between brain and behaviour. However, fMRI analysis typically involves choices between many possible options, with each choice potentially biasing any brain–behaviour correlations that emerge. Standard methods of fMRI analysis assess each voxel individually, but then face the problem of selection bias when combining those voxels into a region-of-interest, or ROI. Multivariate pattern-based fMRI analysis methods use classifiers to analyse multiple voxels together, but can also introduce selection bias via data-reduction steps as feature selection of voxels, pre-selecting activated regions, or principal components analysis. We show here that strong brain–behaviour links can be revealed without any voxel selection or data reduction, using just plain linear regression as a classifier applied to the whole brain at once, i.e. treating each entire brain volume as a single multi-voxel pattern. The brain–behaviour correlations emerged despite the fact that the classifier was not provided with any information at all about subjects' behaviour, but instead was given only the neural data and its condition-labels. Surprisingly, more powerful classifiers such as a linear SVM and regularised logistic regression produce very similar results. We discuss some possible reasons why the very simple brain-wide linear regression model is able to find correlations with behaviour that are as strong as those obtained on the one hand from a specific ROI and on the other hand from more complex classifiers. In a manner which is unencumbered by arbitrary choices, our approach offers a method for investigating connections between brain and behaviour which is simple, rigorous and direct. PMID:20132896

SU-E-J-90: Lobar-Level Lung Ventilation Analysis Using 4DCT and Deformable Image Registration

DOE Office of Scientific and Technical Information (OSTI.GOV)

Du, K; Bayouth, J; Patton, T

2015-06-15

Purpose: To assess regional changes in human lung ventilation and mechanics using four-dimensional computed tomography (4DCT) and deformable image registration. This work extends our prior analysis of the entire lung to a lobe-based analysis. Methods: 4DCT images acquired from 20 patients prior to radiation therapy (RT) were used for this analysis. Jacobian ventilation and motion maps were computed from the displacement field after deformable image registration between the end of expiration breathing phase and the end of inspiration breathing phase. The lobes were manually segmented on the reference phase by a medical physicist expert. The voxel-by-voxel ventilation and motion magnitudemore » for all subjects were grouped by lobes and plotted into cumulative voxel frequency curves respectively. In addition, to eliminate the effect of different breathing efforts across subjects, we applied the inter-subject equivalent lung volume (ELV) method on a subset of the cohort and reevaluated the lobar ventilation. Results: 95% of voxels in the lung are expanding during inspiration. However, some local regions of lung tissue show far more expansion than others. The greatest expansion with respiration occurs within the lower lobes; between exhale and inhale the median expansion in lower lobes is approximately 15%, while the median expansion in upper lobes is 10%. This appears to be driven by a subset of lung tissues within the lobe that have greater expansion; twice the number of voxels in the lower lobes (20%) expand by > 30% when compared to the upper lobes (10%). Conclusion: Lung ventilation and motion show significant difference on the lobar level. There are different lobar fractions of driving voxels that contribute to the major expansion of the lung. This work was supported by NIH grant CA166703.« less

Comparison of Arterial Spin-labeling Perfusion Images at Different Spatial Normalization Methods Based on Voxel-based Statistical Analysis.

PubMed

Tani, Kazuki; Mio, Motohira; Toyofuku, Tatsuo; Kato, Shinichi; Masumoto, Tomoya; Ijichi, Tetsuya; Matsushima, Masatoshi; Morimoto, Shoichi; Hirata, Takumi

2017-01-01

Spatial normalization is a significant image pre-processing operation in statistical parametric mapping (SPM) analysis. The purpose of this study was to clarify the optimal method of spatial normalization for improving diagnostic accuracy in SPM analysis of arterial spin-labeling (ASL) perfusion images. We evaluated the SPM results of five spatial normalization methods obtained by comparing patients with Alzheimer's disease or normal pressure hydrocephalus complicated with dementia and cognitively healthy subjects. We used the following methods: 3DT1-conventional based on spatial normalization using anatomical images; 3DT1-DARTEL based on spatial normalization with DARTEL using anatomical images; 3DT1-conventional template and 3DT1-DARTEL template, created by averaging cognitively healthy subjects spatially normalized using the above methods; and ASL-DARTEL template created by averaging cognitively healthy subjects spatially normalized with DARTEL using ASL images only. Our results showed that ASL-DARTEL template was small compared with the other two templates. Our SPM results obtained with ASL-DARTEL template method were inaccurate. Also, there were no significant differences between 3DT1-conventional and 3DT1-DARTEL template methods. In contrast, the 3DT1-DARTEL method showed higher detection sensitivity, and precise anatomical location. Our SPM results suggest that we should perform spatial normalization with DARTEL using anatomical images.

Multiple-point statistical simulation for hydrogeological models: 3-D training image development and conditioning strategies

NASA Astrophysics Data System (ADS)

Høyer, Anne-Sophie; Vignoli, Giulio; Mejer Hansen, Thomas; Thanh Vu, Le; Keefer, Donald A.; Jørgensen, Flemming

2017-12-01

Most studies on the application of geostatistical simulations based on multiple-point statistics (MPS) to hydrogeological modelling focus on relatively fine-scale models and concentrate on the estimation of facies-level structural uncertainty. Much less attention is paid to the use of input data and optimal construction of training images. For instance, even though the training image should capture a set of spatial geological characteristics to guide the simulations, the majority of the research still relies on 2-D or quasi-3-D training images. In the present study, we demonstrate a novel strategy for 3-D MPS modelling characterized by (i) realistic 3-D training images and (ii) an effective workflow for incorporating a diverse group of geological and geophysical data sets. The study covers an area of 2810 km2 in the southern part of Denmark. MPS simulations are performed on a subset of the geological succession (the lower to middle Miocene sediments) which is characterized by relatively uniform structures and dominated by sand and clay. The simulated domain is large and each of the geostatistical realizations contains approximately 45 million voxels with size 100 m × 100 m × 5 m. Data used for the modelling include water well logs, high-resolution seismic data, and a previously published 3-D geological model. We apply a series of different strategies for the simulations based on data quality, and develop a novel method to effectively create observed spatial trends. The training image is constructed as a relatively small 3-D voxel model covering an area of 90 km2. We use an iterative training image development strategy and find that even slight modifications in the training image create significant changes in simulations. Thus, this study shows how to include both the geological environment and the type and quality of input information in order to achieve optimal results from MPS modelling. We present a practical workflow to build the training image and effectively handle different types of input information to perform large-scale geostatistical modelling.

Probabilistic, sediment-geochemical parameterisation of the groundwater compartment of the Netherlands for spatially distributed, reactive transport modelling

NASA Astrophysics Data System (ADS)

Janssen, Gijs; Gunnink, Jan; van Vliet, Marielle; Goldberg, Tanya; Griffioen, Jasper

2017-04-01

Pollution of groundwater aquifers with contaminants as nitrate is a common problem. Reactive transport models are useful to predict the fate of such contaminants and to characterise the efficiency of mitigating or preventive measures. Parameterisation of a groundwater transport model on reaction capacity is a necessary step during building the model. Two Dutch, national programs are combined to establish a methodology for building a probabilistic model on reaction capacity of the groundwater compartment at the national scale: the Geological Survey program and the NHI Netherlands Hydrological Instrument program. Reaction capacity is considered as a series of geochemical characteristics that control acid/base condition, redox condition and sorption capacity. Five primary reaction capacity variables are characterised: 1. pyrite, 2. non-pyrite, reactive iron (oxides, siderite and glauconite), 3. clay fraction, 4. organic matter and 5. Ca-carbonate. Important reaction capacity variables that are determined by more than one solid compound are also deduced: 1. potential reduction capacity (PRC) by pyrite and organic matter, 2. cation-exchange capacity (CEC) by organic matter and clay content, 3. carbonate buffering upon pyrite oxidation (CPBO) by carbonate and pyrite. Statistical properties of these variables are established based on c. 16,000 sediment geochemical analyses. The first tens of meters are characterised based on 25 regions using combinations of lithological class and geological formation as strata. Because of both less data and more geochemical uniformity, the deeper subsurface is characterised in a similar way based on 3 regions. The statistical data is used as input in an algoritm that probabilistically calculates the reaction capacity per grid cell. First, the cumulative frequency distribution (cfd) functions are calculated from the statistical data for the geochemical strata. Second, all voxel cells are classified into the geochemical strata. Third, the cfd functions are used to put random reaction capacity variables into the hydrological voxel model. Here, the distribution can be conditioned on two variables. Two important variables are clay content and depth. The first is valid because more dense data is available for clay content than for geochemical variables as pyrite and probabilistic, lithological models are also built at TNO Geological Survey. The second is important to account for locally different depths at which the redox cline between NO3-rich and Fe(II)-rich groundwater occurs within the first tens of meters of the subsurface. An extensive data-set of groundwater quality analyses is used to derive criteria for depth variability of the redox cline. The result is a unique algoritm in order to obtain heterogeneous geochemical reaction capacity models of the entire groundwater compartment of the Netherlands.

Diffusion tensor imaging reveals changes in the adult rat brain following long-term and passive moderate acoustic exposure.

PubMed

Abdoli, Sherwin; Ho, Leon C; Zhang, Jevin W; Dong, Celia M; Lau, Condon; Wu, Ed X

2016-12-01

This study investigated neuroanatomical changes following long-term acoustic exposure at moderate sound pressure level (SPL) under passive conditions, without coupled behavioral training. The authors utilized diffusion tensor imaging (DTI) to detect morphological changes in white matter. DTIs from adult rats (n = 8) exposed to continuous acoustic exposure at moderate SPL for 2 months were compared with DTIs from rats (n = 8) reared under standard acoustic conditions. Two distinct forms of DTI analysis were applied in a sequential manner. First, DTI images were analyzed using voxel-based statistics which revealed greater fractional anisotropy (FA) of the pyramidal tract and decreased FA of the tectospinal tract and trigeminothalamic tract of the exposed rats. Region of interest analysis confirmed (p < 0.05) that FA had increased in the pyramidal tract but did not show a statistically significant difference in the FA of the tectospinal or trigeminothalamic tract. The results of the authors show that long-term and passive acoustic exposure at moderate SPL increases the organization of white matter in the pyramidal tract.

Accelerating separable footprint (SF) forward and back projection on GPU

NASA Astrophysics Data System (ADS)

Xie, Xiaobin; McGaffin, Madison G.; Long, Yong; Fessler, Jeffrey A.; Wen, Minhua; Lin, James

2017-03-01

Statistical image reconstruction (SIR) methods for X-ray CT can improve image quality and reduce radiation dosages over conventional reconstruction methods, such as filtered back projection (FBP). However, SIR methods require much longer computation time. The separable footprint (SF) forward and back projection technique simplifies the calculation of intersecting volumes of image voxels and finite-size beams in a way that is both accurate and efficient for parallel implementation. We propose a new method to accelerate the SF forward and back projection on GPU with NVIDIA's CUDA environment. For the forward projection, we parallelize over all detector cells. For the back projection, we parallelize over all 3D image voxels. The simulation results show that the proposed method is faster than the acceleration method of the SF projectors proposed by Wu and Fessler.13 We further accelerate the proposed method using multiple GPUs. The results show that the computation time is reduced approximately proportional to the number of GPUs.

LORETA imaging of P300 in schizophrenia with individual MRI and 128-channel EEG.

PubMed

Pae, Ji Soo; Kwon, Jun Soo; Youn, Tak; Park, Hae-Jeong; Kim, Myung Sun; Lee, Boreom; Park, Kwang Suk

2003-11-01

We investigated the characteristics of P300 generators in schizophrenics by using voxel-based statistical parametric mapping of current density images. P300 generators, produced by a rare target tone of 1500 Hz (15%) under a frequent nontarget tone of 1000 Hz (85%), were measured in 20 right-handed schizophrenics and 21 controls. Low-resolution electromagnetic tomography (LORETA), using a realistic head model of the boundary element method based on individual MRI, was applied to the 128-channel EEG. Three-dimensional current density images were reconstructed from the LORETA intensity maps that covered the whole cortical gray matter. Spatial normalization and intensity normalization of the smoothed current density images were used to reduce anatomical variance and subject-specific global activity and statistical parametric mapping (SPM) was applied for the statistical analysis. We found that the sources of P300 were consistently localized at the left superior parietal area in normal subjects, while those of schizophrenics were diversely distributed. Upon statistical comparison, schizophrenics, with globally reduced current densities, showed a significant P300 current density reduction in the left medial temporal area and in the left inferior parietal area, while both left prefrontal and right orbitofrontal areas were relatively activated. The left parietotemporal area was found to correlate negatively with Positive and Negative Syndrome Scale total scores of schizophrenic patients. In conclusion, the reduced and increased areas of current density in schizophrenic patients suggest that the medial temporal and frontal areas contribute to the pathophysiology of schizophrenia, the frontotemporal circuitry abnormality.

Lesion correlates of patholinguistic profiles in chronic aphasia: comparisons of syndrome-, modality- and symptom-level assessment.

PubMed

Henseler, Ilona; Regenbrecht, Frank; Obrig, Hellmuth

2014-03-01

One way to investigate the neuronal underpinnings of language competence is to correlate patholinguistic profiles of aphasic patients to corresponding lesion sites. Constituting the beginnings of aphasiology and neurolinguistics over a century ago, this approach has been revived and refined in the past decade by statistical approaches mapping continuous variables (providing metrics that are not simply categorical) on voxel-wise lesion information (voxel-based lesion-symptom mapping). Here we investigate whether and how voxel-based lesion-symptom mapping allows us to delineate specific lesion patterns for differentially fine-grained clinical classifications. The latter encompass 'classical' syndrome-based approaches (e.g. Broca's aphasia), more symptom-oriented descriptions (e.g. agrammatism) and further refinement to linguistic sub-functions (e.g. lexico-semantic deficits for inanimate versus animate items). From a large database of patients treated for aphasia of different aetiologies (n = 1167) a carefully selected group of 102 first ever ischaemic stroke patients with chronic aphasia (∅ 12 months) were included in a VLSM analysis. Specifically, we investigated how performance in the Aachen Aphasia Test-the standard clinical test battery for chronic aphasia in German-relates to distinct brain lesions. The Aachen Aphasia Test evaluates aphasia on different levels: a non-parametric discriminant procedure yields probabilities for the allocation to one of the four 'standard' syndromes (Broca, Wernicke, global and amnestic aphasia), whereas standardized subtests target linguistic modalities (e.g. repetition), or even more specific symptoms (e.g. phoneme repetition). Because some subtests of the Aachen Aphasia Test (e.g. for the linguistic level of lexico-semantics) rely on rather coarse and heterogeneous test items we complemented the analysis with a number of more detailed clinically used tests in selected mostly mildly affected subgroups of patients. Our results indicate that: (i) Aachen Aphasia Test-based syndrome allocation allows for an unexpectedly concise differentiation between 'Broca's' and 'Wernicke's' aphasia corresponding to non-overlapping anterior and posterior lesion sites; whereas (ii) analyses for modalities and specific symptoms yielded more circumscribed but partially overlapping lesion foci, often cutting across the above syndrome territories; and (iii) especially for lexico-semantic capacities more specialized clinical test-batteries are required to delineate precise lesion patterns at this linguistic level. In sum this is the first report on a successful lesion-delineation of syndrome-based aphasia classification highlighting the relevance of vascular distribution for the syndrome level while confirming and extending a number of more linguistically motivated differentiations, based on clinically used tests. We consider such a comprehensive view reaching from the syndrome to a fine-grained symptom-oriented assessment mandatory to converge neurolinguistic, patholinguistic and clinical-therapeutic knowledge on language-competence and impairment.

Voxel-Based Morphometry ALE meta-analysis of Bipolar Disorder

NASA Astrophysics Data System (ADS)

Magana, Omar; Laird, Robert

2012-03-01

A meta-analysis was performed independently to view the changes in gray matter (GM) on patients with Bipolar disorder (BP). The meta-analysis was conducted on a Talairach Space using GingerALE to determine the voxels and their permutation. In order to achieve the data acquisition, published experiments and similar research studies were uploaded onto the online Voxel-Based Morphometry database (VBM). By doing so, coordinates of activation locations were extracted from Bipolar disorder related journals utilizing Sleuth. Once the coordinates of given experiments were selected and imported to GingerALE, a Gaussian was performed on all foci points to create the concentration points of GM on BP patients. The results included volume reductions and variations of GM between Normal Healthy controls and Patients with Bipolar disorder. A significant amount of GM clusters were obtained in Normal Healthy controls over BP patients on the right precentral gyrus, right anterior cingulate, and the left inferior frontal gyrus. In future research, more published journals could be uploaded onto the database and another VBM meta-analysis could be performed including more activation coordinates or a variation of age groups.

Automatic segmentation of multimodal brain tumor images based on classification of super-voxels.

PubMed

Kadkhodaei, M; Samavi, S; Karimi, N; Mohaghegh, H; Soroushmehr, S M R; Ward, K; All, A; Najarian, K

2016-08-01

Despite the rapid growth in brain tumor segmentation approaches, there are still many challenges in this field. Automatic segmentation of brain images has a critical role in decreasing the burden of manual labeling and increasing robustness of brain tumor diagnosis. We consider segmentation of glioma tumors, which have a wide variation in size, shape and appearance properties. In this paper images are enhanced and normalized to same scale in a preprocessing step. The enhanced images are then segmented based on their intensities using 3D super-voxels. Usually in images a tumor region can be regarded as a salient object. Inspired by this observation, we propose a new feature which uses a saliency detection algorithm. An edge-aware filtering technique is employed to align edges of the original image to the saliency map which enhances the boundaries of the tumor. Then, for classification of tumors in brain images, a set of robust texture features are extracted from super-voxels. Experimental results indicate that our proposed method outperforms a comparable state-of-the-art algorithm in term of dice score.

Making data matter: Voxel printing for the digital fabrication of data across scales and domains.

PubMed

Bader, Christoph; Kolb, Dominik; Weaver, James C; Sharma, Sunanda; Hosny, Ahmed; Costa, João; Oxman, Neri

2018-05-01

We present a multimaterial voxel-printing method that enables the physical visualization of data sets commonly associated with scientific imaging. Leveraging voxel-based control of multimaterial three-dimensional (3D) printing, our method enables additive manufacturing of discontinuous data types such as point cloud data, curve and graph data, image-based data, and volumetric data. By converting data sets into dithered material deposition descriptions, through modifications to rasterization processes, we demonstrate that data sets frequently visualized on screen can be converted into physical, materially heterogeneous objects. Our approach alleviates the need to postprocess data sets to boundary representations, preventing alteration of data and loss of information in the produced physicalizations. Therefore, it bridges the gap between digital information representation and physical material composition. We evaluate the visual characteristics and features of our method, assess its relevance and applicability in the production of physical visualizations, and detail the conversion of data sets for multimaterial 3D printing. We conclude with exemplary 3D-printed data sets produced by our method pointing toward potential applications across scales, disciplines, and problem domains.

OASIS is Automated Statistical Inference for Segmentation, with applications to multiple sclerosis lesion segmentation in MRI.

PubMed

Sweeney, Elizabeth M; Shinohara, Russell T; Shiee, Navid; Mateen, Farrah J; Chudgar, Avni A; Cuzzocreo, Jennifer L; Calabresi, Peter A; Pham, Dzung L; Reich, Daniel S; Crainiceanu, Ciprian M

2013-01-01

Magnetic resonance imaging (MRI) can be used to detect lesions in the brains of multiple sclerosis (MS) patients and is essential for diagnosing the disease and monitoring its progression. In practice, lesion load is often quantified by either manual or semi-automated segmentation of MRI, which is time-consuming, costly, and associated with large inter- and intra-observer variability. We propose OASIS is Automated Statistical Inference for Segmentation (OASIS), an automated statistical method for segmenting MS lesions in MRI studies. We use logistic regression models incorporating multiple MRI modalities to estimate voxel-level probabilities of lesion presence. Intensity-normalized T1-weighted, T2-weighted, fluid-attenuated inversion recovery and proton density volumes from 131 MRI studies (98 MS subjects, 33 healthy subjects) with manual lesion segmentations were used to train and validate our model. Within this set, OASIS detected lesions with a partial area under the receiver operating characteristic curve for clinically relevant false positive rates of 1% and below of 0.59% (95% CI; [0.50%, 0.67%]) at the voxel level. An experienced MS neuroradiologist compared these segmentations to those produced by LesionTOADS, an image segmentation software that provides segmentation of both lesions and normal brain structures. For lesions, OASIS out-performed LesionTOADS in 74% (95% CI: [65%, 82%]) of cases for the 98 MS subjects. To further validate the method, we applied OASIS to 169 MRI studies acquired at a separate center. The neuroradiologist again compared the OASIS segmentations to those from LesionTOADS. For lesions, OASIS ranked higher than LesionTOADS in 77% (95% CI: [71%, 83%]) of cases. For a randomly selected subset of 50 of these studies, one additional radiologist and one neurologist also scored the images. Within this set, the neuroradiologist ranked OASIS higher than LesionTOADS in 76% (95% CI: [64%, 88%]) of cases, the neurologist 66% (95% CI: [52%, 78%]) and the radiologist 52% (95% CI: [38%, 66%]). OASIS obtains the estimated probability for each voxel to be part of a lesion by weighting each imaging modality with coefficient weights. These coefficients are explicit, obtained using standard model fitting techniques, and can be reused in other imaging studies. This fully automated method allows sensitive and specific detection of lesion presence and may be rapidly applied to large collections of images.

Voxel-based lesion mapping of meningioma: a comprehensive lesion location mapping of 260 lesions.

PubMed

Hirayama, Ryuichi; Kinoshita, Manabu; Arita, Hideyuki; Kagawa, Naoki; Kishima, Haruhiko; Hashimoto, Naoya; Fujimoto, Yasunori; Yoshimine, Toshiki

2018-06-01

OBJECTIVE In the present study the authors aimed to determine preferred locations of meningiomas by avoiding descriptive analysis and instead using voxel-based lesion mapping and 3D image-rendering techniques. METHODS Magnetic resonance images obtained in 248 treatment-naïve meningioma patients with 260 lesions were retrospectively and consecutively collected. All images were registered to a 1-mm isotropic, high-resolution, T1-weighted brain atlas provided by the Montreal Neurological Institute (the MNI152), and a lesion frequency map was created, followed by 3D volume rendering to visualize the preferred locations of meningiomas in 3D. RESULTS The 3D lesion frequency map clearly showed that skull base structures such as parasellar, sphenoid wing, and petroclival regions were commonly affected by the tumor. The middle one-third of the superior sagittal sinus was most commonly affected in parasagittal tumors. Substantial lesion accumulation was observed around the leptomeninges covering the central sulcus and the sylvian fissure, with very few lesions observed at the frontal, parietal, and occipital convexities. CONCLUSIONS Using an objective visualization method, meningiomas were shown to be located around the middle third of the superior sagittal sinus, the perisylvian convexity, and the skull base. These observations, which are in line with previous descriptive analyses, justify further use of voxel-based lesion mapping techniques to help understand the biological nature of this disease.

Automatic Generation of Indoor Navigable Space Using a Point Cloud and its Scanner Trajectory

NASA Astrophysics Data System (ADS)

Staats, B. R.; Diakité, A. A.; Voûte, R. L.; Zlatanova, S.

2017-09-01

Automatic generation of indoor navigable models is mostly based on 2D floor plans. However, in many cases the floor plans are out of date. Buildings are not always built according to their blue prints, interiors might change after a few years because of modified walls and doors, and furniture may be repositioned to the user's preferences. Therefore, new approaches for the quick recording of indoor environments should be investigated. This paper concentrates on laser scanning with a Mobile Laser Scanner (MLS) device. The MLS device stores a point cloud and its trajectory. If the MLS device is operated by a human, the trajectory contains information which can be used to distinguish different surfaces. In this paper a method is presented for the identification of walkable surfaces based on the analysis of the point cloud and the trajectory of the MLS scanner. This method consists of several steps. First, the point cloud is voxelized. Second, the trajectory is analysing and projecting to acquire seed voxels. Third, these seed voxels are generated into floor regions by the use of a region growing process. By identifying dynamic objects, doors and furniture, these floor regions can be modified so that each region represents a specific navigable space inside a building as a free navigable voxel space. By combining the point cloud and its corresponding trajectory, the walkable space can be identified for any type of building even if the interior is scanned during business hours.

Regional patterns of grey matter atrophy and magnetisation transfer ratio abnormalities in multiple sclerosis clinical subgroups: A voxel-based analysis study

PubMed Central

Muhlert, Nils; Samson, Rebecca S; Sethi, Varun; Wheeler-Kingshott, Claudia AM; Miller, David H; Chard, Declan T

2015-01-01

Background: In multiple sclerosis (MS), demyelination and neuro-axonal loss occur in the brain grey matter (GM). We used magnetic resonance imaging (MRI) measures of GM magnetisation transfer ratio (MTR) and volume to assess the regional localisation of reduced MTR (reflecting demyelination) and atrophy (reflecting neuro-axonal loss) in relapsing–remitting MS (RRMS), secondary progressive MS (SPMS) and primary progressive MS (PPMS). Methods: A total of 98 people with MS (51 RRMS, 28 SPMS, 19 PPMS) and 29 controls had T1-weighted volumetric and magnetisation transfer scans. SPM8 was used to undertake voxel-based analysis (VBA) of GM tissue volumes and MTR. MS subgroups were compared with controls, adjusting for age and gender. A voxel-by-voxel basis correlation analysis between MTR and volume within each subject group was performed, using biological parametric mapping. Results: MTR reduction was more extensive than atrophy. RRMS and SPMS patients showed proportionately more atrophy in the deep GM. SPMS and PPMS patients showed proportionately greater cortical MTR reduction. RRMS patients demonstrated the most correlation of MTR reduction and atrophy in deep GM. In SPMS and PPMS patients, there was less extensive correlation. Conclusions: These results suggest that in the deep GM of RRMS patients, demyelination and neuro-axonal loss may be linked, while in SPMS and PPMS patients, neuro-axonal loss and demyelination may occur mostly independently. PMID:25145689

Long-Term Functional Outcomes and Correlation with Regional Brain Connectivity by MRI Diffusion Tractography Metrics in a Near-Term Rabbit Model of Intrauterine Growth Restriction

PubMed Central

Illa, Miriam; Eixarch, Elisenda; Batalle, Dafnis; Arbat-Plana, Ariadna; Muñoz-Moreno, Emma; Figueras, Francesc; Gratacos, Eduard

2013-01-01

Background Intrauterine growth restriction (IUGR) affects 5–10% of all newborns and is associated with increased risk of memory, attention and anxiety problems in late childhood and adolescence. The neurostructural correlates of long-term abnormal neurodevelopment associated with IUGR are unknown. Thus, the aim of this study was to provide a comprehensive description of the long-term functional and neurostructural correlates of abnormal neurodevelopment associated with IUGR in a near-term rabbit model (delivered at 30 days of gestation) and evaluate the development of quantitative imaging biomarkers of abnormal neurodevelopment based on diffusion magnetic resonance imaging (MRI) parameters and connectivity. Methodology At +70 postnatal days, 10 cases and 11 controls were functionally evaluated with the Open Field Behavioral Test which evaluates anxiety and attention and the Object Recognition Task that evaluates short-term memory and attention. Subsequently, brains were collected, fixed and a high resolution MRI was performed. Differences in diffusion parameters were analyzed by means of voxel-based and connectivity analysis measuring the number of fibers reconstructed within anxiety, attention and short-term memory networks over the total fibers. Principal Findings The results of the neurobehavioral and cognitive assessment showed a significant higher degree of anxiety, attention and memory problems in cases compared to controls in most of the variables explored. Voxel-based analysis (VBA) revealed significant differences between groups in multiple brain regions mainly in grey matter structures, whereas connectivity analysis demonstrated lower ratios of fibers within the networks in cases, reaching the statistical significance only in the left hemisphere for both networks. Finally, VBA and connectivity results were also correlated with functional outcome. Conclusions The rabbit model used reproduced long-term functional impairments and their neurostructural correlates of abnormal neurodevelopment associated with IUGR. The description of the pattern of microstructural changes underlying functional defects may help to develop biomarkers based in diffusion MRI and connectivity analysis. PMID:24143189

Long-term functional outcomes and correlation with regional brain connectivity by MRI diffusion tractography metrics in a near-term rabbit model of intrauterine growth restriction.

PubMed

Illa, Miriam; Eixarch, Elisenda; Batalle, Dafnis; Arbat-Plana, Ariadna; Muñoz-Moreno, Emma; Figueras, Francesc; Gratacos, Eduard

2013-01-01

Intrauterine growth restriction (IUGR) affects 5-10% of all newborns and is associated with increased risk of memory, attention and anxiety problems in late childhood and adolescence. The neurostructural correlates of long-term abnormal neurodevelopment associated with IUGR are unknown. Thus, the aim of this study was to provide a comprehensive description of the long-term functional and neurostructural correlates of abnormal neurodevelopment associated with IUGR in a near-term rabbit model (delivered at 30 days of gestation) and evaluate the development of quantitative imaging biomarkers of abnormal neurodevelopment based on diffusion magnetic resonance imaging (MRI) parameters and connectivity. At +70 postnatal days, 10 cases and 11 controls were functionally evaluated with the Open Field Behavioral Test which evaluates anxiety and attention and the Object Recognition Task that evaluates short-term memory and attention. Subsequently, brains were collected, fixed and a high resolution MRI was performed. Differences in diffusion parameters were analyzed by means of voxel-based and connectivity analysis measuring the number of fibers reconstructed within anxiety, attention and short-term memory networks over the total fibers. The results of the neurobehavioral and cognitive assessment showed a significant higher degree of anxiety, attention and memory problems in cases compared to controls in most of the variables explored. Voxel-based analysis (VBA) revealed significant differences between groups in multiple brain regions mainly in grey matter structures, whereas connectivity analysis demonstrated lower ratios of fibers within the networks in cases, reaching the statistical significance only in the left hemisphere for both networks. Finally, VBA and connectivity results were also correlated with functional outcome. The rabbit model used reproduced long-term functional impairments and their neurostructural correlates of abnormal neurodevelopment associated with IUGR. The description of the pattern of microstructural changes underlying functional defects may help to develop biomarkers based in diffusion MRI and connectivity analysis.

SU-E-J-159: Intra-Patient Deformable Image Registration Uncertainties Quantified Using the Distance Discordance Metric

DOE Office of Scientific and Technical Information (OSTI.GOV)

Saleh, Z; Thor, M; Apte, A

2014-06-01

Purpose: The quantitative evaluation of deformable image registration (DIR) is currently challenging due to lack of a ground truth. In this study we test a new method proposed for quantifying multiple-image based DIRrelated uncertainties, for DIR of pelvic images. Methods: 19 patients were analyzed, each with 6 CT scans, who previously had radiotherapy for prostate cancer. Manually delineated structures for rectum and bladder, which served as ground truth structures, were delineated on the planning CT and each subsequent scan. For each patient, voxel-by-voxel DIR-related uncertainties were evaluated, following B-spline based DIR, by applying a previously developed metric, the distance discordancemore » metric (DDM; Saleh et al., PMB (2014) 59:733). The DDM map was superimposed on the first acquired CT scan and DDM statistics were assessed, also relative to two metrics estimating the agreement between the propagated and the manually delineated structures. Results: The highest DDM values which correspond to greatest spatial uncertainties were observed near the body surface and in the bowel due to the presence of gas. The mean rectal and bladder DDM values ranged from 1.1–11.1 mm and 1.5–12.7 mm, respectively. There was a strong correlation in the DDMs between the rectum and bladder (Pearson R = 0.68 for the max DDM). For both structures, DDM was correlated with the ratio between the DIR-propagated and manually delineated volumes (R = 0.74 for the max rectal DDM). The maximum rectal DDM was negatively correlated with the Dice Similarity Coefficient between the propagated and the manually delineated volumes (R= −0.52). Conclusion: The multipleimage based DDM map quantified considerable DIR variability across different structures and among patients. Besides using the DDM for quantifying DIR-related uncertainties it could potentially be used to adjust for uncertainties in DIR-based accumulated dose distributions.« less

Electronically tunable metamaterials using subwavelength magnetoresponsive particles

NASA Astrophysics Data System (ADS)

Allen, Monica; Allen, Jeffery; Parrow, Jacob; Asif, Sajid; Iftikar, Adnan; Wenner, Brett; Braaten, Benjamin

We demonstrate tunability of material properties of an engineered electromagnetic material in the RF regime using microparticles that respond to static magnetic biasing fields. The magnetic particles align with field lines creating a short/inductive state of the switch in the addressed voxel. When the biasing magnetic field is removed, the switch returns to an open/capacitive state. Each voxel measures 1.5 mm x 1.5 mm x 0.508 mm in the x, y, and z direction respectively, with a 0.9 mm diameter cylindrical cavity. The cavity is along the z-axis and is partially filled with microparticles composed of a magnetite core with Ag coating. Cu foil placed on the top and bottom encloses the particles in the cavity and acts as the biasing electrodes. Switching between inductive and capacitive states in spatially addressed voxels controls the cumulative ɛ and μ of the host material (i.e., layer) and controls the phase of an incident wave. We present finite element based models of prototype voxels with experimental measurements that validate the models on a host. This research can be applied to real-time tuning of material parameters with subwavelength voxel precision enabling wave control/manipulation as well as devices for switching and software-dictated tunable impedance capabilities. Authors JWA, MSA and BRW are grateful for support from AFOSR Lab Task 17RWCOR397 (Dr. H. Weinstock). NDSU was supported by (FA-8651-15-2-002) from the US Air Force Research Laboratory Munitions Directorate.

Improved estimates of partial volume coefficients from noisy brain MRI using spatial context.

PubMed

Manjón, José V; Tohka, Jussi; Robles, Montserrat

2010-11-01

This paper addresses the problem of accurate voxel-level estimation of tissue proportions in the human brain magnetic resonance imaging (MRI). Due to the finite resolution of acquisition systems, MRI voxels can contain contributions from more than a single tissue type. The voxel-level estimation of this fractional content is known as partial volume coefficient estimation. In the present work, two new methods to calculate the partial volume coefficients under noisy conditions are introduced and compared with current similar methods. Concretely, a novel Markov Random Field model allowing sharp transitions between partial volume coefficients of neighbouring voxels and an advanced non-local means filtering technique are proposed to reduce the errors due to random noise in the partial volume coefficient estimation. In addition, a comparison was made to find out how the different methodologies affect the measurement of the brain tissue type volumes. Based on the obtained results, the main conclusions are that (1) both Markov Random Field modelling and non-local means filtering improved the partial volume coefficient estimation results, and (2) non-local means filtering was the better of the two strategies for partial volume coefficient estimation. Copyright 2010 Elsevier Inc. All rights reserved.

Probabilistic Evaluation of Three-Dimensional Reconstructions from X-Ray Images Spanning a Limited Angle

PubMed Central

Frost, Anja; Renners, Eike; Hötter, Michael; Ostermann, Jörn

2013-01-01

An important part of computed tomography is the calculation of a three-dimensional reconstruction of an object from series of X-ray images. Unfortunately, some applications do not provide sufficient X-ray images. Then, the reconstructed objects no longer truly represent the original. Inside of the volumes, the accuracy seems to vary unpredictably. In this paper, we introduce a novel method to evaluate any reconstruction, voxel by voxel. The evaluation is based on a sophisticated probabilistic handling of the measured X-rays, as well as the inclusion of a priori knowledge about the materials that the object receiving the X-ray examination consists of. For each voxel, the proposed method outputs a numerical value that represents the probability of existence of a predefined material at the position of the voxel while doing X-ray. Such a probabilistic quality measure was lacking so far. In our experiment, false reconstructed areas get detected by their low probability. In exact reconstructed areas, a high probability predominates. Receiver Operating Characteristics not only confirm the reliability of our quality measure but also demonstrate that existing methods are less suitable for evaluating a reconstruction. PMID:23344378

Brain metabolism in patients with vegetative state after post-resuscitated hypoxic-ischemic brain injury: statistical parametric mapping analysis of F-18 fluorodeoxyglucose positron emission tomography.

PubMed

Kim, Yong Wook; Kim, Hyoung Seop; An, Young-sil

2013-03-01

Hypoxic-ischemic brain injury (HIBI) after cardiopulmonary resuscitation is one of the most devastating neurological conditions that causing the impaired consciousness. However, there were few studies investigated the changes of brain metabolism in patients with vegetative state (VS) after post-resuscitated HIBI. This study aimed to analyze the change of overall brain metabolism and elucidated the brain area correlated with the level of consciousness (LOC) in patients with VS after post-resuscitated HIBI. We consecutively enrolled 17 patients with VS after HIBI, who experienced cardiopulmonary resuscitation. Overall brain metabolism was measured by F-18 fluorodeoxyglucose positron emission tomography (F-18 FDG PET) and we compared regional brain metabolic patterns from 17 patients with those from 15 normal controls using voxel-by-voxel based statistical parametric mapping analysis. Additionally, we correlated the LOC measured by the JFK-coma recovery scale-revised of each patient with brain metabolism by covariance analysis. Compared with normal controls, the patients with VS after post-resuscitated HIBI revealed significantly decreased brain metabolism in bilateral precuneus, bilateral posterior cingulate gyrus, bilateral middle frontal gyri, bilateral superior parietal gyri, bilateral middle occipital gyri, bilateral precentral gyri (PFEW correctecd < 0.0001), and increased brain metabolism in bilateral insula, bilateral cerebella, and the brainstem (PFEW correctecd < 0.0001). In covariance analysis, the LOC was significantly correlated with brain metabolism in bilateral fusiform and superior temporal gyri (Puncorrected < 0.005). Our study demonstrated that the precuneus, the posterior cingulate area and the frontoparietal cortex, which is a component of neural correlate for consciousness, may be relevant structure for impaired consciousness in patient with VS after post-resuscitated HIBI. In post-resuscitated HIBI, measurement of brain metabolism using PET images may be helpful for investigating the brain function that cannot be obtained by morphological imaging and can be used to assess the brain area responsible for consciousness.

SU-E-T-625: Robustness Evaluation and Robust Optimization of IMPT Plans Based on Per-Voxel Standard Deviation of Dose Distributions.

PubMed

Liu, W; Mohan, R

2012-06-01

Proton dose distributions, IMPT in particular, are highly sensitive to setup and range uncertainties. We report a novel method, based on per-voxel standard deviation (SD) of dose distributions, to evaluate the robustness of proton plans and to robustly optimize IMPT plans to render them less sensitive to uncertainties. For each optimization iteration, nine dose distributions are computed - the nominal one, and one each for ± setup uncertainties along x, y and z axes and for ± range uncertainty. SD of dose in each voxel is used to create SD-volume histogram (SVH) for each structure. SVH may be considered a quantitative representation of the robustness of the dose distribution. For optimization, the desired robustness may be specified in terms of an SD-volume (SV) constraint on the CTV and incorporated as a term in the objective function. Results of optimization with and without this constraint were compared in terms of plan optimality and robustness using the so called'worst case' dose distributions; which are obtained by assigning the lowest among the nine doses to each voxel in the clinical target volume (CTV) and the highest to normal tissue voxels outside the CTV. The SVH curve and the area under it for each structure were used as quantitative measures of robustness. Penalty parameter of SV constraint may be varied to control the tradeoff between robustness and plan optimality. We applied these methods to one case each of H&N and lung. In both cases, we found that imposing SV constraint improved plan robustness but at the cost of normal tissue sparing. SVH-based optimization and evaluation is an effective tool for robustness evaluation and robust optimization of IMPT plans. Studies need to be conducted to test the methods for larger cohorts of patients and for other sites. This research is supported by National Cancer Institute (NCI) grant P01CA021239, the University Cancer Foundation via the Institutional Research Grant program at the University of Texas MD Anderson Cancer Center, and MD Anderson’s cancer center support grant CA016672. © 2012 American Association of Physicists in Medicine.

Regional flux analysis for discovering and quantifying anatomical changes: An application to the brain morphometry in Alzheimer's disease.

PubMed

Lorenzi, M; Ayache, N; Pennec, X

2015-07-15

In this study we introduce the regional flux analysis, a novel approach to deformation based morphometry based on the Helmholtz decomposition of deformations parameterized by stationary velocity fields. We use the scalar pressure map associated to the irrotational component of the deformation to discover the critical regions of volume change. These regions are used to consistently quantify the associated measure of volume change by the probabilistic integration of the flux of the longitudinal deformations across the boundaries. The presented framework unifies voxel-based and regional approaches, and robustly describes the volume changes at both group-wise and subject-specific level as a spatial process governed by consistently defined regions. Our experiments on the large cohorts of the ADNI dataset show that the regional flux analysis is a powerful and flexible instrument for the study of Alzheimer's disease in a wide range of scenarios: cross-sectional deformation based morphometry, longitudinal discovery and quantification of group-wise volume changes, and statistically powered and robust quantification of hippocampal and ventricular atrophy. Copyright © 2015 Elsevier Inc. All rights reserved.

The effect of anatomical modeling on space radiation dose estimates: a comparison of doses for NASA phantoms and the 5th, 50th, and 95th percentile male and female astronauts.

PubMed

Bahadori, Amir A; Van Baalen, Mary; Shavers, Mark R; Dodge, Charles; Semones, Edward J; Bolch, Wesley E

2011-03-21

The National Aeronautics and Space Administration (NASA) performs organ dosimetry and risk assessment for astronauts using model-normalized measurements of the radiation fields encountered in space. To determine the radiation fields in an organ or tissue of interest, particle transport calculations are performed using self-shielding distributions generated with the computer program CAMERA to represent the human body. CAMERA mathematically traces linear rays (or path lengths) through the computerized anatomical man (CAM) phantom, a computational stylized model developed in the early 1970s with organ and body profiles modeled using solid shapes and scaled to represent the body morphometry of the 1950 50th percentile (PCTL) Air Force male. With the increasing use of voxel phantoms in medical and health physics, a conversion from a mathematical-based to a voxel-based ray-tracing algorithm is warranted. In this study, the voxel-based ray tracer (VoBRaT) is introduced to ray trace voxel phantoms using a modified version of the algorithm first proposed by Siddon (1985 Med. Phys. 12 252-5). After validation, VoBRAT is used to evaluate variations in body self-shielding distributions for NASA phantoms and six University of Florida (UF) hybrid phantoms, scaled to represent the 5th, 50th, and 95th PCTL male and female astronaut body morphometries, which have changed considerably since the inception of CAM. These body self-shielding distributions are used to generate organ dose equivalents and effective doses for five commonly evaluated space radiation environments. It is found that dosimetric differences among the phantoms are greatest for soft radiation spectra and light vehicular shielding.

Lung vessel segmentation in CT images using graph-cuts

NASA Astrophysics Data System (ADS)

Zhai, Zhiwei; Staring, Marius; Stoel, Berend C.

2016-03-01

Accurate lung vessel segmentation is an important operation for lung CT analysis. Filters that are based on analyzing the eigenvalues of the Hessian matrix are popular for pulmonary vessel enhancement. However, due to their low response at vessel bifurcations and vessel boundaries, extracting lung vessels by thresholding the vesselness is not sufficiently accurate. Some methods turn to graph-cuts for more accurate segmentation, as it incorporates neighbourhood information. In this work, we propose a new graph-cuts cost function combining appearance and shape, where CT intensity represents appearance and vesselness from a Hessian-based filter represents shape. Due to the amount of voxels in high resolution CT scans, the memory requirement and time consumption for building a graph structure is very high. In order to make the graph representation computationally tractable, those voxels that are considered clearly background are removed from the graph nodes, using a threshold on the vesselness map. The graph structure is then established based on the remaining voxel nodes, source/sink nodes and the neighbourhood relationship of the remaining voxels. Vessels are segmented by minimizing the energy cost function with the graph-cuts optimization framework. We optimized the parameters used in the graph-cuts cost function and evaluated the proposed method with two manually labeled sub-volumes. For independent evaluation, we used 20 CT scans of the VESSEL12 challenge. The evaluation results of the sub-volume data show that the proposed method produced a more accurate vessel segmentation compared to the previous methods, with F1 score 0.76 and 0.69. In the VESSEL12 data-set, our method obtained a competitive performance with an area under the ROC curve of 0.975, especially among the binary submissions.

WE-G-BRE-03: Dose Painting by Numbers Using Targeted Gold Nanoparticles

DOE Office of Scientific and Technical Information (OSTI.GOV)

Altundal, Y; Sajo, E; Korideck, H

Purpose: Homogeneous dose enhancement in tumor cells of lung cancer patients treated with conventional dose of 60–66 Gy in five fractions is limited due to increased risk of toxicity to normal structures. Dose painting by numbers (DPBN) is the prescription of a non-uniform radiation dose distribution in the tumor for each voxel based on the intensity level of that voxel obtained from the tumor image. The purpose of this study is to show that DPBN using targeted gold nanoparticles (GNPs) could enhance conventional doses in the more resistant tumor areas. Methods: Cone beam computed tomography (CBCT) images of GNPs aftermore » intratumoral injection into human tumor were taken at 0, 48, 144 and 160 hours. The dose enhancement in the tumor voxels by secondary electrons from the GNPs was calculated based on analytical microdosimetry methods. The dose enhancement factor (DEF) is the ratio of the doses to the tumor with and without the presence of GNPs. The DEF was calculated for each voxel of the images based on the GNP concentration in the tumor sub-volumes using 6-MV photon spectra obtained using Monte Carlo simulations at 5 cm depth (10×10 cm2 field). Results: The results revealed DEF values of 1.05–2.38 for GNPs concentrations of 1–30 mg/g which corresponds to 12.60 – 28.56 Gy per fraction for delivering 12 Gy per fraction homogenously to lung tumor region. Conclusion: Our preliminary results verify that DPBN could be achieved using GNPs to enhance conventional doses to high risk tumor sub-volumes. In practice, DPBN using GNPs could be achieved due to diffusion of targeted GNPs sustainably released in-situ from radiotherapy biomaterials (e.g. fiducials) coated with polymer film containing the GNPs.« less

Automatic analysis (aa): efficient neuroimaging workflows and parallel processing using Matlab and XML.

PubMed

Cusack, Rhodri; Vicente-Grabovetsky, Alejandro; Mitchell, Daniel J; Wild, Conor J; Auer, Tibor; Linke, Annika C; Peelle, Jonathan E

2014-01-01

Recent years have seen neuroimaging data sets becoming richer, with larger cohorts of participants, a greater variety of acquisition techniques, and increasingly complex analyses. These advances have made data analysis pipelines complicated to set up and run (increasing the risk of human error) and time consuming to execute (restricting what analyses are attempted). Here we present an open-source framework, automatic analysis (aa), to address these concerns. Human efficiency is increased by making code modular and reusable, and managing its execution with a processing engine that tracks what has been completed and what needs to be (re)done. Analysis is accelerated by optional parallel processing of independent tasks on cluster or cloud computing resources. A pipeline comprises a series of modules that each perform a specific task. The processing engine keeps track of the data, calculating a map of upstream and downstream dependencies for each module. Existing modules are available for many analysis tasks, such as SPM-based fMRI preprocessing, individual and group level statistics, voxel-based morphometry, tractography, and multi-voxel pattern analyses (MVPA). However, aa also allows for full customization, and encourages efficient management of code: new modules may be written with only a small code overhead. aa has been used by more than 50 researchers in hundreds of neuroimaging studies comprising thousands of subjects. It has been found to be robust, fast, and efficient, for simple-single subject studies up to multimodal pipelines on hundreds of subjects. It is attractive to both novice and experienced users. aa can reduce the amount of time neuroimaging laboratories spend performing analyses and reduce errors, expanding the range of scientific questions it is practical to address.

Episodic memory function is associated with multiple measures of white matter integrity in cognitive aging

PubMed Central

Lockhart, Samuel N.; Mayda, Adriane B. V.; Roach, Alexandra E.; Fletcher, Evan; Carmichael, Owen; Maillard, Pauline; Schwarz, Christopher G.; Yonelinas, Andrew P.; Ranganath, Charan; DeCarli, Charles

2011-01-01

Previous neuroimaging research indicates that white matter injury and integrity, measured respectively by white matter hyperintensities (WMH) and fractional anisotropy (FA) obtained from diffusion tensor imaging (DTI), differ with aging and cerebrovascular disease (CVD) and are associated with episodic memory deficits in cognitively normal older adults. However, knowledge about tract-specific relationships between WMH, FA, and episodic memory in aging remains limited. We hypothesized that white matter connections between frontal cortex and subcortical structures as well as connections between frontal and temporo-parietal cortex would be most affected. In the current study, we examined relationships between WMH, FA and episodic memory in 15 young adults, 13 elders with minimal WMH and 15 elders with extensive WMH, using an episodic recognition memory test for object-color associations. Voxel-based statistics were used to identify voxel clusters where white matter measures were specifically associated with variations in episodic memory performance, and white matter tracts intersecting these clusters were analyzed to examine white matter-memory relationships. White matter injury and integrity measures were significantly associated with episodic memory in extensive regions of white matter, located predominantly in frontal, parietal, and subcortical regions. Template based tractography indicated that white matter injury, as measured by WMH, in the uncinate and inferior longitudinal fasciculi were significantly negatively associated with episodic memory performance. Other tracts such as thalamo-frontal projections, superior longitudinal fasciculus, and dorsal cingulum bundle demonstrated strong negative associations as well. The results suggest that white matter injury to multiple pathways, including connections of frontal and temporal cortex and frontal-subcortical white matter tracts, plays a critical role in memory differences seen in older individuals. PMID:22438841

Cluster analysis of quantitative parametric maps from DCE-MRI: application in evaluating heterogeneity of tumor response to antiangiogenic treatment.

PubMed

Longo, Dario Livio; Dastrù, Walter; Consolino, Lorena; Espak, Miklos; Arigoni, Maddalena; Cavallo, Federica; Aime, Silvio

2015-07-01

The objective of this study was to compare a clustering approach to conventional analysis methods for assessing changes in pharmacokinetic parameters obtained from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) during antiangiogenic treatment in a breast cancer model. BALB/c mice bearing established transplantable her2+ tumors were treated with a DNA-based antiangiogenic vaccine or with an empty plasmid (untreated group). DCE-MRI was carried out by administering a dose of 0.05 mmol/kg of Gadocoletic acid trisodium salt, a Gd-based blood pool contrast agent (CA) at 1T. Changes in pharmacokinetic estimates (K(trans) and vp) in a nine-day interval were compared between treated and untreated groups on a voxel-by-voxel analysis. The tumor response to therapy was assessed by a clustering approach and compared with conventional summary statistics, with sub-regions analysis and with histogram analysis. Both the K(trans) and vp estimates, following blood-pool CA injection, showed marked and spatial heterogeneous changes with antiangiogenic treatment. Averaged values for the whole tumor region, as well as from the rim/core sub-regions analysis were unable to assess the antiangiogenic response. Histogram analysis resulted in significant changes only in the vp estimates (p<0.05). The proposed clustering approach depicted marked changes in both the K(trans) and vp estimates, with significant spatial heterogeneity in vp maps in response to treatment (p<0.05), provided that DCE-MRI data are properly clustered in three or four sub-regions. This study demonstrated the value of cluster analysis applied to pharmacokinetic DCE-MRI parametric maps for assessing tumor response to antiangiogenic therapy. Copyright © 2015 Elsevier Inc. All rights reserved.

Sex differences of gray matter morphology in cortico-limbic-striatal neural system in major depressive disorder.

PubMed

Kong, Lingtao; Chen, Kaiyuan; Womer, Fay; Jiang, Wenyan; Luo, Xingguang; Driesen, Naomi; Liu, Jie; Blumberg, Hilary; Tang, Yanqing; Xu, Ke; Wang, Fei

2013-06-01

Sex differences are observed in both epidemiological and clinical aspects of major depressive disorder (MDD). The cortico-limbic-striatal neural system, including the prefrontal cortex, amygdala, hippocampus, and striatum, have shown sexually dimorphic morphological features and have been implicated in the dysfunctional regulation of mood and emotion in MDD. In this study, we utilized a whole-brain, voxel-based approach to examine sex differences in the regional distribution of gray matter (GM) morphological abnormalities in medication-naïve participants with MDD. Participants included 29 medication-naïve individuals with MDD (16 females and 13 males) and 33 healthy controls (HC) (17 females and 16 males). Gray matter morphology of the cortico-limbic-striatal neural system was examined using voxel-based morphometry analyzes of high-resolution structural magnetic resonance imaging scans. The main effect of diagnosis and interaction effect of diagnosis by sex on GM morphology were statistically significant (p < 0.05, corrected) in the left ventral prefrontal cortex, right amygdala, right hippocampus and bilateral caudate when comparing the MDD and HC groups. Posthoc analyzes showed that females with MDD had significant GM decreases in limbic regions (p < 0.05, corrected), compared to female HC; while males with MDD demonstrated significant GM reduction in striatal regions, (p < 0.05, corrected), compared to HC males. The observed sex-related patterns of abnormalities within the cortico-limbic-strial neural system, such as predominant prefrontal-limbic abnormalities in MDD females vs. predominant prefrontal-striatal abnormalities in MDD males, suggest differences in neural circuitry that may mediate sex differences in the clinical presentation of MDD and potential targets for sex-differentiated treatment of the disorder. Copyright © 2013 Elsevier Ltd. All rights reserved.

Cortical Brain Atrophy and Intra-Individual Variability in Neuropsychological Test Performance in HIV Disease

PubMed Central

HINES, Lindsay J.; MILLER, Eric N.; HINKIN, Charles H.; ALGER, Jeffery R.; BARKER, Peter; GOODKIN, Karl; MARTIN, Eileen M.; MARUCA, Victoria; RAGIN, Ann; SACKTOR, Ned; SANDERS, Joanne; SELNES, Ola; BECKER, James T.

2015-01-01

Objective To characterize the relationship between dispersion-based intra-individual variability (IIVd) in neuropsychological test performance and brain volume among HIV seropositive and seronegative men and to determine the effects of cardiovascular risk and HIV infection on this relationship. Methods Magnetic Resonance Imaging (MRI) was used to acquire high-resolution neuroanatomic data from 147 men age 50 and over, including 80 HIV seropositive (HIV+) and 67 seronegative controls (HIV−) in this cross-sectional cohort study. Voxel Based Morphometry was used to derive volumetric measurements at the level of the individual voxel. These brain structure maps were analyzed using Statistical Parametric Mapping (SPM2). IIVd was measured by computing intra-individual standard deviations (ISD’s) from the standardized performance scores of five neuropsychological tests: Wechsler Memory Scale-III Visual Reproduction I and II, Logical Memory I and II, Wechsler Adult Intelligence Scale-III Letter Number Sequencing. Results Total gray matter (GM) volume was inversely associated with IIVd. Among all subjects, IIVd -related GM atrophy was observed primarily in: 1) the inferior frontal gyrus bilaterally, the left inferior temporal gyrus extending to the supramarginal gyrus, spanning the lateral sulcus; 2) the right superior parietal lobule and intraparietal sulcus; and, 3) dorsal/ventral regions of the posterior section of the transverse temporal gyrus. HIV status, biological, and cardiovascular disease (CVD) variables were not linked to IIVd -related GM atrophy. Conclusions IIVd in neuropsychological test performance may be a sensitive marker of cortical integrity in older adults, regardless of HIV infection status or CVD risk factors, and degree of intra-individual variability links with volume loss in specific cortical regions; independent of mean-level performance on neuropsychological tests. PMID:26303224

Effects of gestational age on brain volume and cognitive functions in generally healthy very preterm born children during school-age: A voxel-based morphometry study.

PubMed

Lemola, Sakari; Oser, Nadine; Urfer-Maurer, Natalie; Brand, Serge; Holsboer-Trachsler, Edith; Bechtel, Nina; Grob, Alexander; Weber, Peter; Datta, Alexandre N

2017-01-01

To determine whether the relationship of gestational age (GA) with brain volumes and cognitive functions is linear or whether it follows a threshold model in preterm and term born children during school-age. We studied 106 children (M = 10 years 1 month, SD = 16 months; 40 females) enrolled in primary school: 57 were healthy very preterm children (10 children born 24-27 completed weeks' gestation (extremely preterm), 14 children born 28-29 completed weeks' gestation, 19 children born 30-31 completed weeks' gestation (very preterm), and 14 born 32 completed weeks' gestation (moderately preterm)) all born appropriate for GA (AGA) and 49 term-born children. Neuroimaging involved voxel-based morphometry with the statistical parametric mapping software. Cognitive functions were assessed with the WISC-IV. General Linear Models and multiple regressions were conducted controlling age, sex, and maternal education. Compared to groups of children born 30 completed weeks' gestation and later, children born <28 completed weeks' gestation had less gray matter volume (GMV) and white matter volume (WMV) and poorer cognitive functions including decreased full scale IQ, and processing speed. Differences in GMV partially mediated the relationship between GA and full scale IQ in preterm born children. In preterm children who are born AGA and without major complications GA is associated with brain volume and cognitive functions. In particular, decreased brain volume becomes evident in the extremely preterm group (born <28 completed weeks' gestation). In preterm children born 30 completed weeks' gestation and later the relationship of GA with brain volume and cognitive functions may be less strong as previously thought.

Female adolescents with severe substance and conduct problems have substantially less brain gray matter volume.

PubMed

Dalwani, Manish S; McMahon, Mary Agnes; Mikulich-Gilbertson, Susan K; Young, Susan E; Regner, Michael F; Raymond, Kristen M; McWilliams, Shannon K; Banich, Marie T; Tanabe, Jody L; Crowley, Thomas J; Sakai, Joseph T

2015-01-01

Structural neuroimaging studies have demonstrated lower regional gray matter volume in adolescents with severe substance and conduct problems. These research studies, including ours, have generally focused on male-only or mixed-sex samples of adolescents with conduct and/or substance problems. Here we compare gray matter volume between female adolescents with severe substance and conduct problems and female healthy controls of similar ages. Female adolescents with severe substance and conduct problems will show significantly less gray matter volume in frontal regions critical to inhibition (i.e. dorsolateral prefrontal cortex and ventrolateral prefrontal cortex), conflict processing (i.e., anterior cingulate), valuation of expected outcomes (i.e., medial orbitofrontal cortex) and the dopamine reward system (i.e. striatum). We conducted whole-brain voxel-based morphometric comparison of structural MR images of 22 patients (14-18 years) with severe substance and conduct problems and 21 controls of similar age using statistical parametric mapping (SPM) and voxel-based morphometric (VBM8) toolbox. We tested group differences in regional gray matter volume with analyses of covariance, adjusting for age and IQ at p<0.05, corrected for multiple comparisons at whole-brain cluster-level threshold. Female adolescents with severe substance and conduct problems compared to controls showed significantly less gray matter volume in right dorsolateral prefrontal cortex, left ventrolateral prefrontal cortex, medial orbitofrontal cortex, anterior cingulate, bilateral somatosensory cortex, left supramarginal gyrus, and bilateral angular gyrus. Considering the entire brain, patients had 9.5% less overall gray matter volume compared to controls. Female adolescents with severe substance and conduct problems in comparison to similarly aged female healthy controls showed substantially lower gray matter volume in brain regions involved in inhibition, conflict processing, valuation of outcomes, decision-making, reward, risk-taking, and rule-breaking antisocial behavior.

Differences in the neural correlates of frontal lobe tests.

PubMed

Matsuoka, Teruyuki; Kato, Yuka; Imai, Ayu; Fujimoto, Hiroshi; Shibata, Keisuke; Nakamura, Kaeko; Yamada, Kei; Narumoto, Jin

2018-01-01

The Executive Interview (EXIT25), the executive clock-drawing task (CLOX1), and the Frontal Assessment Battery (FAB) are used to assess executive function at the bedside. These tests assess distinct psychometric properties. The aim of this study was to examine differences in the neural correlates of the EXIT25, CLOX1, and FAB based on magnetic resonance imaging. Fifty-eight subjects (30 with Alzheimer's disease, 10 with mild cognitive impairment, and 18 healthy controls) participated in this study. Multiple regression analyses were performed to examine the brain regions correlated with the EXIT25, CLOX1, and FAB scores. Age, gender, and years of education were included as covariates. Statistical thresholds were set to uncorrected P-values of 0.001 at the voxel level and 0.05 at the cluster level. The EXIT25 score correlated inversely with the regional grey matter volume in the left lateral frontal lobe (Brodmann areas 6, 9, 44, and 45). The CLOX1 score correlated positively with the regional grey matter volume in the right orbitofrontal cortex (Brodmann area 11) and the left supramarginal gyrus (Brodmann area 40). The FAB score correlated positively with the regional grey matter volume in the right precentral gyrus (Brodmann area 6). The left lateral frontal lobe (Brodmann area 9) and the right lateral frontal lobe (Brodmann area 46) were identified as common brain regions that showed association with EXIT25, CLOX1, and FAB based only a voxel-level threshold. The results of this study suggest that the EXIT25, CLOX1, and FAB may be associated with the distinct neural correlates of the frontal cortex. © 2018 Japanese Psychogeriatric Society.

Regional gray matter correlates of memory for emotion-laden words in middle-aged and older adults: A voxel-based morphometry study.

PubMed

Saarela, Carina; Joutsa, Juho; Laine, Matti; Parkkola, Riitta; Rinne, Juha O; Karrasch, Mira

2017-01-01

Emotional content is known to enhance memory in a content-dependent manner in healthy populations. In middle-aged and older adults, a reduced preference for negative material, or even an enhanced preference for positive material has been observed. This preference seems to be modulated by the emotional arousal that the material evokes. The neuroanatomical basis for emotional memory processes is, however, not well understood in middle-aged and older healthy people. Previous research on local gray matter correlates of emotional memory in older populations has mainly been conducted with patients suffering from various neurodegenerative diseases. To our knowledge, this is the first study to examine regional gray matter correlates of immediate free recall and recognition memory of intentionally encoded positive, negative, and emotionally neutral words using voxel-based morphometry (VBM) in a sample of 50-to-79-year-old cognitively intact normal adults. The behavioral analyses yielded a positivity bias in recognition memory, but not in immediate free recall. No associations with memory performance emerged from the region-of-interest (ROI) analyses using amygdalar and hippocampal volumes. Controlling for total intracranial volume, age, and gender, the whole-brain VBM analyses showed statistically significant associations between immediate free recall of negative words and volumes in various frontal regions, between immediate free recall of positive words and cerebellar volume, and between recognition memory of positive words and primary visual cortex volume. The findings indicate that the neural areas subserving memory for emotion-laden information encompass posterior brain areas, including the cerebellum, and that memory for emotion-laden information may be driven by cognitive control functions.

Brain structural changes in spasmodic dysphonia: A multimodal magnetic resonance imaging study.

PubMed

Kostic, Vladimir S; Agosta, Federica; Sarro, Lidia; Tomić, Aleksandra; Kresojević, Nikola; Galantucci, Sebastiano; Svetel, Marina; Valsasina, Paola; Filippi, Massimo

2016-04-01

The pathophysiology of spasmodic dysphonia is poorly understood. This study evaluated patterns of cortical morphology, basal ganglia, and white matter microstructural alterations in patients with spasmodic dysphonia relative to healthy controls. T1-weighted and diffusion tensor magnetic resonance imaging (MRI) scans were obtained from 13 spasmodic dysphonia patients and 30 controls. Tract-based spatial statistics was applied to compare diffusion tensor MRI indices (i.e., mean, radial and axial diffusivities, and fractional anisotropy) between groups on a voxel-by-voxel basis. Cortical measures were analyzed using surface-based morphometry. Basal ganglia were segmented on T1-weighted images, and volumes and diffusion tensor MRI metrics of nuclei were measured. Relative to controls, patients with spasmodic dysphonia showed increased cortical surface area of the primary somatosensory cortex bilaterally in a region consistent with the buccal sensory representation, as well as right primary motor cortex, left superior temporal, supramarginal and superior frontal gyri. A decreased cortical area was found in the rolandic operculum bilaterally, left superior/inferior parietal and lingual gyri, as well as in the right angular gyrus. Compared to controls, spasmodic dysphonia patients showed increased diffusivities and decreased fractional anisotropy of the corpus callosum and major white matter tracts, in the right hemisphere. Altered diffusion tensor MRI measures were found in the right caudate and putamen nuclei with no volumetric changes. Multi-level alterations in voice-controlling networks, that included regions devoted not only to sensorimotor integration, motor preparation and motor execution, but also processing of auditory and visual information during speech, might have a role in the pathophysiology of spasmodic dysphonia. Copyright © 2016 Elsevier Ltd. All rights reserved.

Regional gray matter correlates of memory for emotion-laden words in middle-aged and older adults: A voxel-based morphometry study

PubMed Central

Joutsa, Juho; Laine, Matti; Parkkola, Riitta; Rinne, Juha O.; Karrasch, Mira

2017-01-01

Emotional content is known to enhance memory in a content-dependent manner in healthy populations. In middle-aged and older adults, a reduced preference for negative material, or even an enhanced preference for positive material has been observed. This preference seems to be modulated by the emotional arousal that the material evokes. The neuroanatomical basis for emotional memory processes is, however, not well understood in middle-aged and older healthy people. Previous research on local gray matter correlates of emotional memory in older populations has mainly been conducted with patients suffering from various neurodegenerative diseases. To our knowledge, this is the first study to examine regional gray matter correlates of immediate free recall and recognition memory of intentionally encoded positive, negative, and emotionally neutral words using voxel-based morphometry (VBM) in a sample of 50-to-79-year-old cognitively intact normal adults. The behavioral analyses yielded a positivity bias in recognition memory, but not in immediate free recall. No associations with memory performance emerged from the region-of-interest (ROI) analyses using amygdalar and hippocampal volumes. Controlling for total intracranial volume, age, and gender, the whole-brain VBM analyses showed statistically significant associations between immediate free recall of negative words and volumes in various frontal regions, between immediate free recall of positive words and cerebellar volume, and between recognition memory of positive words and primary visual cortex volume. The findings indicate that the neural areas subserving memory for emotion-laden information encompass posterior brain areas, including the cerebellum, and that memory for emotion-laden information may be driven by cognitive control functions. PMID:28771634

Cortical surface-based threshold-free cluster enhancement and cortexwise mediation.

PubMed

Lett, Tristram A; Waller, Lea; Tost, Heike; Veer, Ilya M; Nazeri, Arash; Erk, Susanne; Brandl, Eva J; Charlet, Katrin; Beck, Anne; Vollstädt-Klein, Sabine; Jorde, Anne; Kiefer, Falk; Heinz, Andreas; Meyer-Lindenberg, Andreas; Chakravarty, M Mallar; Walter, Henrik

2017-06-01

Threshold-free cluster enhancement (TFCE) is a sensitive means to incorporate spatial neighborhood information in neuroimaging studies without using arbitrary thresholds. The majority of methods have applied TFCE to voxelwise data. The need to understand the relationship among multiple variables and imaging modalities has become critical. We propose a new method of applying TFCE to vertexwise statistical images as well as cortexwise (either voxel- or vertexwise) mediation analysis. Here we present TFCE_mediation, a toolbox that can be used for cortexwise multiple regression analysis with TFCE, and additionally cortexwise mediation using TFCE. The toolbox is open source and publicly available (https://github.com/trislett/TFCE_mediation). We validated TFCE_mediation in healthy controls from two independent multimodal neuroimaging samples (N = 199 and N = 183). We found a consistent structure-function relationship between surface area and the first independent component (IC1) of the N-back task, that white matter fractional anisotropy is strongly associated with IC1 N-back, and that our voxel-based results are essentially identical to FSL randomise using TFCE (all P FWE <0.05). Using cortexwise mediation, we showed that the relationship between white matter FA and IC1 N-back is mediated by surface area in the right superior frontal cortex (P FWE  < 0.05). We also demonstrated that the same mediation model is present using vertexwise mediation (P FWE  < 0.05). In conclusion, cortexwise analysis with TFCE provides an effective analysis of multimodal neuroimaging data. Furthermore, cortexwise mediation analysis may identify or explain a mechanism that underlies an observed relationship among a predictor, intermediary, and dependent variables in which one of these variables is assessed at a whole-brain scale. Hum Brain Mapp 38:2795-2807, 2017. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

Tumor segmentation on FDG-PET: usefulness of locally connected conditional random fields

NASA Astrophysics Data System (ADS)

Nishio, Mizuho; Kono, Atsushi K.; Koyama, Hisanobu; Nishii, Tatsuya; Sugimura, Kazuro

2015-03-01

This study aimed to develop software for tumor segmentation on 18F-fluorodeoxyglucose (FDG) positron emission tomography (PET). To segment the tumor from the background, we used graph cut, whose segmentation energy was generally divided into two terms: the unary and pairwise terms. Locally connected conditional random fields (LCRF) was proposed for the pairwise term. In LCRF, a three-dimensional cubic window with length L was set for each voxel, and voxels within the window were considered for the pairwise term. To evaluate our method, 64 clinically suspected metastatic bone tumors were tested, which were revealed by FDG-PET. To obtain ground truth, the tumors were manually delineated via consensus of two board-certified radiologists. To compare the LCRF accuracy, other types of segmentation were also applied such as region-growing based on 35%, 40%, and 45% of the tumor maximum standardized uptake value (RG35, RG40, and RG45, respectively), SLIC superpixels (SS), and region-based active contour models (AC). To validate the tumor segmentation accuracy, a dice similarity coefficient (DSC) was calculated between manual segmentation and result of each technique. The DSC difference was tested using the Wilcoxon signed rank test. The mean DSCs of LCRF at L = 3, 5, 7, and 9 were 0.784, 0.801, 0.809, and 0.812, respectively. The mean DSCs of other techniques were RG35, 0.633; RG40, 0.675; RG45, 0.689; SS, 0.709; and AC, 0.758. The DSC differences between LCRF and other techniques were statistically significant (p <0.05). In conclusion, tumor segmentation was more reliably performed with LCRF relative to other techniques.

Influence of chronic nicotine administration on cerebral type 1 cannabinoid receptor binding: an in vivo micro-PET study in the rat using [18F]MK-9470.

PubMed

Gérard, Nathalie; Ceccarini, Jenny; Bormans, Guy; Vanbilloen, Bert; Casteels, Cindy; Goffin, Karolien; Bosier, Barbara; Lambert, Didier M; Van Laere, Koen

2010-10-01

Several lines of evidence suggest a functional interaction between central nicotinic and endocannabinoid systems. Furthermore, type 1 cannabinoid receptor (CB1R) antagonism is evaluated as antismoking therapy, and nicotine usage can be an important confound in positron emission tomography (PET) imaging studies of the CB1R. We evaluated CB1R binding in the rat brain using the PET radioligand [(18)F]MK-9470 after chronic administration of nicotine. Twelve female Wistar rats were scanned at baseline and after chronic administration of either nicotine (1 mg/kg; 2 weeks daily intraperitoneal (IP)) or saline as control. In vivo micro-PET images of CB1R binding were anatomically standardized and analyzed by voxel-based statistical parametric mapping and a predefined volume-of-interest approach. We did not observe changes in [(18)F]MK-9470 binding (p (height) < 0.001 level; uncorrected) on a group basis in either condition. Only at a less stringent threshold of p (height) < 0.005 (uncorrected) was a modest increase observed in tracer binding in the cerebellum for nicotine (peak voxel value + 6.8%, p (cluster) = 0.002 corrected). In conclusion, chronic IP administration of nicotine does not produce major cerebral changes in CB1R binding of [(18)F]MK-9470 in the rat. These results also suggest that chronic nicotine usage is unlikely to interfere with human PET imaging using this radioligand.

Correlation analysis between pulmonary function test parameters and CT image parameters of emphysema

NASA Astrophysics Data System (ADS)

Liu, Cheng-Pei; Li, Chia-Chen; Yu, Chong-Jen; Chang, Yeun-Chung; Wang, Cheng-Yi; Yu, Wen-Kuang; Chen, Chung-Ming

2016-03-01

Conventionally, diagnosis and severity classification of Chronic Obstructive Pulmonary Disease (COPD) are usually based on the pulmonary function tests (PFTs). To reduce the need of PFT for the diagnosis of COPD, this paper proposes a correlation model between the lung CT images and the crucial index of the PFT, FEV1/FVC, a severity index of COPD distinguishing a normal subject from a COPD patient. A new lung CT image index, Mirage Index (MI), has been developed to describe the severity of COPD primarily with emphysema disease. Unlike conventional Pixel Index (PI) which takes into account all voxels with HU values less than -950, the proposed approach modeled these voxels by different sizes of bullae balls and defines MI as a weighted sum of the percentages of the bullae balls of different size classes and locations in a lung. For evaluation of the efficacy of the proposed model, 45 emphysema subjects of different severity were involved in this study. In comparison with the conventional index, PI, the correlation between MI and FEV1/FVC is -0.75+/-0.08, which substantially outperforms the correlation between PI and FEV1/FVC, i.e., -0.63+/-0.11. Moreover, we have shown that the emphysematous lesion areas constituted by small bullae balls are basically irrelevant to FEV1/FVC. The statistical analysis and special case study results show that MI can offer better assessment in different analyses.

Effects of white matter lesions on brain perfusion in patients with mild cognitive impairment.

PubMed

Ishibashi, Masato; Kimura, Noriyuki; Aso, Yasuhiro; Matsubara, Etsuro

2018-05-01

To evaluate the effects of white matter lesions on regional cerebral blood flow in subjects with amnestic mild cognitive impairment. Seventy-five subjects with mild cognitive impairment (36 men and 39 women; mean age, 78.1 years) were included in the study. We used the Mini-Mental State Examination to assess cognitive function. All subjects underwent brain magnetic resonance imaging and 99m Tc ethylcysteinate dimer single photon emission computed tomography. Subjects were stratified based on the presence or absence of white matter lesions on magnetic resonance imaging. Statistical parametric mapping of differences in regional cerebral blood flow between the two groups were assessed by voxel-by-voxel group analysis using SPM8. Of all 75 subjects with mild cognitive impairment, 46 (61.3%) had mild to moderate white matter lesions. The prevalence of hypertension tended to be higher in subjects with white matter lesions than in those without white matter lesions. Mini-Mental State Examination scores were significantly lower in subjects with white matter lesions than in those without white matter lesions. Subjects with white matter lesions had decreased regional cerebral blood flow mainly in the frontal, parietal, and medial temporal lobes, as well as the putamen, compared to those without white matter lesions. In subjects with mild cognitive impairment, white matter lesions were associated with cognitive impairment and mainly frontal lobe brain function. Copyright © 2018 Elsevier B.V. All rights reserved.

Brain metabolism in patients with freezing of gait after hypoxic-ischemic brain injury: A pilot study.

PubMed

Yoon, Seo Yeon; Lee, Sang Chul; Kim, Na Young; An, Young-Sil; Kim, Yong Wook

2017-11-01

Movement disorders are 1 of the long-term neurological complications that can occur after hypoxic-ischemic brain injury (HIBI). However, freezing of gait (FOG) after HIBI is rare. The aim of this study was to examine the brain metabolism of patients with FOG after HIBI using F-18 fluoro-2-deoxy-D-glucose positron emission tomography (F-18 FDG PET).We consecutively enrolled 11 patients with FOG after HIBI. The patients' overall brain metabolism was measured by F-18 FDG PET, and we compared their regional brain metabolic activity with that from 15 healthy controls using a voxel-by-voxel-based statistical mapping analysis. Additionally, we correlated each patient's FOG severity with the brain metabolism using a covariance analysis.Patients with FOG had significantly decreased brain glucose metabolism in the midbrain, bilateral thalamus, bilateral cingulate gyri, right supramarginal gyrus, right angular gyrus, right paracentral lobule, and left precentral gyrus (PFDR-corrected < .01, k = 50). No significant increases in brain metabolism were noted in patients with FOG. The covariance analysis identified significant correlations between the FOG severity and the brain metabolism in the right lingual gyrus, left fusiform gyrus, and bilateral cerebellar crus I (Puncorrected < 0.001, k = 50).Our data suggest that brain regions in the gait-related neural network, including the cerebral cortex, subcortical structures, brainstem, and cerebellum, may significantly contribute to the development of FOG in HIBI. Moreover, the FOG severity may be associated with the visual cortex and cerebellar regions.

The effect of sex and handedness on white matter anisotropy: a diffusion tensor magnetic resonance imaging study.

PubMed

Powell, J L; Parkes, L; Kemp, G J; Sluming, V; Barrick, T R; García-Fiñana, M

2012-04-05

Diffusion tensor magnetic resonance imaging provides a way of assessing the asymmetry of white matter (WM) connectivity, the degree of anisotropic diffusion within a given voxel being a marker of coherently bundled myelinated fibers. Voxel-based statistical analysis was performed on fractional anisotropy (FA) images of 42 right- and 40 left-handers, to assess differences in underlying WM anisotropy and FA asymmetry across the whole brain. Right-handers show greater anisotropy than left-handers in the uncinate fasciculus (UF) within the limbic lobe, and WM underlying prefrontal cortex, medial and inferior frontal gyri. Significantly greater leftward FA asymmetry in cerebellum posterior lobe is seen in left- than right-handers, and males show significantly greater rightward (right-greater-than-left) FA asymmetry in regions of middle occipital lobe, medial temporal gyrus, and a region of the superior longitudinal fasciculus underlying the supramarginal gyrus. Leftward (left-greater-than-right) anisotropy is found in regions of the arcuate fasciculus (AF), UF, and WM underlying pars triangularis in both handedness groups, with right-handers alone showing additional leftward FA asymmetry along the length of the superior temporal gyrus. Overall results indicate that although both handedness groups show anisotropy in similar WM regions, greater anisotropy is observed in right-handers compared with left-handers. The largest differences in FA asymmetry are found between males and females, suggesting a greater effect of sex than handedness on FA asymmetry. Copyright © 2012 IBRO. Published by Elsevier Ltd. All rights reserved.

Voxel-based Gaussian naïve Bayes classification of ischemic stroke lesions in individual T1-weighted MRI scans.

PubMed

Griffis, Joseph C; Allendorfer, Jane B; Szaflarski, Jerzy P

2016-01-15

Manual lesion delineation by an expert is the standard for lesion identification in MRI scans, but it is time-consuming and can introduce subjective bias. Alternative methods often require multi-modal MRI data, user interaction, scans from a control population, and/or arbitrary statistical thresholding. We present an approach for automatically identifying stroke lesions in individual T1-weighted MRI scans using naïve Bayes classification. Probabilistic tissue segmentation and image algebra were used to create feature maps encoding information about missing and abnormal tissue. Leave-one-case-out training and cross-validation was used to obtain out-of-sample predictions for each of 30 cases with left hemisphere stroke lesions. Our method correctly predicted lesion locations for 30/30 un-trained cases. Post-processing with smoothing (8mm FWHM) and cluster-extent thresholding (100 voxels) was found to improve performance. Quantitative evaluations of post-processed out-of-sample predictions on 30 cases revealed high spatial overlap (mean Dice similarity coefficient=0.66) and volume agreement (mean percent volume difference=28.91; Pearson's r=0.97) with manual lesion delineations. Our automated approach agrees with manual tracing. It provides an alternative to automated methods that require multi-modal MRI data, additional control scans, or user interaction to achieve optimal performance. Our fully trained classifier has applications in neuroimaging and clinical contexts. Copyright © 2015 Elsevier B.V. All rights reserved.

Multimodal voxel-based meta-analysis of white matter abnormalities in obsessive-compulsive disorder.

PubMed

Radua, Joaquim; Grau, Mar; van den Heuvel, Odile A; Thiebaut de Schotten, Michel; Stein, Dan J; Canales-Rodríguez, Erick J; Catani, Marco; Mataix-Cols, David

2014-06-01

White matter (WM) abnormalities have long been suspected in obsessive-compulsive disorder (OCD) but the available evidence has been inconsistent. We conducted the first multimodal meta-analysis of WM volume (WMV) and fractional anisotropy (FA) studies in OCD. All voxel-wise studies comparing WMV or FA between patients with OCD and healthy controls in the PubMed, ScienceDirect, Google Scholar, Web of Knowledge and Scopus databases were retrieved. Manual searches were also conducted and authors were contacted soliciting additional data. Thirty-four data sets were identified, of which 22 met inclusion criteria (five of them unpublished; comprising 537 adult and pediatric patients with OCD and 575 matched healthy controls). Whenever possible, raw statistical parametric maps were also obtained from the authors. Peak and raw WMV and FA data were combined using novel multimodal meta-analytic methods implemented in effect-size signed differential mapping. Patients with OCD showed widespread WM abnormalities, but findings were particularly robust in the anterior midline tracts (crossing between anterior parts of cingulum bundle and body of corpus callosum), which showed both increased WMV and decreased FA, possibly suggesting an increase of fiber crossing in these regions. This finding was also observed when the analysis was limited to adult participants, and especially pronounced in samples with a higher proportion of medicated patients. Therefore, patients with OCD may have widespread WM abnormalities, particularly evident in anterior midline tracts, although these changes might be, at least in part, attributable to the effects of therapeutic drugs.

WE-FG-202-09: Voxel-Level Analysis of Adverse Treatment Response in Pediatric Patients Treated for Ependymoma with Passive Scattering Proton Therapy

DOE Office of Scientific and Technical Information (OSTI.GOV)

Peeler, C; The University of Texas Graduate School of Biomedical Sciences at Houston, Houston, TX; Mirkovic, D

2016-06-15

Purpose: We identified patients treated for ependymoma with passive scattering proton therapy who subsequently developed treatment-related imaging changes on MRI. We sought to determine if there is any spatial correlation between imaged response, dose, and LET. Methods: A group of 14 patients treated for ependymoma were identified as having post-treatment MR imaging changes observable as T2-FLAIR hyperintensity with or without enhancement on T1 post-contrast sequences. MR images were registered with treatment planning CT images and regions of treatment-related change contoured by a practicing radiation oncologist. The contoured regions were identified as response with voxels represented as 1 while voxels withinmore » the brain outside of the response region were represented as 0. An in-house Monte Carlo system was used to recalculate treatment plans to obtain dose and LET information. Voxels were binned according to LET values in 0.3 keV µm{sup −1} bins. Dose and corresponding response value (0 or 1) for each voxel for a given LET bin were then plotted and fit with the Lyman-Kutcher-Burman dose response model to determine TD{sub 50} and m parameters for each LET value. Response parameters from all patients were then collated, and linear fits of the data were performed. Results: The response parameters TD50 and m both show trends with LET. Outliers were observed due to low numbers of response voxels in some cases. TD{sub 50} values decreased with LET while m increased with LET. The former result would indicate that for higher LET values, the dose is more effective, which is consistent with relative biological effectiveness (RBE) models for proton therapy. Conclusion: A novel method of voxel-level analysis of image biomarker-based adverse patient treatment response in proton therapy according to dose and LET has been presented. Fitted TD{sub 50} values show a decreasing trend with LET supporting the typical models of proton RBE. Funding provided by NIH Program Project Grant 2U19CA021239-35.« less

MO-F-CAMPUS-I-02: Accuracy in Converting the Average Breast Dose Into the Mean Glandular Dose (MGD) Using the F-Factor in Cone Beam Breast CT- a Monte Carlo Study Using Homogeneous and Quasi-Homogeneous Phantoms

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lai, C; Zhong, Y; Wang, T

2015-06-15

Purpose: To investigate the accuracy in estimating the mean glandular dose (MGD) for homogeneous breast phantoms by converting from the average breast dose using the F-factor in cone beam breast CT. Methods: EGSnrc-based Monte Carlo codes were used to estimate the MGDs. 13-cm in diameter, 10-cm high hemi-ellipsoids were used to simulate pendant-geometry breasts. Two different types of hemi-ellipsoidal models were employed: voxels in quasi-homogeneous phantoms were designed as either adipose or glandular tissue while voxels in homogeneous phantoms were designed as the mixture of adipose and glandular tissues. Breast compositions of 25% and 50% volume glandular fractions (VGFs), definedmore » as the ratio of glandular tissue voxels to entire breast voxels in the quasi-homogeneous phantoms, were studied. These VGFs were converted into glandular fractions by weight and used to construct the corresponding homogeneous phantoms. 80 kVp x-rays with a mean energy of 47 keV was used in the simulation. A total of 109 photons were used to image the phantoms and the energies deposited in the phantom voxels were tallied. Breast doses in homogeneous phantoms were averaged over all voxels and then used to calculate the MGDs using the F-factors evaluated at the mean energy of the x-rays. The MGDs for quasi-homogeneous phantoms were computed directly by averaging the doses over all glandular tissue voxels. The MGDs estimated for the two types of phantoms were normalized to the free-in-air dose at the iso-center and compared. Results: The normalized MGDs were 0.756 and 0.732 mGy/mGy for the 25% and 50% VGF homogeneous breasts and 0.761 and 0.733 mGy/mGy for the corresponding quasi-homogeneous breasts, respectively. The MGDs estimated for the two types of phantoms were similar within 1% in this study. Conclusion: MGDs for homogeneous breast models may be adequately estimated by converting from the average breast dose using the F-factor.« less

Skeletal dosimetry: A hyperboloid representation of the bone-marrow interface to reduce voxel effects in three-dimensional images of trabecular bone

NASA Astrophysics Data System (ADS)

Rajon, Didier Alain

Radiation damage to the hematopoietic bone marrow is clearly defined as the limiting factor to the development of internal emitter therapies. Current dosimetry models rely on chord-length distributions measured through the complex microstructure of the trabecular bone regions of the skeleton in which most of the active marrow is located. Recently, Nuclear Magnetic Resonance (NMR) has been used to obtain high-resolution three-dimensional (3D) images of small trabecular bone samples. These images have been coupled with computer programs to estimate dosimetric parameters such as chord-length distributions, and energy depositions by monoenergetic electrons. This new technique is based on the assumption that each voxel of the image is assigned either to bone tissue or to marrow tissue after application of a threshold value. Previous studies showed that this assumption had important consequences on the outcome of the computer calculations. Both the chord-length distribution measurements and the energy deposition calculations are subject to voxel effects that are responsible for large discrepancies when applied to mathematical models of trabecular bone. The work presented in this dissertation proposes first a quantitative study of the voxel effects. Consensus is that the voxelized representation of surfaces should not be used as direct input to dosimetry computer programs. Instead we need a new technique to transform the interfaces into smooth surfaces. The Marching Cube (MC) algorithm was used and adapted to do this transformation. The initial image was used to generate a continuous gray-level field throughout the image. The interface between bone and marrow was then simulated by the iso-gray-level surface that corresponds to a predetermined threshold value. Calculations were then performed using this new representation. Excellent results were obtained for both the chord-length distribution and the energy deposition measurements. Voxel effects were reduced to an acceptable level and the discrepancies found when using the voxelized representation of the interface were reduced to a few percent. We conclude that this new model should be used every time one performs dosimetry estimates using NMR images of trabecular bone samples.

Structural Differences in Gray Matter between Glider Pilots and Non-Pilots. A Voxel-Based Morphometry Study

PubMed Central

Ahamed, Tosif; Kawanabe, Motoaki; Ishii, Shin; Callan, Daniel E.

2014-01-01

Glider flying is a unique skill that requires pilots to control an aircraft at high speeds in three dimensions and amidst frequent full-body rotations. In the present study, we investigated the neural correlates of flying a glider using voxel-based morphometry. The comparison between gray matter densities of 15 glider pilots and a control group of 15 non-pilots exhibited significant gray matter density increases in left ventral premotor cortex, anterior cingulate cortex, and the supplementary eye field. We posit that the identified regions might be associated with cognitive and motor processes related to flying, such as joystick control, visuo-vestibular interaction, and oculomotor control. PMID:25506339

The neural basis of reversible sentence comprehension: Evidence from voxel-based lesion-symptom mapping in aphasia

PubMed Central

Thothathiri, Malathi; Kimberg, Daniel Y.; Schwartz, Myrna F.

2012-01-01

We explored the neural basis of reversible sentence comprehension in a large group of aphasic patients (N=79). Voxel-based lesion-symptom mapping revealed a significant association between damage in temporoparietal cortex and impaired sentence comprehension. This association remained after we controlled for phonological working memory. We hypothesize that this region plays an important role in the thematic or what-where processing of sentences. In contrast, we detected weak or no association between reversible sentence comprehension and the ventrolateral prefrontal cortex, which includes Broca’s area, even for syntactically complex sentences. This casts doubt on theories that presuppose a critical role for this region in syntactic computations. PMID:21861679

Structural Differences in Gray Matter between Glider Pilots and Non-Pilots. A Voxel-Based Morphometry Study.

PubMed

Ahamed, Tosif; Kawanabe, Motoaki; Ishii, Shin; Callan, Daniel E

2014-01-01

Glider flying is a unique skill that requires pilots to control an aircraft at high speeds in three dimensions and amidst frequent full-body rotations. In the present study, we investigated the neural correlates of flying a glider using voxel-based morphometry. The comparison between gray matter densities of 15 glider pilots and a control group of 15 non-pilots exhibited significant gray matter density increases in left ventral premotor cortex, anterior cingulate cortex, and the supplementary eye field. We posit that the identified regions might be associated with cognitive and motor processes related to flying, such as joystick control, visuo-vestibular interaction, and oculomotor control.

Linguistic Summarization of Video for Fall Detection Using Voxel Person and Fuzzy Logic

PubMed Central

Anderson, Derek; Luke, Robert H.; Keller, James M.; Skubic, Marjorie; Rantz, Marilyn; Aud, Myra

2009-01-01

In this paper, we present a method for recognizing human activity from linguistic summarizations of temporal fuzzy inference curves representing the states of a three-dimensional object called voxel person. A hierarchy of fuzzy logic is used, where the output from each level is summarized and fed into the next level. We present a two level model for fall detection. The first level infers the states of the person at each image. The second level operates on linguistic summarizations of voxel person’s states and inference regarding activity is performed. The rules used for fall detection were designed under the supervision of nurses to ensure that they reflect the manner in which elders perform these activities. The proposed framework is extremely flexible. Rules can be modified, added, or removed, allowing for per-resident customization based on knowledge about their cognitive and physical ability. PMID:20046216

Katja — the 24th week of virtual pregnancy for dosimetric calculations

NASA Astrophysics Data System (ADS)

Becker, Janine; Zankl, Maria; Fill, Ute; Hoeschen, Christoph

2008-01-01

Virtual human models, a.k.a. voxel models, are currently the state of the art in radiation protection for computing organ irradiation doses without difficult or morally unfeasible experiments. They are based on medical image data of human patients and offer a realistic, three dimensional representation of human anatomy. We present our newest voxel model Katja, a virtual woman in the 24th week of pregnancy. Katja integrates two previous voxel models, one obtained from the abdominal MRI scan of a pregnant patient and an already segmented model of a non-pregnant woman. The latter is the ICRP-AF, fitting the reference values for standard height, weight and organ masses given by the Internationals Committee of Radiological Protection (ICRP). The dataset was altered in order to fit the segmented foetus taken from the abdominal MRI scan. The resulting pregnant woman model, Katja, complies with the ICRP reference values for the adult female.

SPARK: Sparsity-based analysis of reliable k-hubness and overlapping network structure in brain functional connectivity.

PubMed

Lee, Kangjoo; Lina, Jean-Marc; Gotman, Jean; Grova, Christophe

2016-07-01

Functional hubs are defined as the specific brain regions with dense connections to other regions in a functional brain network. Among them, connector hubs are of great interests, as they are assumed to promote global and hierarchical communications between functionally specialized networks. Damage to connector hubs may have a more crucial effect on the system than does damage to other hubs. Hubs in graph theory are often identified from a correlation matrix, and classified as connector hubs when the hubs are more connected to regions in other networks than within the networks to which they belong. However, the identification of hubs from functional data is more complex than that from structural data, notably because of the inherent problem of multicollinearity between temporal dynamics within a functional network. In this context, we developed and validated a method to reliably identify connectors and corresponding overlapping network structure from resting-state fMRI. This new method is actually handling the multicollinearity issue, since it does not rely on counting the number of connections from a thresholded correlation matrix. The novelty of the proposed method is that besides counting the number of networks involved in each voxel, it allows us to identify which networks are actually involved in each voxel, using a data-driven sparse general linear model in order to identify brain regions involved in more than one network. Moreover, we added a bootstrap resampling strategy to assess statistically the reproducibility of our results at the single subject level. The unified framework is called SPARK, i.e. SParsity-based Analysis of Reliable k-hubness, where k-hubness denotes the number of networks overlapping in each voxel. The accuracy and robustness of SPARK were evaluated using two dimensional box simulations and realistic simulations that examined detection of artificial hubs generated on real data. Then, test/retest reliability of the method was assessed using the 1000 Functional Connectome Project database, which includes data obtained from 25 healthy subjects at three different occasions with long and short intervals between sessions. We demonstrated that SPARK provides an accurate and reliable estimation of k-hubness, suggesting a promising tool for understanding hub organization in resting-state fMRI. Copyright © 2016 Elsevier Inc. All rights reserved.

Multiparametric [18F]Fluorodeoxyglucose/ [18F]Fluoromisonidazole Positron Emission Tomography/ Magnetic Resonance Imaging of Locally Advanced Cervical Cancer for the Non-Invasive Detection of Tumor Heterogeneity: A Pilot Study

PubMed Central

Andrzejewski, Piotr; Baltzer, Pascal; Polanec, Stephan H.; Sturdza, Alina; Georg, Dietmar; Helbich, Thomas H.; Karanikas, Georgios; Grimm, Christoph; Polterauer, Stephan; Poetter, Richard; Wadsak, Wolfgang; Mitterhauser, Markus; Georg, Petra

2016-01-01

Objectives To investigate fused multiparametric positron emission tomography/magnetic resonance imaging (MP PET/MRI) at 3T in patients with locally advanced cervical cancer, using high-resolution T2-weighted, contrast-enhanced MRI (CE-MRI), diffusion-weighted imaging (DWI), and the radiotracers [18F]fluorodeoxyglucose ([18F]FDG) and [18F]fluoromisonidazol ([18F]FMISO) for the non-invasive detection of tumor heterogeneity for an improved planning of chemo-radiation therapy (CRT). Materials and Methods Sixteen patients with locally advanced cervix were enrolled in this IRB approved and were examined with fused MP [18F]FDG/ [18F]FMISO PET/MRI and in eleven patients complete data sets were acquired. MP PET/MRI was assessed for tumor volume, enhancement (EH)-kinetics, diffusivity, and [18F]FDG/ [18F]FMISO-avidity. Descriptive statistics and voxel-by-voxel analysis of MRI and PET parameters were performed. Correlations were assessed using multiple correlation analysis. Results All tumors displayed imaging parameters concordant with cervix cancer, i.e. type II/III EH-kinetics, restricted diffusivity (median ADC 0.80x10-3mm2/sec), [18F]FDG- (median SUVmax16.2) and [18F]FMISO-avidity (median SUVmax3.1). In all patients, [18F]FMISO PET identified the hypoxic tumor subvolume, which was independent of tumor volume. A voxel-by-voxel analysis revealed only weak correlations between the MRI and PET parameters (0.05–0.22), indicating that each individual parameter yields independent information and the presence of tumor heterogeneity. Conclusion MP [18F]FDG/ [18F]FMISO PET/MRI in patients with cervical cancer facilitates the acquisition of independent predictive and prognostic imaging parameters. MP [18F]FDG/ [18F]FMISO PET/MRI enables insights into tumor biology on multiple levels and provides information on tumor heterogeneity, which has the potential to improve the planning of CRT. PMID:27167829

Multiparametric [18F]Fluorodeoxyglucose/ [18F]Fluoromisonidazole Positron Emission Tomography/ Magnetic Resonance Imaging of Locally Advanced Cervical Cancer for the Non-Invasive Detection of Tumor Heterogeneity: A Pilot Study.

PubMed

Pinker, Katja; Andrzejewski, Piotr; Baltzer, Pascal; Polanec, Stephan H; Sturdza, Alina; Georg, Dietmar; Helbich, Thomas H; Karanikas, Georgios; Grimm, Christoph; Polterauer, Stephan; Poetter, Richard; Wadsak, Wolfgang; Mitterhauser, Markus; Georg, Petra

2016-01-01

To investigate fused multiparametric positron emission tomography/magnetic resonance imaging (MP PET/MRI) at 3T in patients with locally advanced cervical cancer, using high-resolution T2-weighted, contrast-enhanced MRI (CE-MRI), diffusion-weighted imaging (DWI), and the radiotracers [18F]fluorodeoxyglucose ([18F]FDG) and [18F]fluoromisonidazol ([18F]FMISO) for the non-invasive detection of tumor heterogeneity for an improved planning of chemo-radiation therapy (CRT). Sixteen patients with locally advanced cervix were enrolled in this IRB approved and were examined with fused MP [18F]FDG/ [18F]FMISO PET/MRI and in eleven patients complete data sets were acquired. MP PET/MRI was assessed for tumor volume, enhancement (EH)-kinetics, diffusivity, and [18F]FDG/ [18F]FMISO-avidity. Descriptive statistics and voxel-by-voxel analysis of MRI and PET parameters were performed. Correlations were assessed using multiple correlation analysis. All tumors displayed imaging parameters concordant with cervix cancer, i.e. type II/III EH-kinetics, restricted diffusivity (median ADC 0.80x10-3mm2/sec), [18F]FDG- (median SUVmax16.2) and [18F]FMISO-avidity (median SUVmax3.1). In all patients, [18F]FMISO PET identified the hypoxic tumor subvolume, which was independent of tumor volume. A voxel-by-voxel analysis revealed only weak correlations between the MRI and PET parameters (0.05-0.22), indicating that each individual parameter yields independent information and the presence of tumor heterogeneity. MP [18F]FDG/ [18F]FMISO PET/MRI in patients with cervical cancer facilitates the acquisition of independent predictive and prognostic imaging parameters. MP [18F]FDG/ [18F]FMISO PET/MRI enables insights into tumor biology on multiple levels and provides information on tumor heterogeneity, which has the potential to improve the planning of CRT.

Segmentation of human brain using structural MRI.

PubMed

Helms, Gunther

2016-04-01

Segmentation of human brain using structural MRI is a key step of processing in imaging neuroscience. The methods have undergone a rapid development in the past two decades and are now widely available. This non-technical review aims at providing an overview and basic understanding of the most common software. Starting with the basis of structural MRI contrast in brain and imaging protocols, the concepts of voxel-based and surface-based segmentation are discussed. Special emphasis is given to the typical contrast features and morphological constraints of cortical and sub-cortical grey matter. In addition to the use for voxel-based morphometry, basic applications in quantitative MRI, cortical thickness estimations, and atrophy measurements as well as assignment of cortical regions and deep brain nuclei are briefly discussed. Finally, some fields for clinical applications are given.

White matter alterations in narcolepsy patients with cataplexy: tract-based spatial statistics.

PubMed

Park, Yun K; Kwon, Oh-Hun; Joo, Eun Yeon; Kim, Jae-Hun; Lee, Jong M; Kim, Sung T; Hong, Seung B

2016-04-01

Functional imaging studies and voxel-based morphometry analysis of brain magnetic resonance imaging showed abnormalities in the hypothalamus-thalamus-orbitofrontal pathway, demonstrating altered hypocretin pathway in narcolepsy. Those distinct morphometric changes account for problems in wake-sleep control, attention and memory. It also raised the necessity to evaluate white matter changes. To investigate brain white matter alterations in drug-naïve narcolepsy patients with cataplexy and to explore relationships between white matter changes and patient clinical characteristics, drug-naïve narcolepsy patients with cataplexy (n = 22) and healthy age- and gender-matched controls (n = 26) were studied. Fractional anisotropy and mean diffusivity images were obtained from whole-brain diffusion tensor imaging, and tract-based spatial statistics were used to localize white matter abnormalities. Compared with controls, patients showed significant decreases in fractional anisotropy of white matter of the bilateral anterior cingulate, fronto-orbital area, frontal lobe, anterior limb of the internal capsule and corpus callosum, as well as the left anterior and medial thalamus. Patients and controls showed no differences in mean diffusivity. Among patients, mean diffusivity values of white matter in the bilateral superior frontal gyri, bilateral fronto-orbital gyri and right superior parietal gyrus were positively correlated with depressive mood. This tract-based spatial statistics study demonstrated that drug-naïve patients with narcolepsy had reduced fractional anisotropy of white matter in multiple brain areas and significant relationship between increased mean diffusivity of white matter in frontal/cingulate and depression. It suggests the widespread disruption of white matter integrity and prevalent brain degeneration of frontal lobes according to a depressive symptom in narcolepsy. © 2015 European Sleep Research Society.

Assessment of uncertainties in the lung activity measurement of low-energy photon emitters using Monte Carlo simulation of ICRP male thorax voxel phantom.

PubMed

Nadar, M Y; Akar, D K; Rao, D D; Kulkarni, M S; Pradeepkumar, K S

2015-12-01

Assessment of intake due to long-lived actinides by inhalation pathway is carried out by lung monitoring of the radiation workers inside totally shielded steel room using sensitive detection systems such as Phoswich and an array of HPGe detectors. In this paper, uncertainties in the lung activity estimation due to positional errors, chest wall thickness (CWT) and detector background variation are evaluated. First, calibration factors (CFs) of Phoswich and an array of three HPGe detectors are estimated by incorporating ICRP male thorax voxel phantom and detectors in Monte Carlo code 'FLUKA'. CFs are estimated for the uniform source distribution in lungs of the phantom for various photon energies. The variation in the CFs for positional errors of ±0.5, 1 and 1.5 cm in horizontal and vertical direction along the chest are studied. The positional errors are also evaluated by resizing the voxel phantom. Combined uncertainties are estimated at different energies using the uncertainties due to CWT, detector positioning, detector background variation of an uncontaminated adult person and counting statistics in the form of scattering factors (SFs). SFs are found to decrease with increase in energy. With HPGe array, highest SF of 1.84 is found at 18 keV. It reduces to 1.36 at 238 keV. © The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Parietal lobe critically supports successful paired immediate and single-item delayed memory for targets.

PubMed

Krumm, Sabine; Kivisaari, Sasa L; Monsch, Andreas U; Reinhardt, Julia; Ulmer, Stephan; Stippich, Christoph; Kressig, Reto W; Taylor, Kirsten I

2017-05-01

The parietal lobe is important for successful recognition memory, but its role is not yet fully understood. We investigated the parietal lobes' contribution to immediate paired-associate memory and delayed item-recognition memory separately for hits (targets) and correct rejections (distractors). We compared the behavioral performance of 56 patients with known parietal and medial temporal lobe dysfunction (i.e. early Alzheimer's Disease) to 56 healthy control participants in an immediate paired and delayed single item object memory task. Additionally, we performed voxel-based morphometry analyses to investigate the functional-neuroanatomic relationships between performance and voxel-based estimates of atrophy in whole-brain analyses. Behaviorally, all participants performed better identifying targets than rejecting distractors. The voxel-based morphometry analyses associated atrophy in the right ventral parietal cortex with fewer correct responses to familiar items (i.e. hits) in the immediate and delayed conditions. Additionally, medial temporal lobe integrity correlated with better performance in rejecting distractors, but not in identifying targets, in the immediate paired-associate task. Our findings suggest that the parietal lobe critically supports successful immediate and delayed target recognition memory, and that the ventral aspect of the parietal cortex and the medial temporal lobe may have complementary preferences for identifying targets and rejecting distractors, respectively, during recognition memory. Copyright © 2017. Published by Elsevier Inc.

Application of 3D-MR image registration to monitor diseases around the knee joint.

PubMed

Takao, Masaki; Sugano, Nobuhiko; Nishii, Takashi; Miki, Hidenobu; Koyama, Tsuyoshi; Masumoto, Jun; Sato, Yoshinobu; Tamura, Shinichi; Yoshikawa, Hideki

2005-11-01

To estimate the accuracy and consistency of a method using a voxel-based MR image registration algorithm for precise monitoring of knee joint diseases. Rigid body transformation was calculated using a normalized cross-correlation (NCC) algorithm involving simple manual segmentation of the bone region based on its anatomical features. The accuracy of registration was evaluated using four phantoms, followed by a consistency test using MR data from the 11 patients with knee joint disease. The registration accuracy in the phantom experiment was 0.49+/-0.19 mm (SD) for the femur and 0.56+/-0.21 mm (SD) for the tibia. The consistency value in the experiment using clinical data was 0.69+/-0.25 mm (SD) for the femur and 0.77+/-0.37 mm (SD) for the tibia. These values were all smaller than a voxel (1.25 x 1.25 x 1.5 mm). The present method based on an NCC algorithm can be used to register serial MR images of the knee joint with error on the order of a sub-voxel. This method would be useful for precisely assessing therapeutic response and monitoring knee joint diseases; normalized cross-correlation; accuracy. J. Magn. Reson. Imaging 2005. (c) 2005 Wiley-Liss, Inc.

Voxel-based analysis of the immediate early gene, c-jun, in the honey bee brain after a sucrose stimulus.

PubMed

McNeill, M S; Robinson, G E

2015-06-01

Immediate early genes (IEGs) have served as useful markers of brain neuronal activity in mammals, and more recently in insects. The mammalian canonical IEG, c-jun, is part of regulatory pathways conserved in insects and has been shown to be responsive to alarm pheromone in honey bees. We tested whether c-jun was responsive in honey bees to another behaviourally relevant stimulus, sucrose, in order to further identify the brain regions involved in sucrose processing. To identify responsive regions, we developed a new method of voxel-based analysis of c-jun mRNA expression. We found that c-jun is expressed in somata throughout the brain. It was rapidly induced in response to sucrose stimuli, and it responded in somata near the antennal and mechanosensory motor centre, mushroom body calices and lateral protocerebrum, which are known to be involved in sucrose processing. c-jun also responded to sucrose in somata near the lateral suboesophageal ganglion, dorsal optic lobe, ventral optic lobe and dorsal posterior protocerebrum, which had not been previously identified by other methods. These results demonstrate the utility of voxel-based analysis of mRNA expression in the insect brain. © 2015 The Royal Entomological Society.

Wavelet-based segmentation of renal compartments in DCE-MRI of human kidney: initial results in patients and healthy volunteers.

PubMed

Li, Sheng; Zöllner, Frank G; Merrem, Andreas D; Peng, Yinghong; Roervik, Jarle; Lundervold, Arvid; Schad, Lothar R

2012-03-01

Renal diseases can lead to kidney failure that requires life-long dialysis or renal transplantation. Early detection and treatment can prevent progression towards end stage renal disease. MRI has evolved into a standard examination for the assessment of the renal morphology and function. We propose a wavelet-based clustering to group the voxel time courses and thereby, to segment the renal compartments. This approach comprises (1) a nonparametric, discrete wavelet transform of the voxel time course, (2) thresholding of the wavelet coefficients using Stein's Unbiased Risk estimator, and (3) k-means clustering of the wavelet coefficients to segment the kidneys. Our method was applied to 3D dynamic contrast enhanced (DCE-) MRI data sets of human kidney in four healthy volunteers and three patients. On average, the renal cortex in the healthy volunteers could be segmented at 88%, the medulla at 91%, and the pelvis at 98% accuracy. In the patient data, with aberrant voxel time courses, the segmentation was also feasible with good results for the kidney compartments. In conclusion wavelet based clustering of DCE-MRI of kidney is feasible and a valuable tool towards automated perfusion and glomerular filtration rate quantification. Copyright © 2011 Elsevier Ltd. All rights reserved.

Design, fabrication, and implementation of voxel-based 3D printed textured phantoms for task-based image quality assessment in CT

NASA Astrophysics Data System (ADS)

Solomon, Justin; Ba, Alexandre; Diao, Andrew; Lo, Joseph; Bier, Elianna; Bochud, François; Gehm, Michael; Samei, Ehsan

2016-03-01

In x-ray computed tomography (CT), task-based image quality studies are typically performed using uniform background phantoms with low-contrast signals. Such studies may have limited clinical relevancy for modern non-linear CT systems due to possible influence of background texture on image quality. The purpose of this study was to design and implement anatomically informed textured phantoms for task-based assessment of low-contrast detection. Liver volumes were segmented from 23 abdominal CT cases. The volumes were characterized in terms of texture features from gray-level co-occurrence and run-length matrices. Using a 3D clustered lumpy background (CLB) model, a fitting technique based on a genetic optimization algorithm was used to find the CLB parameters that were most reflective of the liver textures, accounting for CT system factors of spatial blurring and noise. With the modeled background texture as a guide, a cylinder phantom (165 mm in diameter and 30 mm height) was designed, containing 20 low-contrast spherical signals (6 mm in diameter at targeted contrast levels of ~3.2, 5.2, 7.2, 10, and 14 HU, 4 repeats per signal). The phantom was voxelized and input into a commercial multi-material 3D printer (Object Connex 350), with custom software for voxel-based printing. Using principles of digital half-toning and dithering, the 3D printer was programmed to distribute two base materials (VeroWhite and TangoPlus, nominal voxel size of 42x84x30 microns) to achieve the targeted spatial distribution of x-ray attenuation properties. The phantom was used for task-based image quality assessment of a clinically available iterative reconstruction algorithm (Sinogram Affirmed Iterative Reconstruction, SAFIRE) using a channelized Hotelling observer paradigm. Images of the textured phantom and a corresponding uniform phantom were acquired at six dose levels and observer model performance was estimated for each condition (5 contrasts x 6 doses x 2 reconstructions x 2 backgrounds = 120 total conditions). Based on the observer model results, the dose reduction potential of SAFIRE was computed and compared between the uniform and textured phantom. The dose reduction potential of SAFIRE was found to be 23% based on the uniform phantom and 17% based on the textured phantom. This discrepancy demonstrates the need to consider background texture when assessing non-linear reconstruction algorithms.

Neuroimaging in epilepsy.

PubMed

Sidhu, Meneka Kaur; Duncan, John S; Sander, Josemir W

2018-05-17

Epilepsy neuroimaging is important for detecting the seizure onset zone, predicting and preventing deficits from surgery and illuminating mechanisms of epileptogenesis. An aspiration is to integrate imaging and genetic biomarkers to enable personalized epilepsy treatments. The ability to detect lesions, particularly focal cortical dysplasia and hippocampal sclerosis, is increased using ultra high-field imaging and postprocessing techniques such as automated volumetry, T2 relaxometry, voxel-based morphometry and surface-based techniques. Statistical analysis of PET and single photon emission computer tomography (STATISCOM) are superior to qualitative analysis alone in identifying focal abnormalities in MRI-negative patients. These methods have also been used to study mechanisms of epileptogenesis and pharmacoresistance.Recent language fMRI studies aim to localize, and also lateralize language functions. Memory fMRI has been recommended to lateralize mnemonic function and predict outcome after surgery in temporal lobe epilepsy. Combinations of structural, functional and post-processing methods have been used in multimodal and machine learning models to improve the identification of the seizure onset zone and increase understanding of mechanisms underlying structural and functional aberrations in epilepsy.

How to identify rectal sub-regions likely involved in rectal bleeding in prostate cancer radiotherapy

NASA Astrophysics Data System (ADS)

Dréan, G.; Acosta, O.; Ospina, J. D.; Voisin, C.; Rigaud, B.; Simon, A.; Haigron, P.; de Crevoisier, R.

2013-11-01

Nowadays, the de nition of patient-speci c constraints in prostate cancer radiotherapy planning are solely based on dose-volume histogram (DVH) parameters. Nevertheless those DVH models lack of spatial accuracy since they do not use the complete 3D information of the dose distribution. The goal of the study was to propose an automatic work ow to de ne patient-speci c rectal sub-regions (RSR) involved in rectal bleeding (RB) in case of prostate cancer radiotherapy. A multi-atlas database spanning the large rectal shape variability was built from a population of 116 individuals. Non-rigid registration followed by voxel-wise statistical analysis on those templates allowed nding RSR likely correlated with RB (from a learning cohort of 63 patients). To de ne patient-speci c RSR, weighted atlas-based segmentation with a vote was then applied to 30 test patients. Results show the potentiality of the method to be used for patient-speci c planning of intensity modulated radiotherapy (IMRT).

Volumetric abnormalities of the brain in a rat model of recurrent headache.

PubMed

Jia, Zhihua; Tang, Wenjing; Zhao, Dengfa; Hu, Guanqun; Li, Ruisheng; Yu, Shengyuan

2018-01-01

Voxel-based morphometry is used to detect structural brain changes in patients with migraine. However, the relevance of migraine and structural changes is not clear. This study investigated structural brain abnormalities based on voxel-based morphometry using a rat model of recurrent headache. The rat model was established by infusing an inflammatory soup through supradural catheters in conscious male rats. Rats were subgrouped according to the frequency and duration of the inflammatory soup infusion. Tactile sensory testing was conducted prior to infusion of the inflammatory soup or saline. The periorbital tactile thresholds in the high-frequency inflammatory soup stimulation group declined persistently from day 5. Increased white matter volume was observed in the rats three weeks after inflammatory soup stimulation, brainstem in the in the low-frequency inflammatory soup-infusion group and cortex in the high-frequency inflammatory soup-infusion group. After six weeks' stimulation, rats showed gray matter volume changes. The brain structural abnormalities recovered after the stimulation was stopped in the low-frequency inflammatory soup-infused rats and persisted even after the high-frequency inflammatory soup stimulus stopped. The changes of voxel-based morphometry in migraineurs may be the result of recurrent headache. Cognition, memory, and learning may play an important role in the chronification of migraines. Reducing migraine attacks has the promise of preventing chronicity of migraine.

Lower Parietal Encoding Activation Is Associated with Sharper Information and Better Memory.

PubMed

Lee, Hongmi; Chun, Marvin M; Kuhl, Brice A

2017-04-01

Mean fMRI activation in ventral posterior parietal cortex (vPPC) during memory encoding often negatively predicts successful remembering. A popular interpretation of this phenomenon is that vPPC reflects "off-task" processing. However, recent fMRI studies considering distributed patterns of activity suggest that vPPC actively represents encoded material. Here, we assessed the relationships between pattern-based content representations in vPPC, mean activation in vPPC, and subsequent remembering. We analyzed data from two fMRI experiments where subjects studied then recalled word-face or word-scene associations. For each encoding trial, we measured 1) mean univariate activation within vPPC and 2) the strength of face/scene information as indexed by pattern analysis. Mean activation in vPPC negatively predicted subsequent remembering, but the strength of pattern-based information in the same vPPC voxels positively predicted later memory. Indeed, univariate amplitude averaged across vPPC voxels negatively correlated with pattern-based information strength. This dissociation reflected a tendency for univariate reductions to maximally occur in voxels that were not strongly tuned for the category of encoded stimuli. These results indicate that vPPC activity patterns reflect the content and quality of memory encoding and constitute a striking example of lower univariate activity corresponding to stronger pattern-based information. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

TU-AB-303-11: Predict Parotids Deformation Applying SIS Epidemiological Model in H&N Adaptive RT

DOE Office of Scientific and Technical Information (OSTI.GOV)

Maffei, N; Guidi, G; University of Bologna, Bologna, Bologna

2015-06-15

Purpose: The aim is to investigate the use of epidemiological models to predict morphological variations in patients undergoing radiation therapy (RT). The susceptible-infected-susceptible (SIS) deterministic model was applied to simulate warping within a focused region of interest (ROI). Hypothesis is to consider each voxel like a single subject of the whole sample and to treat displacement vector fields like an infection. Methods: Using Raystation hybrid deformation algorithms and automatic re-contouring based on mesh grid, we post-processed 360 MVCT images of 12 H&N patients treated with Tomotherapy. Study focused on parotid glands, identified by literature and previous analysis, as ROI moremore » susceptible to warping in H&N region. Susceptible (S) and infectious (I) cases were identified in voxels with inter-fraction movement respectively under and over a set threshold. IronPython scripting allowed to export positions and displacement data of surface voxels for every fraction. A MATLAB homemade toolbox was developed to model the SIS. Results: SIS model was validated simulating organ motion on QUASAR phantom. Applying model in patients, within a [0–1cm] range, a single voxel movement of 0.4cm was selected as displacement threshold. SIS indexes were evaluated by MATLAB simulations. Dynamic time warping algorithm was used to assess matching between model and parotids behavior days of treatments. The best fit of the model was obtained with contact rate of 7.89±0.94 and recovery rate of 2.36±0.21. Conclusion: SIS model can follow daily structures evolutions, making possible to compare warping conditions and highlighting challenges due to abnormal variation and set-up errors. By epidemiology approach, organ motion could be assessed and predicted not in terms of average of the whole ROI, but in a voxel-by-voxel deterministic trend. Identifying anatomical region subjected to variations, would be possible to focus clinic controls within a cohort of pre-selected patients eligible for adaptive RT. The research is partially co-funded by the Italian Research Grant: Dose warping methods for IGRT and Adaptive RT: dose accumulation based on organ motion and anatomical variations of the patients during radiation therapy treatments,MoH (GR-2010-2318757) and Tecnologie Avanzate S.r.l.(Italy)« less

The brain in myotonic dystrophy 1 and 2: evidence for a predominant white matter disease

PubMed Central

Weber, Bernd; Schoene-Bake, Jan-Christoph; Roeske, Sandra; Mirbach, Sandra; Anspach, Christian; Schneider-Gold, Christiane; Betz, Regina C.; Helmstaedter, Christoph; Tittgemeyer, Marc; Klockgether, Thomas; Kornblum, Cornelia

2011-01-01

Myotonic dystrophy types 1 and 2 are progressive multisystemic disorders with potential brain involvement. We compared 22 myotonic dystrophy type 1 and 22 myotonic dystrophy type 2 clinically and neuropsychologically well-characterized patients and a corresponding healthy control group using structural brain magnetic resonance imaging at 3 T (T1/T2/diffusion-weighted). Voxel-based morphometry and diffusion tensor imaging with tract-based spatial statistics were applied for voxel-wise analysis of cerebral grey and white matter affection (Pcorrected < 0.05). We further examined the association of structural brain changes with clinical and neuropsychological data. White matter lesions rated visually were more prevalent and severe in myotonic dystrophy type 1 compared with controls, with frontal white matter most prominently affected in both disorders, and temporal lesions restricted to myotonic dystrophy type 1. Voxel-based morphometry analyses demonstrated extensive white matter involvement in all cerebral lobes, brainstem and corpus callosum in myotonic dystrophy types 1 and 2, while grey matter decrease (cortical areas, thalamus, putamen) was restricted to myotonic dystrophy type 1. Accordingly, we found more prominent white matter affection in myotonic dystrophy type 1 than myotonic dystrophy type 2 by diffusion tensor imaging. Association fibres throughout the whole brain, limbic system fibre tracts, the callosal body and projection fibres (e.g. internal/external capsules) were affected in myotonic dystrophy types 1 and 2. Central motor pathways were exclusively impaired in myotonic dystrophy type 1. We found mild executive and attentional deficits in our patients when neuropsychological tests were corrected for manual motor dysfunctioning. Regression analyses revealed associations of white matter affection with several clinical parameters in both disease entities, but not with neuropsychological performance. We showed that depressed mood and fatigue were more prominent in patients with myotonic dystrophy type 1 with less white matter affection (early disease stages), contrary to patients with myotonic dystrophy type 2. Thus, depression in myotonic dystrophies might be a reactive adjustment disorder rather than a direct consequence of structural brain damage. Associations of white matter affection with age/disease duration as well as patterns of cerebral water diffusion parameters pointed towards an ongoing process of myelin destruction and/or axonal loss in our cross-sectional study design. Our data suggest that both myotonic dystrophy types 1 and 2 are serious white matter diseases with prominent callosal body and limbic system affection. White matter changes dominated the extent of grey matter changes, which might argue against Wallerian degeneration as the major cause of white matter affection in myotonic dystrophies. PMID:22131273

A method to estimate the effect of deformable image registration uncertainties on daily dose mapping

PubMed Central

Murphy, Martin J.; Salguero, Francisco J.; Siebers, Jeffrey V.; Staub, David; Vaman, Constantin

2012-01-01

Purpose: To develop a statistical sampling procedure for spatially-correlated uncertainties in deformable image registration and then use it to demonstrate their effect on daily dose mapping. Methods: Sequential daily CT studies are acquired to map anatomical variations prior to fractionated external beam radiotherapy. The CTs are deformably registered to the planning CT to obtain displacement vector fields (DVFs). The DVFs are used to accumulate the dose delivered each day onto the planning CT. Each DVF has spatially-correlated uncertainties associated with it. Principal components analysis (PCA) is applied to measured DVF error maps to produce decorrelated principal component modes of the errors. The modes are sampled independently and reconstructed to produce synthetic registration error maps. The synthetic error maps are convolved with dose mapped via deformable registration to model the resulting uncertainty in the dose mapping. The results are compared to the dose mapping uncertainty that would result from uncorrelated DVF errors that vary randomly from voxel to voxel. Results: The error sampling method is shown to produce synthetic DVF error maps that are statistically indistinguishable from the observed error maps. Spatially-correlated DVF uncertainties modeled by our procedure produce patterns of dose mapping error that are different from that due to randomly distributed uncertainties. Conclusions: Deformable image registration uncertainties have complex spatial distributions. The authors have developed and tested a method to decorrelate the spatial uncertainties and make statistical samples of highly correlated error maps. The sample error maps can be used to investigate the effect of DVF uncertainties on daily dose mapping via deformable image registration. An initial demonstration of this methodology shows that dose mapping uncertainties can be sensitive to spatial patterns in the DVF uncertainties. PMID:22320766

New Fetal Dose Estimates from 18F-FDG Administered During Pregnancy: Standardization of Dose Calculations and Estimations with Voxel-Based Anthropomorphic Phantoms.

PubMed

Zanotti-Fregonara, Paolo; Chastan, Mathieu; Edet-Sanson, Agathe; Ekmekcioglu, Ozgul; Erdogan, Ezgi Basak; Hapdey, Sebastien; Hindie, Elif; Stabin, Michael G

2016-11-01

Data from the literature show that the fetal absorbed dose from 18 F-FDG administration to the pregnant mother ranges from 0.5E-2 to 4E-2 mGy/MBq. These figures were, however, obtained using different quantification techniques and with basic geometric anthropomorphic phantoms. The aim of this study was to refine the fetal dose estimates of published as well as new cases using realistic voxel-based phantoms. The 18 F-FDG doses to the fetus (n = 19; 5-34 wk of pregnancy) were calculated with new voxel-based anthropomorphic phantoms of the pregnant woman. The image-derived fetal time-integrated activity values were combined with those of the mothers' organs from the International Commission on Radiological Protection publication 106 and the dynamic bladder model with a 1-h bladder-voiding interval. The dose to the uterus was used as a proxy for early pregnancy (up to 10 wk). The time-integrated activities were entered into OLINDA/EXM 1.1 to derive the dose with the classic anthropomorphic phantoms of pregnant women, then into OLINDA/EXM 2.0 to assess the dose using new voxel-based phantoms. The average fetal doses (mGy/MBq) with OLINDA/EXM 2.0 were 2.5E-02 in early pregnancy, 1.3E-02 in the late part of the first trimester, 8.5E-03 in the second trimester, and 5.1E-03 in the third trimester. The differences compared with the doses calculated with OLINDA/EXM 1.1 were +7%, +70%, +35%, and -8%, respectively. Except in late pregnancy, the doses estimated with realistic voxelwise anthropomorphic phantoms are higher than the doses derived from old geometric phantoms. The doses remain, however, well below the threshold for any deterministic effects. Thus, pregnancy is not an absolute contraindication of a clinically justified 18 F-FDG PET scan. © 2016 by the Society of Nuclear Medicine and Molecular Imaging, Inc.

TU-H-CAMPUS-JeP3-02: Automated Dose Accumulation and Dose Accuracy Assessment for Online Or Offline Adaptive Replanning

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chen, G; Ahunbay, E; Li, X

Purpose: With introduction of high-quality treatment imaging during radiation therapy (RT) delivery, e.g., MR-Linac, adaptive replanning of either online or offline becomes appealing. Dose accumulation of delivered fractions, a prerequisite for the adaptive replanning, can be cumbersome and inaccurate. The purpose of this work is to develop an automated process to accumulate daily doses and to assess the dose accumulation accuracy voxel-by-voxel for adaptive replanning. Methods: The process includes the following main steps: 1) reconstructing daily dose for each delivered fraction with a treatment planning system (Monaco, Elekta) based on the daily images using machine delivery log file and consideringmore » patient repositioning if applicable, 2) overlaying the daily dose to the planning image based on deformable image registering (DIR) (ADMIRE, Elekta), 3) assessing voxel dose deformation accuracy based on deformation field using predetermined criteria, and 4) outputting accumulated dose and dose-accuracy volume histograms and parameters. Daily CTs acquired using a CT-on-rails during routine CT-guided RT for sample patients with head and neck and prostate cancers were used to test the process. Results: Daily and accumulated doses (dose-volume histograms, etc) along with their accuracies (dose-accuracy volume histogram) can be robustly generated using the proposed process. The test data for a head and neck cancer case shows that the gross tumor volume decreased by 20% towards the end of treatment course, and the parotid gland mean dose increased by 10%. Such information would trigger adaptive replanning for the subsequent fractions. The voxel-based accuracy in the accumulated dose showed that errors in accumulated dose near rigid structures were small. Conclusion: A procedure as well as necessary tools to automatically accumulate daily dose and assess dose accumulation accuracy is developed and is useful for adaptive replanning. Partially supported by Elekta, Inc.« less

A prostate MRI atlas of biochemical failures following cancer treatment

NASA Astrophysics Data System (ADS)

Rusu, Mirabela; Kurhanewicz, John; Tewari, Ashutosh; Madabhushi, Anant

2014-03-01

Radical prostatectomy (RP) and radiation therapy (RT) are the most common treatment options for prostate cancer (PCa). Despite advancements in radiation delivery and surgical procedures, RP and RT can result in failure rates as high as 40% and >25%, respectively. Treatment failure is characterized by biochemical recurrence (BcR), which is defined in terms of prostate specific antigen (PSA) concentrations and its variation following treatment. PSA is expected to decrease following treatment, thereby its presence in even small concentrations (e.g 0.2 ng/ml for surgery or 2 ng/ml over the nadir PSA for radiation therapy) is indicative of treatment failure. Early identification of treatment failure may enable the use of more aggressive or neo-adjuvant therapies. Moreover, predicting failure prior to treatment may spare the patient from a procedure that is unlikely to be successful. Our goal is to identify differences on pre-treatment MRI between patients who have BcR and those who remain disease-free at 5 years post-treatment. Specifically, we focus on (1) identifying statistically significant differences in MRI intensities, (2) establishing morphological differences in prostatic anatomic structures, and (3) comparing these differences with the natural variability of prostatic structures. In order to attain these objectives, we use an anatomically constrained registration framework to construct BcR and non-BcR statistical atlases based on the pre-treatment magnetic resonance images (MRI) of the prostate. The patients included in the atlas either underwent RP or RT and were followed up for at least 5 years. The BcR atlas was constructed from a combined population of 12 pre-RT 1.5 Tesla (T) MRI and 33 pre-RP 3T MRI from patients with BcR within 5 years of treatment. Similarly, the non-BcR atlas was built based on a combined cohort of 20 pre-RT 1.5T MRI and 41 pre-RP 3T MRI from patients who remain disease-free 5 years post treatment. We chose the atlas framework as it enables the mapping of MR images from all subjects into the same canonical space, while constructing both an imaging and a morphological statistical atlas. Such co-registration allowed us to perform voxel-by-voxel comparisons of MRI intensities and capsule and central gland morphology to identify statistically significant differences between the BcR and non-BcR patient populations. To assess whether the morphological differences are valid, we performed an additional experiment where we constructed sub-population atlases by randomly sampling RT patients to construct the BcR and non-BcR atlases. Following these experiments we observed that: (1) statistically significant MRI intensity differences exist between BcR and non-BcR patients, specifically on the border of the central gland; (2) statistically significant morphological differences are visible in the prostate and central gland, specifically in the proximity of the apex, and (3) the differences between the BcR and non-BcR cohorts in terms of shape appeared to be consistent across these sub-population atlases as observed in our RT atlases.

An analytical method for computing voxel S values for electrons and photons.

PubMed

Amato, Ernesto; Minutoli, Fabio; Pacilio, Massimiliano; Campenni, Alfredo; Baldari, Sergio

2012-11-01

The use of voxel S values (VSVs) is perhaps the most common approach to radiation dosimetry for nonuniform distributions of activity within organs or tumors. However, VSVs are currently available only for a limited number of voxel sizes and radionuclides. The objective of this study was to develop a general method to evaluate them for any spectrum of electrons and photons in any cubic voxel dimension of practical interest for clinical dosimetry in targeted radionuclide therapy. The authors developed a Monte Carlo simulation in Geant4 in order to evaluate the energy deposited per disintegration (E(dep)) in a voxelized region of soft tissue from monoenergetic electrons (10-2000 keV) or photons (10-1000 keV) homogeneously distributed in the central voxel, considering voxel dimensions ranging from 3 mm to 10 mm. E(dep) was represented as a function of a dimensionless quantity termed the "normalized radius," R(n) = R∕l, where l is the voxel size and R is the distance from the origin. The authors introduced two parametric functions in order to fit the electron and photon results, and they interpolated the parameters to derive VSVs for any energy and voxel side within the ranges mentioned above. In order to validate the results, the authors determined VSV for two radionuclides ((131)I and (89)Sr) and two voxel dimensions and they compared them with reference data. A validation study in a simple sphere model, accounting for tissue inhomogeneities, is presented. The E(dep)(R(n)) for both monoenergetic electrons and photons exhibit a smooth variation with energy and voxel size, implying that VSVs for monoenergetic electrons or photons may be derived by interpolation over the range of energies and dimensions considered. By integration, S values for continuous emission spectra from β(-) decay may be derived as well. The approach allows the determination of VSVs for monoenergetic (Auger or conversion) electrons and (x-ray or gamma-ray) photons by means of two functions whose parameters can be interpolated from tabular data provided. Through integration, it is possible to generalize the method to any continuous (beta) spectrum, allowing to calculate VSVs for any electron and photon emitter in a voxelized structure.

Automated modification and fusion of voxel models to construct body phantoms with heterogeneous breast tissue: Application to MRI simulations.

PubMed

Rispoli, Joseph V; Wright, Steven M; Malloy, Craig R; McDougall, Mary P

2017-01-01

Human voxel models incorporating detailed anatomical features are vital tools for the computational evaluation of electromagnetic (EM) fields within the body. Besides whole-body human voxel models, phantoms representing smaller heterogeneous anatomical features are often employed; for example, localized breast voxel models incorporating fatty and fibroglandular tissues have been developed for a variety of EM applications including mammography simulation and dosimetry, magnetic resonance imaging (MRI), and ultra-wideband microwave imaging. However, considering wavelength effects, electromagnetic modeling of the breast at sub-microwave frequencies necessitates detailed breast phantoms in conjunction with whole-body voxel models. Heterogeneous breast phantoms are sized to fit within radiofrequency coil hardware, modified by voxel-wise extrusion, and fused to whole-body models using voxel-wise, tissue-dependent logical operators. To illustrate the utility of this method, finite-difference time-domain simulations are performed using a whole-body model integrated with a variety of available breast phantoms spanning the standard four tissue density classifications representing the majority of the population. The software library uses a combination of voxel operations to seamlessly size, modify, and fuse eleven breast phantoms to whole-body voxel models. The software is publicly available on GitHub and is linked to the file exchange at MATLAB ® Central. Simulations confirm the proportions of fatty and fibroglandular tissues in breast phantoms have significant yet predictable implications on projected power deposition in tissue. Breast phantoms may be modified and fused to whole-body voxel models using the software presented in this work; user considerations for the open-source software and resultant phantoms are discussed. Furthermore, results indicate simulating breast models as predominantly fatty tissue can considerably underestimate the potential for tissue heating in women with substantial fibroglandular tissue.

Automated modification and fusion of voxel models to construct body phantoms with heterogeneous breast tissue: Application to MRI simulations

PubMed Central

Rispoli, Joseph V.; Wright, Steven M.; Malloy, Craig R.; McDougall, Mary P.

2017-01-01

Background Human voxel models incorporating detailed anatomical features are vital tools for the computational evaluation of electromagnetic (EM) fields within the body. Besides whole-body human voxel models, phantoms representing smaller heterogeneous anatomical features are often employed; for example, localized breast voxel models incorporating fatty and fibroglandular tissues have been developed for a variety of EM applications including mammography simulation and dosimetry, magnetic resonance imaging (MRI), and ultra-wideband microwave imaging. However, considering wavelength effects, electromagnetic modeling of the breast at sub-microwave frequencies necessitates detailed breast phantoms in conjunction with whole-body voxel models. Methods Heterogeneous breast phantoms are sized to fit within radiofrequency coil hardware, modified by voxel-wise extrusion, and fused to whole-body models using voxel-wise, tissue-dependent logical operators. To illustrate the utility of this method, finite-difference time-domain simulations are performed using a whole-body model integrated with a variety of available breast phantoms spanning the standard four tissue density classifications representing the majority of the population. Results The software library uses a combination of voxel operations to seamlessly size, modify, and fuse eleven breast phantoms to whole-body voxel models. The software is publicly available on GitHub and is linked to the file exchange at MATLAB® Central. Simulations confirm the proportions of fatty and fibroglandular tissues in breast phantoms have significant yet predictable implications on projected power deposition in tissue. Conclusions Breast phantoms may be modified and fused to whole-body voxel models using the software presented in this work; user considerations for the open-source software and resultant phantoms are discussed. Furthermore, results indicate simulating breast models as predominantly fatty tissue can considerably underestimate the potential for tissue heating in women with substantial fibroglandular tissue. PMID:28798837

A statistical approach for segregating cognitive task stages from multivariate fMRI BOLD time series.

PubMed

Demanuele, Charmaine; Bähner, Florian; Plichta, Michael M; Kirsch, Peter; Tost, Heike; Meyer-Lindenberg, Andreas; Durstewitz, Daniel

2015-01-01

Multivariate pattern analysis can reveal new information from neuroimaging data to illuminate human cognition and its disturbances. Here, we develop a methodological approach, based on multivariate statistical/machine learning and time series analysis, to discern cognitive processing stages from functional magnetic resonance imaging (fMRI) blood oxygenation level dependent (BOLD) time series. We apply this method to data recorded from a group of healthy adults whilst performing a virtual reality version of the delayed win-shift radial arm maze (RAM) task. This task has been frequently used to study working memory and decision making in rodents. Using linear classifiers and multivariate test statistics in conjunction with time series bootstraps, we show that different cognitive stages of the task, as defined by the experimenter, namely, the encoding/retrieval, choice, reward and delay stages, can be statistically discriminated from the BOLD time series in brain areas relevant for decision making and working memory. Discrimination of these task stages was significantly reduced during poor behavioral performance in dorsolateral prefrontal cortex (DLPFC), but not in the primary visual cortex (V1). Experimenter-defined dissection of time series into class labels based on task structure was confirmed by an unsupervised, bottom-up approach based on Hidden Markov Models. Furthermore, we show that different groupings of recorded time points into cognitive event classes can be used to test hypotheses about the specific cognitive role of a given brain region during task execution. We found that whilst the DLPFC strongly differentiated between task stages associated with different memory loads, but not between different visual-spatial aspects, the reverse was true for V1. Our methodology illustrates how different aspects of cognitive information processing during one and the same task can be separated and attributed to specific brain regions based on information contained in multivariate patterns of voxel activity.

TH-A-9A-04: Incorporating Liver Functionality in Radiation Therapy Treatment Planning

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wu, V; Epelman, M; Feng, M

2014-06-15

Purpose: Liver SBRT patients have both variable pretreatment liver function (e.g., due to degree of cirrhosis and/or prior treatments) and sensitivity to radiation, leading to high variability in potential liver toxicity with similar doses. This work aims to explicitly incorporate liver perfusion into treatment planning to redistribute dose to preserve well-functioning areas without compromising target coverage. Methods: Voxel-based liver perfusion, a measure of functionality, was computed from dynamic contrast-enhanced MRI. Two optimization models with different cost functions subject to the same dose constraints (e.g., minimum target EUD and maximum critical structure EUDs) were compared. The cost functions minimized were EUDmore » (standard model) and functionality-weighted EUD (functional model) to the liver. The resulting treatment plans delivering the same target EUD were compared with respect to their DVHs, their dose wash difference, the average dose delivered to voxels of a particular perfusion level, and change in number of high-/low-functioning voxels receiving a particular dose. Two-dimensional synthetic and three-dimensional clinical examples were studied. Results: The DVHs of all structures of plans from each model were comparable. In contrast, in plans obtained with the functional model, the average dose delivered to high-/low-functioning voxels was lower/higher than in plans obtained with its standard counterpart. The number of high-/low-functioning voxels receiving high/low dose was lower in the plans that considered perfusion in the cost function than in the plans that did not. Redistribution of dose can be observed in the dose wash differences. Conclusion: Liver perfusion can be used during treatment planning potentially to minimize the risk of toxicity during liver SBRT, resulting in better global liver function. The functional model redistributes dose in the standard model from higher to lower functioning voxels, while achieving the same target EUD and satisfying dose limits to critical structures. This project is funded by MCubed and grant R01-CA132834.« less

Why prostate tumour delineation based on apparent diffusion coefficient is challenging: an exploration of the tissue microanatomy.

PubMed

Borren, Alie; Moman, Maaike R; Groenendaal, Greetje; Boeken Kruger, Arto E; van Diest, Paul J; van der Groep, Petra; van der Heide, Uulke A; van Vulpen, Marco; Philippens, Marielle E P

2013-11-01

Focal boosting of prostate tumours to improve outcome, requires accurate tumour delineation. For this, the apparent diffusion coefficient (ADC) derived from diffusion-weighted MR imaging (DWI) seems a useful tool. On voxel level, the relationship between ADC and histological presence of tumour is, however, ambiguous. Therefore, in this study the relationship between ADC and histological variables was investigated on voxel level to understand the strengths and limitations of DWI for prostate tumour delineation. Twelve radical prostatectomy patients underwent a pre-operative 3.0T DWI exam and the ADC was calculated. From whole-mount histological sections cell density and glandular area were retrieved for every voxel. The distribution of all variables was described for tumour, peripheral zone (PZ) and central gland (CG) on regional and voxel level. Correlations between variables and differences between regions were calculated. Large heterogeneity of ADC on voxel level was observed within tumours, between tumours and between patients. This heterogeneity was reflected by the distribution of cell density and glandular area. On regional level, tumour was different from PZ having higher cell density (p = 0.007), less glandular area (p = 0.017) and lower ADCs (p = 0.017). ADC was correlated with glandular area (r = 0.402) and tumour volume (r = -0.608), but not with Gleason score. ADC tended to decrease with increasing cell density (r = -0.327, p = 0.073). On voxel level, correlations between ADC and histological variables varied among patients, for cell density ranging from r = -0.439 to r = 0.261 and for glandular area from r = 0.593 to r = 0.207. The variation in ADC can to a certain extent be explained by the variation in cell density and glandular area. The ADC is highly heterogeneous, which reflects the heterogeneity of malignant and benign prostate tissue. This heterogeneity might however obscure small tumours or parts of tumours. Therefore, DWI has to be used in the context of multiparametric MRI.

Detection of white matter lesions in cerebral small vessel disease

NASA Astrophysics Data System (ADS)

Riad, Medhat M.; Platel, Bram; de Leeuw, Frank-Erik; Karssemeijer, Nico

2013-02-01

White matter lesions (WML) are diffuse white matter abnormalities commonly found in older subjects and are important indicators of stroke, multiple sclerosis, dementia and other disorders. We present an automated WML detection method and evaluate it on a dataset of small vessel disease (SVD) patients. In early SVD, small WMLs are expected to be of importance for the prediction of disease progression. Commonly used WML segmentation methods tend to ignore small WMLs and are mostly validated on the basis of total lesion load or a Dice coefficient for all detected WMLs. Therefore, in this paper, we present a method that is designed to detect individual lesions, large or small, and we validate the detection performance of our system with FROC (free-response ROC) analysis. For the automated detection, we use supervised classification making use of multimodal voxel based features from different magnetic resonance imaging (MRI) sequences, including intensities, tissue probabilities, voxel locations and distances, neighborhood textures and others. After preprocessing, including co-registration, brain extraction, bias correction, intensity normalization, and nonlinear registration, ventricle segmentation is performed and features are calculated for each brain voxel. A gentle-boost classifier is trained using these features from 50 manually annotated subjects to give each voxel a probability of being a lesion voxel. We perform ROC analysis to illustrate the benefits of using additional features to the commonly used voxel intensities; significantly increasing the area under the curve (Az) from 0.81 to 0.96 (p<0.05). We perform the FROC analysis by testing our classifier on 50 previously unseen subjects and compare the results with manual annotations performed by two experts. Using the first annotator results as our reference, the second annotator performs at a sensitivity of 0.90 with an average of 41 false positives per subject while our automated method reached the same level of sensitivity at approximately 180 false positives per subject.

FASH and MASH: female and male adult human phantoms based on polygon mesh surfaces: II. Dosimetric calculations

NASA Astrophysics Data System (ADS)

Kramer, R.; Cassola, V. F.; Khoury, H. J.; Vieira, J. W.; de Melo Lima, V. J.; Robson Brown, K.

2010-01-01

Female and male adult human phantoms, called FASH (Female Adult meSH) and MASH (Male Adult meSH), have been developed in the first part of this study using 3D animation software and anatomical atlases to replace the image-based FAX06 and the MAX06 voxel phantoms. 3D modelling methods allow for phantom development independent from medical images of patients, volunteers or cadavers. The second part of this study investigates the dosimetric implications for organ and tissue equivalent doses due to the anatomical differences between the new and the old phantoms. These differences are mainly caused by the supine position of human bodies during scanning in order to acquire digital images for voxel phantom development. Compared to an upright standing person, in image-based voxel phantoms organs are often coronally shifted towards the head and sometimes the sagittal diameter of the trunk is reduced by a gravitational change of the fat distribution. In addition, volumes of adipose and muscle tissue shielding internal organs are sometimes too small, because adaptation of organ volumes to ICRP-based organ masses often occurs at the expense of general soft tissues, such as adipose, muscle or unspecified soft tissue. These effects have dosimetric consequences, especially for partial body exposure, such as in x-ray diagnosis, but also for whole body external exposure and for internal exposure. Using the EGSnrc Monte Carlo code, internal and external exposure to photons and electrons has been simulated with both pairs of phantoms. The results show differences between organ and tissue equivalent doses for the upright standing FASH/MASH and the image-based supine FAX06/MAX06 phantoms of up to 80% for external exposure and up to 100% for internal exposure. Similar differences were found for external exposure between FASH/MASH and REGINA/REX, the reference voxel phantoms of the International Commission on Radiological Protection. Comparison of effective doses for external photon exposure showed good agreement between FASH/MASH and REGINA/REX, but large differences between FASH/MASH and the mesh-based RPI_AM and the RPI_AF phantoms, developed at the Rensselaer Polytechnic Institute (RPI).

3D change detection in staggered voxels model for robotic sensing and navigation

NASA Astrophysics Data System (ADS)

Liu, Ruixu; Hampshire, Brandon; Asari, Vijayan K.

2016-05-01

3D scene change detection is a challenging problem in robotic sensing and navigation. There are several unpredictable aspects in performing scene change detection. A change detection method which can support various applications in varying environmental conditions is proposed. Point cloud models are acquired from a RGB-D sensor, which provides the required color and depth information. Change detection is performed on robot view point cloud model. A bilateral filter smooths the surface and fills the holes as well as keeps the edge details on depth image. Registration of the point cloud model is implemented by using Random Sample Consensus (RANSAC) algorithm. It uses surface normal as the previous stage for the ground and wall estimate. After preprocessing the data, we create a point voxel model which defines voxel as surface or free space. Then we create a color model which defines each voxel that has a color by the mean of all points' color value in this voxel. The preliminary change detection is detected by XOR subtract on the point voxel model. Next, the eight neighbors for this center voxel are defined. If they are neither all `changed' voxels nor all `no changed' voxels, a histogram of location and hue channel color is estimated. The experimental evaluations performed to evaluate the capability of our algorithm show promising results for novel change detection that indicate all the changing objects with very limited false alarm rate.

Use of spectral analysis with iterative filter for voxelwise determination of regional rates of cerebral protein synthesis with L-[1-11C]leucine PET.

PubMed

Veronese, Mattia; Schmidt, Kathleen C; Smith, Carolyn Beebe; Bertoldo, Alessandra

2012-06-01

A spectral analysis approach was used to estimate kinetic parameters of the L-[1-(11)C]leucine positron emission tomography (PET) method and regional rates of cerebral protein synthesis (rCPS) on a voxel-by-voxel basis. Spectral analysis applies to both heterogeneous and homogeneous tissues; it does not require prior assumptions concerning number of tissue compartments. Parameters estimated with spectral analysis can be strongly affected by noise, but numerical filters improve estimation performance. Spectral analysis with iterative filter (SAIF) was originally developed to improve estimation of leucine kinetic parameters and rCPS in region-of-interest (ROI) data analyses. In the present study, we optimized SAIF for application at the voxel level. In measured L-[1-(11)C]leucine PET data, voxel-level SAIF parameter estimates averaged over all voxels within a ROI (mean voxel-SAIF) generally agreed well with corresponding estimates derived by applying the originally developed SAIF to ROI time-activity curves (ROI-SAIF). Region-of-interest-SAIF and mean voxel-SAIF estimates of rCPS were highly correlated. Simulations showed that mean voxel-SAIF rCPS estimates were less biased and less variable than ROI-SAIF estimates in the whole brain and cortex; biases were similar in white matter. We conclude that estimation of rCPS with SAIF is improved when the method is applied at voxel level than in ROI analysis.

Third generation anthropomorphic physical phantom for mammography and DBT: incorporating voxelized 3D printing and uniform chest wall QC region

NASA Astrophysics Data System (ADS)

Zhao, Christine; Solomon, Justin; Sturgeon, Gregory M.; Gehm, Michael E.; Catenacci, Matthew; Wiley, Benjamin J.; Samei, Ehsan; Lo, Joseph Y.

2017-03-01

Physical breast phantoms provide a standard method to test, optimize, and develop clinical mammography systems, including new digital breast tomosynthesis (DBT) systems. In previous work, we produced an anthropomorphic phantom based on 500x500x500 μm breast CT data using commercial 3D printing. We now introduce an improved phantom based on a new cohort of virtual models with 155x155x155 μm voxels and fabricated through voxelized 3D printing and dithering, which confer higher resolution and greater control over contrast. This new generation includes a uniform chest wall extension for evaluating conventional QC metrics. The uniform region contains a grayscale step wedge, chest wall coverage markers, fiducial markers, spheres, and metal ink stickers of line pairs and edges to assess contrast, resolution, artifact spread function, MTF, and other criteria. We also experimented with doping photopolymer material with calcium, iodine, and zinc to increase our current contrast. In particular, zinc was discovered to significantly increase attenuation beyond 100% breast density with a linear relationship between zinc concentration and attenuation or breast density. This linear relationship was retained when the zinc-doped material was applied in conjunction with 3D printing. As we move towards our long term goal of phantoms that are indistinguishable from patients, this new generation of anthropomorphic physical breast phantom validates our voxelized printing process, demonstrates the utility of a uniform QC region with features from 3D printing and metal ink stickers, and shows potential for improved contrast via doping.

Edge-Related Activity Is Not Necessary to Explain Orientation Decoding in Human Visual Cortex.

PubMed

Wardle, Susan G; Ritchie, J Brendan; Seymour, Kiley; Carlson, Thomas A

2017-02-01

Multivariate pattern analysis is a powerful technique; however, a significant theoretical limitation in neuroscience is the ambiguity in interpreting the source of decodable information used by classifiers. This is exemplified by the continued controversy over the source of orientation decoding from fMRI responses in human V1. Recently Carlson (2014) identified a potential source of decodable information by modeling voxel responses based on the Hubel and Wiesel (1972) ice-cube model of visual cortex. The model revealed that activity associated with the edges of gratings covaries with orientation and could potentially be used to discriminate orientation. Here we empirically evaluate whether "edge-related activity" underlies orientation decoding from patterns of BOLD response in human V1. First, we systematically mapped classifier performance as a function of stimulus location using population receptive field modeling to isolate each voxel's overlap with a large annular grating stimulus. Orientation was decodable across the stimulus; however, peak decoding performance occurred for voxels with receptive fields closer to the fovea and overlapping with the inner edge. Critically, we did not observe the expected second peak in decoding performance at the outer stimulus edge as predicted by the edge account. Second, we evaluated whether voxels that contribute most to classifier performance have receptive fields that cluster in cortical regions corresponding to the retinotopic location of the stimulus edge. Instead, we find the distribution of highly weighted voxels to be approximately random, with a modest bias toward more foveal voxels. Our results demonstrate that edge-related activity is likely not necessary for orientation decoding. A significant theoretical limitation of multivariate pattern analysis in neuroscience is the ambiguity in interpreting the source of decodable information used by classifiers. For example, orientation can be decoded from BOLD activation patterns in human V1, even though orientation columns are at a finer spatial scale than 3T fMRI. Consequently, the source of decodable information remains controversial. Here we test the proposal that information related to the stimulus edges underlies orientation decoding. We map voxel population receptive fields in V1 and evaluate orientation decoding performance as a function of stimulus location in retinotopic cortex. We find orientation is decodable from voxels whose receptive fields do not overlap with the stimulus edges, suggesting edge-related activity does not substantially drive orientation decoding. Copyright © 2017 the authors 0270-6474/17/371187-10$15.00/0.

Brain Tumors: The Influence of Tumor Type and Routine MR Imaging Characteristics at BOLD Functional MR Imaging in the Primary Motor Gyrus

PubMed Central

Fraga de Abreu, Vitor Hugo; Peck, Kyung K.; Petrovich-Brennan, Nicole M.; Woo, Kaitlin M.

2016-01-01

Purpose To evaluate the effects of histologic features and anatomic magnetic resonance (MR) imaging characteristics of brain tumors on the functional MR imaging signal in the primary motor cortex (PMC), as false-negative blood oxygen level–dependent (BOLD) functional MR imaging activation can limit the accurate localization of eloquent cortices. Materials and Methods Institutional review board approval was obtained, and informed consent was waived for this HIPAA-compliant retrospective study. It comprised 63 patients referred between 2006 and 2014 for preoperative functional MR imaging localization of the Rolandic cortex. The patients had glioblastoma multiforme (GBM) (n = 20), metastasis (n = 21), or meningioma (n = 22). The volumes of functional MR imaging activation were measured during performance of a bilateral hand motor task. Ratios of functional MR imaging activation were normalized to PMC volume. Statistical analysis was performed for the following: (a) differences between hemispheres within each histologic tumor type (paired Wilcoxon test), (b) differences across tumor types (Kruskal-Wallis and Fisher tests), (c) pairwise tests between tumor types (Mann-Whitney U test), (d) relationships between fast fluid-attenuated inversion recovery (FLAIR) data and enhancement volume with activation (Spearman rank correlation coefficient), and (e) differences in activation volumes by tumor location (Mann-Whitney U test). Results A significant interhemispheric difference was found between the activation volumes in GBMs (mean, 511.43 voxels ± 307.73 [standard deviation] and 330.78 voxels ± 278.95; P < .01) but not in metastases (504.68 voxels ± 220.98 and 460.22 voxels ± 276.83; P = .15) or meningiomas (424.07 voxels ± 247.58 and 415.18 voxels ± 222.36; P = .85). GBMs showed significantly lower activation ratios (median, 0.49; range, 0.04–1.15) than metastases (median, 0.79; range, 0.28–1.66; P = .043) and meningiomas (median, 0.91; range, 0.52–2.05; P < .01). There was a moderate correlation with the volumes of FLAIR abnormality in metastases (ρ = −0.50) and meningiomas (ρ = −0.55). Enhancement volume (ρ = −0.11) and tumor distance from the PMC (median, 0.73 and range, 0.04–2.05 for near and median, 0.82 and range, 0.39–1.66 for far; P = .14) did not influence activation. Conclusion BOLD functional MR imaging activation in the ipsilateral PMC is influenced by tumor type and is significantly reduced in GBMs. FLAIR abnormality correlates moderately with the activation ratios in metastases and meningiomas. © RSNA, 2016 Online supplemental material is available for this article. PMID:27383533

Clinical evaluation of semi-automatic open-source algorithmic software segmentation of the mandibular bone: Practical feasibility and assessment of a new course of action

PubMed Central

Wallner, Jürgen; Hochegger, Kerstin; Chen, Xiaojun; Mischak, Irene; Reinbacher, Knut; Pau, Mauro; Zrnc, Tomislav; Schwenzer-Zimmerer, Katja; Zemann, Wolfgang; Schmalstieg, Dieter

2018-01-01

Introduction Computer assisted technologies based on algorithmic software segmentation are an increasing topic of interest in complex surgical cases. However—due to functional instability, time consuming software processes, personnel resources or licensed-based financial costs many segmentation processes are often outsourced from clinical centers to third parties and the industry. Therefore, the aim of this trial was to assess the practical feasibility of an easy available, functional stable and licensed-free segmentation approach to be used in the clinical practice. Material and methods In this retrospective, randomized, controlled trail the accuracy and accordance of the open-source based segmentation algorithm GrowCut was assessed through the comparison to the manually generated ground truth of the same anatomy using 10 CT lower jaw data-sets from the clinical routine. Assessment parameters were the segmentation time, the volume, the voxel number, the Dice Score and the Hausdorff distance. Results Overall semi-automatic GrowCut segmentation times were about one minute. Mean Dice Score values of over 85% and Hausdorff Distances below 33.5 voxel could be achieved between the algorithmic GrowCut-based segmentations and the manual generated ground truth schemes. Statistical differences between the assessment parameters were not significant (p<0.05) and correlation coefficients were close to the value one (r > 0.94) for any of the comparison made between the two groups. Discussion Complete functional stable and time saving segmentations with high accuracy and high positive correlation could be performed by the presented interactive open-source based approach. In the cranio-maxillofacial complex the used method could represent an algorithmic alternative for image-based segmentation in the clinical practice for e.g. surgical treatment planning or visualization of postoperative results and offers several advantages. Due to an open-source basis the used method could be further developed by other groups or specialists. Systematic comparisons to other segmentation approaches or with a greater data amount are areas of future works. PMID:29746490

Fast computation of voxel-level brain connectivity maps from resting-state functional MRI using l₁-norm as approximation of Pearson's temporal correlation: proof-of-concept and example vector hardware implementation.

PubMed

Minati, Ludovico; Zacà, Domenico; D'Incerti, Ludovico; Jovicich, Jorge

2014-09-01

An outstanding issue in graph-based analysis of resting-state functional MRI is choice of network nodes. Individual consideration of entire brain voxels may represent a less biased approach than parcellating the cortex according to pre-determined atlases, but entails establishing connectedness for 1(9)-1(11) links, with often prohibitive computational cost. Using a representative Human Connectome Project dataset, we show that, following appropriate time-series normalization, it may be possible to accelerate connectivity determination replacing Pearson correlation with l1-norm. Even though the adjacency matrices derived from correlation coefficients and l1-norms are not identical, their similarity is high. Further, we describe and provide in full an example vector hardware implementation of l1-norm on an array of 4096 zero instruction-set processors. Calculation times <1000 s are attainable, removing the major deterrent to voxel-based resting-sate network mapping and revealing fine-grained node degree heterogeneity. L1-norm should be given consideration as a substitute for correlation in very high-density resting-state functional connectivity analyses. Copyright © 2014 IPEM. Published by Elsevier Ltd. All rights reserved.

Improved superficial brain hemorrhage visualization in susceptibility weighted images by constrained minimum intensity projection

NASA Astrophysics Data System (ADS)

Castro, Marcelo A.; Pham, Dzung L.; Butman, John

2016-03-01

Minimum intensity projection is a technique commonly used to display magnetic resonance susceptibility weighted images, allowing the observer to better visualize hemorrhages and vasculature. The technique displays the minimum intensity in a given projection within a thick slab, allowing different connectivity patterns to be easily revealed. Unfortunately, the low signal intensity of the skull within the thick slab can mask superficial tissues near the skull base and other regions. Because superficial microhemorrhages are a common feature of traumatic brain injury, this effect limits the ability to proper diagnose and follow up patients. In order to overcome this limitation, we developed a method to allow minimum intensity projection to properly display superficial tissues adjacent to the skull. Our approach is based on two brain masks, the largest of which includes extracerebral voxels. The analysis of the rind within both masks containing the actual brain boundary allows reclassification of those voxels initially missed in the smaller mask. Morphological operations are applied to guarantee accuracy and topological correctness, and the mean intensity within the mask is assigned to all outer voxels. This prevents bone from dominating superficial regions in the projection, enabling superior visualization of cortical hemorrhages and vessels.

Grain Boundary Conformed Volumetric Mesh Generation from a Three-Dimensional Voxellated Polycrystalline Microstructure

NASA Astrophysics Data System (ADS)

Lee, Myeong-Jin; Jeon, Young-Ju; Son, Ga-Eun; Sung, Sihwa; Kim, Ju-Young; Han, Heung Nam; Cho, Soo Gyeong; Jung, Sang-Hyun; Lee, Sukbin

2018-07-01

We present a new comprehensive scheme for generating grain boundary conformed, volumetric mesh elements from a three-dimensional voxellated polycrystalline microstructure. From the voxellated image of a polycrystalline microstructure obtained from the Monte Carlo Potts model in the context of isotropic normal grain growth simulation, its grain boundary network is approximated as a curvature-maintained conformal triangular surface mesh using a set of in-house codes. In order to improve the surface mesh quality and to adjust mesh resolution, various re-meshing techniques in a commercial software are applied to the approximated grain boundary mesh. It is found that the aspect ratio, the minimum angle and the Jacobian value of the re-meshed surface triangular mesh are successfully improved. Using such an enhanced surface mesh, conformal volumetric tetrahedral elements of the polycrystalline microstructure are created using a commercial software, again. The resultant mesh seamlessly retains the short- and long-range curvature of grain boundaries and junctions as well as the realistic morphology of the grains inside the polycrystal. It is noted that the proposed scheme is the first to successfully generate three-dimensional mesh elements for polycrystals with high enough quality to be used for the microstructure-based finite element analysis, while the realistic characteristics of grain boundaries and grains are maintained from the corresponding voxellated microstructure image.

Grain Boundary Conformed Volumetric Mesh Generation from a Three-Dimensional Voxellated Polycrystalline Microstructure

NASA Astrophysics Data System (ADS)

Lee, Myeong-Jin; Jeon, Young-Ju; Son, Ga-Eun; Sung, Sihwa; Kim, Ju-Young; Han, Heung Nam; Cho, Soo Gyeong; Jung, Sang-Hyun; Lee, Sukbin

2018-03-01

We present a new comprehensive scheme for generating grain boundary conformed, volumetric mesh elements from a three-dimensional voxellated polycrystalline microstructure. From the voxellated image of a polycrystalline microstructure obtained from the Monte Carlo Potts model in the context of isotropic normal grain growth simulation, its grain boundary network is approximated as a curvature-maintained conformal triangular surface mesh using a set of in-house codes. In order to improve the surface mesh quality and to adjust mesh resolution, various re-meshing techniques in a commercial software are applied to the approximated grain boundary mesh. It is found that the aspect ratio, the minimum angle and the Jacobian value of the re-meshed surface triangular mesh are successfully improved. Using such an enhanced surface mesh, conformal volumetric tetrahedral elements of the polycrystalline microstructure are created using a commercial software, again. The resultant mesh seamlessly retains the short- and long-range curvature of grain boundaries and junctions as well as the realistic morphology of the grains inside the polycrystal. It is noted that the proposed scheme is the first to successfully generate three-dimensional mesh elements for polycrystals with high enough quality to be used for the microstructure-based finite element analysis, while the realistic characteristics of grain boundaries and grains are maintained from the corresponding voxellated microstructure image.

Advantages of utilizing DMD based rapid manufacturing systems in mass customization applications

NASA Astrophysics Data System (ADS)

El-Siblani, A.

2010-02-01

The Use of DMD based Rapid Manufacturing Systems has proven to be very advantageous in the production of highly accurate plastic based components for use in mass customization market such as hearing aids, and dental markets. The voxelization process currently afforded with the DLP technology eliminates any layering effect associated with all existing additive Rapid Manufacturing technologies. The smooth accurate surfaces produced in an additive process utilizing DLP technology, through the voxelization approach, allow for the production of custom finished products. The implementation of DLP technology in rapid prototyping and rapid manufacturing systems allow for the usage of highly viscous photopolymer based liquid and paste composites for rapid manufacturing that could not be used in any other additive process prior to implementation of DLP technology in RP and RM systems. It also allowed for the greater throughput in production without sacrificing quality and accuracy.

White Matter Tract Integrity in Alzheimer's Disease vs. Late Onset Bipolar Disorder and Its Correlation with Systemic Inflammation and Oxidative Stress Biomarkers.

PubMed

Besga, Ariadna; Chyzhyk, Darya; Gonzalez-Ortega, Itxaso; Echeveste, Jon; Graña-Lecuona, Marina; Graña, Manuel; Gonzalez-Pinto, Ana

2017-01-01

Background: Late Onset Bipolar Disorder (LOBD) is the development of Bipolar Disorder (BD) at an age above 50 years old. It is often difficult to differentiate from other aging dementias, such as Alzheimer's Disease (AD), because they share cognitive and behavioral impairment symptoms. Objectives: We look for WM tract voxel clusters showing significant differences when comparing of AD vs. LOBD, and its correlations with systemic blood plasma biomarkers (inflammatory, neurotrophic factors, and oxidative stress). Materials: A sample of healthy controls (HC) ( n = 19), AD patients ( n = 35), and LOBD patients ( n = 24) was recruited at the Alava University Hospital. Blood plasma samples were obtained at recruitment time and analyzed to extract the inflammatory, oxidative stress, and neurotrophic factors. Several modalities of MRI were acquired for each subject, Methods: Fractional anisotropy (FA) coefficients are obtained from diffusion weighted imaging (DWI). Tract based spatial statistics (TBSS) finds FA skeleton clusters of WM tract voxels showing significant differences for all possible contrasts between HC, AD, and LOBD. An ANOVA F -test over all contrasts is carried out. Results of F -test are used to mask TBSS detected clusters for the AD > LOBD and LOBD > AD contrast to select the image clusters used for correlation analysis. Finally, Pearson's correlation coefficients between FA values at cluster sites and systemic blood plasma biomarker values are computed. Results: The TBSS contrasts with by ANOVA F -test has identified strongly significant clusters in the forceps minor, inferior longitudinal fasciculus, inferior fronto-occipital fasciculus, and cingulum gyrus. The correlation analysis of these tract clusters found strong negative correlation of AD with the nerve growth factor (NGF) and brain derived neurotrophic factor (BDNF) blood biomarkers. Negative correlation of AD and positive correlation of LOBD with inflammation biomarker IL6 was also found. Conclusion: TBSS voxel clusters tract atlas localizations are consistent with greater behavioral impairment and mood disorders in LOBD than in AD. Correlation analysis confirms that neurotrophic factors (i.e., NGF, BDNF) play a great role in AD while are absent in LOBD pathophysiology. Also, correlation results of IL1 and IL6 suggest stronger inflammatory effects in LOBD than in AD.

Hepatic MR imaging for in vivo differentiation of steatosis, iron deposition and combined storage disorder: single-ratio in/opposed phase analysis vs. dual-ratio Dixon discrimination.

PubMed

Bashir, Mustafa R; Merkle, Elmar M; Smith, Alastair D; Boll, Daniel T

2012-02-01

To assess whether in vivo dual-ratio Dixon discrimination can improve detection of diffuse liver disease, specifically steatosis, iron deposition and combined disease over traditional single-ratio in/opposed phase analysis. Seventy-one patients with biopsy-proven (17.7 ± 17.0 days) hepatic steatosis (n = 16), iron deposition (n = 11), combined deposition (n = 3) and neither disease (n = 41) underwent MR examinations. Dual-echo in/opposed-phase MR with Dixon water/fat reconstructions were acquired. Analysis consisted of: (a) single-ratio hepatic region-of-interest (ROI)-based assessment of in/opposed ratios; (b) dual-ratio hepatic ROI assessment of in/opposed and fat/water ratios; (c) computer-aided dual-ratio assessment evaluating all hepatic voxels. Disease-specific thresholds were determined; statistical analyses assessed disease-dependent voxel ratios, based on single-ratio (a) and dual-ratio (b and c) techniques. Single-ratio discrimination succeeded in identifying iron deposition (I/O(Ironthreshold)<0.88) and steatosis (I/O(Fatthreshold>1.15)) from normal parenchyma, sensitivity 70.0%; it failed to detect combined disease. Dual-ratio discrimination succeeded in identifying abnormal hepatic parenchyma (F/W(Normalthreshold)>0.05), sensitivity 96.7%; logarithmic functions for iron deposition (I/O(Irondiscriminator)e((F/W(Fat)-0.01)/0.48)) differentiated combined from isolated diseases, sensitivity 100.0%; computer-aided dual-ratio analysis was comparably sensitive but less specific, 90.2% vs. 97.6%. MR two-point-Dixon imaging using dual-ratio post-processing based on in/opposed and fat/water ratios improved in vivo detection of hepatic steatosis, iron deposition, and combined storage disease beyond traditional in/opposed analysis. Copyright © 2011 Elsevier Ireland Ltd. All rights reserved.

Development of a Multileaf Collimator for Proton Radiotherapy

DTIC Science & Technology

2006-06-01

voxel size and slice thickness can be adjusted and determine the resolution. Each voxel is assigned a CT Number, in Hounsfield units , which is a...measure of the linear attenuation of the material in that voxel. The Hounsfield unit is a comparison of the linear attenuation coefficient of some...a header, which contains relevant patient and scan information, and the data, which is a sequential listing of the Hounsfield units of each voxel

Threshold-driven optimization for reference-based auto-planning

NASA Astrophysics Data System (ADS)

Long, Troy; Chen, Mingli; Jiang, Steve; Lu, Weiguo

2018-02-01

We study threshold-driven optimization methodology for automatically generating a treatment plan that is motivated by a reference DVH for IMRT treatment planning. We present a framework for threshold-driven optimization for reference-based auto-planning (TORA). Commonly used voxel-based quadratic penalties have two components for penalizing under- and over-dosing of voxels: a reference dose threshold and associated penalty weight. Conventional manual- and auto-planning using such a function involves iteratively updating the preference weights while keeping the thresholds constant, an unintuitive and often inconsistent method for planning toward some reference DVH. However, driving a dose distribution by threshold values instead of preference weights can achieve similar plans with less computational effort. The proposed methodology spatially assigns reference DVH information to threshold values, and iteratively improves the quality of that assignment. The methodology effectively handles both sub-optimal and infeasible DVHs. TORA was applied to a prostate case and a liver case as a proof-of-concept. Reference DVHs were generated using a conventional voxel-based objective, then altered to be either infeasible or easy-to-achieve. TORA was able to closely recreate reference DVHs in 5-15 iterations of solving a simple convex sub-problem. TORA has the potential to be effective for auto-planning based on reference DVHs. As dose prediction and knowledge-based planning becomes more prevalent in the clinical setting, incorporating such data into the treatment planning model in a clear, efficient way will be crucial for automated planning. A threshold-focused objective tuning should be explored over conventional methods of updating preference weights for DVH-guided treatment planning.

Voxel inversion of airborne electromagnetic data

NASA Astrophysics Data System (ADS)

Auken, E.; Fiandaca, G.; Kirkegaard, C.; Vest Christiansen, A.

2013-12-01

Inversion of electromagnetic data usually refers to a model space being linked to the actual observation points, and for airborne surveys the spatial discretization of the model space reflects the flight lines. On the contrary, geological and groundwater models most often refer to a regular voxel grid, not correlated to the geophysical model space. This means that incorporating the geophysical data into the geological and/or hydrological modelling grids involves a spatial relocation of the models, which in itself is a subtle process where valuable information is easily lost. Also the integration of prior information, e.g. from boreholes, is difficult when the observation points do not coincide with the position of the prior information, as well as the joint inversion of airborne and ground-based surveys. We developed a geophysical inversion algorithm working directly in a voxel grid disconnected from the actual measuring points, which then allows for informing directly geological/hydrogeological models, for easier incorporation of prior information and for straightforward integration of different data types in joint inversion. The new voxel model space defines the soil properties (like resistivity) on a set of nodes, and the distribution of the properties is computed everywhere by means of an interpolation function f (e.g. inverse distance or kriging). The position of the nodes is fixed during the inversion and is chosen to sample the soil taking into account topography and inversion resolution. Given this definition of the voxel model space, both 1D and 2D/3D forward responses can be computed. The 1D forward responses are computed as follows: A) a 1D model subdivision, in terms of model thicknesses and direction of the "virtual" horizontal stratification, is defined for each 1D data set. For EM soundings the "virtual" horizontal stratification is set up parallel to the topography at the sounding position. B) the "virtual" 1D models are constructed by interpolating the soil properties in the medium point of the "virtual" layers. For 2D/3D forward responses the algorithm operates similarly, simply filling the 2D/3D meshes of the forward responses by computing the interpolation values in the centres of the mesh cells. The new definition of the voxel model space allows for incorporating straightforwardly the geophysical information into geological and/or hydrological models, just by using for defining the geophysical model space a voxel (hydro)geological grid. This simplify also the propagation of the uncertainty of geophysical parameters into the (hydro)geological models. Furthermore, prior information from boreholes, like resistivity logs, can be applied directly to the voxel model space, even if the borehole positions do not coincide with the actual observation points. In fact, the prior information is constrained to the model parameters through the interpolation function at the borehole locations. The presented algorithm is a further development of the AarhusInv program package developed at Aarhus University (formerly em1dinv), which manages both large scale AEM surveys and ground-based data. This work has been carried out as part of the HyGEM project, supported by the Danish Council of Strategic Research under grant number DSF 11-116763.

White Matter Changes and Confrontation Naming in Retired Aging National Football League Athletes.

PubMed

Strain, Jeremy F; Didehbani, Nyaz; Spence, Jeffrey; Conover, Heather; Bartz, Elizabeth K; Mansinghani, Sethesh; Jeroudi, Myrtle K; Rao, Neena K; Fields, Lindy M; Kraut, Michael A; Cullum, C Munro; Hart, John; Womack, Kyle B

2017-01-15

Using diffusion tensor imaging (DTI), we assessed the relationship of white matter integrity and performance on the Boston Naming Test (BNT) in a group of retired professional football players and a control group. We examined correlations between fractional anisotropy (FA) and mean diffusivity (MD) with BNT T-scores in an unbiased voxelwise analysis processed with tract-based spatial statistics (TBSS). We also analyzed the DTI data by grouping voxels together as white matter tracts and testing each tract's association with BNT T-scores. Significant voxelwise correlations between FA and BNT performance were only seen in the retired football players (p < 0.02). Two tracts had mean FA values that significantly correlated with BNT performance: forceps minor and forceps major. White matter integrity is important for distributed cognitive processes, and disruption correlates with diminished performance in athletes exposed to concussive and subconcussive brain injuries, but not in controls without such exposure.

Sensitivity study of voxel-based PET image comparison to image registration algorithms

DOE Office of Scientific and Technical Information (OSTI.GOV)

Yip, Stephen, E-mail: syip@lroc.harvard.edu; Chen, Aileen B.; Berbeco, Ross

2014-11-01

Purpose: Accurate deformable registration is essential for voxel-based comparison of sequential positron emission tomography (PET) images for proper adaptation of treatment plan and treatment response assessment. The comparison may be sensitive to the method of deformable registration as the optimal algorithm is unknown. This study investigated the impact of registration algorithm choice on therapy response evaluation. Methods: Sixteen patients with 20 lung tumors underwent a pre- and post-treatment computed tomography (CT) and 4D FDG-PET scans before and after chemoradiotherapy. All CT images were coregistered using a rigid and ten deformable registration algorithms. The resulting transformations were then applied to themore » respective PET images. Moreover, the tumor region defined by a physician on the registered PET images was classified into progressor, stable-disease, and responder subvolumes. Particularly, voxels with standardized uptake value (SUV) decreases >30% were classified as responder, while voxels with SUV increases >30% were progressor. All other voxels were considered stable-disease. The agreement of the subvolumes resulting from difference registration algorithms was assessed by Dice similarity index (DSI). Coefficient of variation (CV) was computed to assess variability of DSI between individual tumors. Root mean square difference (RMS{sub rigid}) of the rigidly registered CT images was used to measure the degree of tumor deformation. RMS{sub rigid} and DSI were correlated by Spearman correlation coefficient (R) to investigate the effect of tumor deformation on DSI. Results: Median DSI{sub rigid} was found to be 72%, 66%, and 80%, for progressor, stable-disease, and responder, respectively. Median DSI{sub deformable} was 63%–84%, 65%–81%, and 82%–89%. Variability of DSI was substantial and similar for both rigid and deformable algorithms with CV > 10% for all subvolumes. Tumor deformation had moderate to significant impact on DSI for progressor subvolume with R{sub rigid} = − 0.60 (p = 0.01) and R{sub deformable} = − 0.46 (p = 0.01–0.20) averaging over all deformable algorithms. For stable-disease subvolumes, the correlations were significant (p < 0.001) for all registration algorithms with R{sub rigid} = − 0.71 and R{sub deformable} = − 0.72. Progressor and stable-disease subvolumes resulting from rigid registration were in excellent agreement (DSI > 70%) for RMS{sub rigid} < 150 HU. However, tumor deformation was observed to have negligible effect on DSI for responder subvolumes with insignificant |R| < 0.26, p > 0.27. Conclusions: This study demonstrated that deformable algorithms cannot be arbitrarily chosen; different deformable algorithms can result in large differences of voxel-based PET image comparison. For low tumor deformation (RMS{sub rigid} < 150 HU), rigid and deformable algorithms yield similar results, suggesting deformable registration is not required for these cases.« less

Statistical testing and power analysis for brain-wide association study.

PubMed

Gong, Weikang; Wan, Lin; Lu, Wenlian; Ma, Liang; Cheng, Fan; Cheng, Wei; Grünewald, Stefan; Feng, Jianfeng

2018-04-05

The identification of connexel-wise associations, which involves examining functional connectivities between pairwise voxels across the whole brain, is both statistically and computationally challenging. Although such a connexel-wise methodology has recently been adopted by brain-wide association studies (BWAS) to identify connectivity changes in several mental disorders, such as schizophrenia, autism and depression, the multiple correction and power analysis methods designed specifically for connexel-wise analysis are still lacking. Therefore, we herein report the development of a rigorous statistical framework for connexel-wise significance testing based on the Gaussian random field theory. It includes controlling the family-wise error rate (FWER) of multiple hypothesis testings using topological inference methods, and calculating power and sample size for a connexel-wise study. Our theoretical framework can control the false-positive rate accurately, as validated empirically using two resting-state fMRI datasets. Compared with Bonferroni correction and false discovery rate (FDR), it can reduce false-positive rate and increase statistical power by appropriately utilizing the spatial information of fMRI data. Importantly, our method bypasses the need of non-parametric permutation to correct for multiple comparison, thus, it can efficiently tackle large datasets with high resolution fMRI images. The utility of our method is shown in a case-control study. Our approach can identify altered functional connectivities in a major depression disorder dataset, whereas existing methods fail. A software package is available at https://github.com/weikanggong/BWAS. Copyright © 2018 Elsevier B.V. All rights reserved.

On Statistical Analysis of Neuroimages with Imperfect Registration

PubMed Central

Kim, Won Hwa; Ravi, Sathya N.; Johnson, Sterling C.; Okonkwo, Ozioma C.; Singh, Vikas

2016-01-01

A variety of studies in neuroscience/neuroimaging seek to perform statistical inference on the acquired brain image scans for diagnosis as well as understanding the pathological manifestation of diseases. To do so, an important first step is to register (or co-register) all of the image data into a common coordinate system. This permits meaningful comparison of the intensities at each voxel across groups (e.g., diseased versus healthy) to evaluate the effects of the disease and/or use machine learning algorithms in a subsequent step. But errors in the underlying registration make this problematic, they either decrease the statistical power or make the follow-up inference tasks less effective/accurate. In this paper, we derive a novel algorithm which offers immunity to local errors in the underlying deformation field obtained from registration procedures. By deriving a deformation invariant representation of the image, the downstream analysis can be made more robust as if one had access to a (hypothetical) far superior registration procedure. Our algorithm is based on recent work on scattering transform. Using this as a starting point, we show how results from harmonic analysis (especially, non-Euclidean wavelets) yields strategies for designing deformation and additive noise invariant representations of large 3-D brain image volumes. We present a set of results on synthetic and real brain images where we achieve robust statistical analysis even in the presence of substantial deformation errors; here, standard analysis procedures significantly under-perform and fail to identify the true signal. PMID:27042168

Treating voxel geometries in radiation protection dosimetry with a patched version of the Monte Carlo codes MCNP and MCNPX.

PubMed

Burn, K W; Daffara, C; Gualdrini, G; Pierantoni, M; Ferrari, P

2007-01-01

The question of Monte Carlo simulation of radiation transport in voxel geometries is addressed. Patched versions of the MCNP and MCNPX codes are developed aimed at transporting radiation both in the standard geometry mode and in the voxel geometry treatment. The patched code reads an unformatted FORTRAN file derived from DICOM format data and uses special subroutines to handle voxel-to-voxel radiation transport. The various phases of the development of the methodology are discussed together with the new input options. Examples are given of employment of the code in internal and external dosimetry and comparisons with results from other groups are reported.

Probabilistic atlas-based segmentation of combined T1-weighted and DUTE MRI for calculation of head attenuation maps in integrated PET/MRI scanners.

PubMed

Poynton, Clare B; Chen, Kevin T; Chonde, Daniel B; Izquierdo-Garcia, David; Gollub, Randy L; Gerstner, Elizabeth R; Batchelor, Tracy T; Catana, Ciprian

2014-01-01

We present a new MRI-based attenuation correction (AC) approach for integrated PET/MRI systems that combines both segmentation- and atlas-based methods by incorporating dual-echo ultra-short echo-time (DUTE) and T1-weighted (T1w) MRI data and a probabilistic atlas. Segmented atlases were constructed from CT training data using a leave-one-out framework and combined with T1w, DUTE, and CT data to train a classifier that computes the probability of air/soft tissue/bone at each voxel. This classifier was applied to segment the MRI of the subject of interest and attenuation maps (μ-maps) were generated by assigning specific linear attenuation coefficients (LACs) to each tissue class. The μ-maps generated with this "Atlas-T1w-DUTE" approach were compared to those obtained from DUTE data using a previously proposed method. For validation of the segmentation results, segmented CT μ-maps were considered to the "silver standard"; the segmentation accuracy was assessed qualitatively and quantitatively through calculation of the Dice similarity coefficient (DSC). Relative change (RC) maps between the CT and MRI-based attenuation corrected PET volumes were also calculated for a global voxel-wise assessment of the reconstruction results. The μ-maps obtained using the Atlas-T1w-DUTE classifier agreed well with those derived from CT; the mean DSCs for the Atlas-T1w-DUTE-based μ-maps across all subjects were higher than those for DUTE-based μ-maps; the atlas-based μ-maps also showed a lower percentage of misclassified voxels across all subjects. RC maps from the atlas-based technique also demonstrated improvement in the PET data compared to the DUTE method, both globally as well as regionally.

A non-voxel-based broad-beam (NVBB) framework for IMRT treatment planning.

PubMed

Lu, Weiguo

2010-12-07

We present a novel framework that enables very large scale intensity-modulated radiation therapy (IMRT) planning in limited computation resources with improvements in cost, plan quality and planning throughput. Current IMRT optimization uses a voxel-based beamlet superposition (VBS) framework that requires pre-calculation and storage of a large amount of beamlet data, resulting in large temporal and spatial complexity. We developed a non-voxel-based broad-beam (NVBB) framework for IMRT capable of direct treatment parameter optimization (DTPO). In this framework, both objective function and derivative are evaluated based on the continuous viewpoint, abandoning 'voxel' and 'beamlet' representations. Thus pre-calculation and storage of beamlets are no longer needed. The NVBB framework has linear complexities (O(N(3))) in both space and time. The low memory, full computation and data parallelization nature of the framework render its efficient implementation on the graphic processing unit (GPU). We implemented the NVBB framework and incorporated it with the TomoTherapy treatment planning system (TPS). The new TPS runs on a single workstation with one GPU card (NVBB-GPU). Extensive verification/validation tests were performed in house and via third parties. Benchmarks on dose accuracy, plan quality and throughput were compared with the commercial TomoTherapy TPS that is based on the VBS framework and uses a computer cluster with 14 nodes (VBS-cluster). For all tests, the dose accuracy of these two TPSs is comparable (within 1%). Plan qualities were comparable with no clinically significant difference for most cases except that superior target uniformity was seen in the NVBB-GPU for some cases. However, the planning time using the NVBB-GPU was reduced many folds over the VBS-cluster. In conclusion, we developed a novel NVBB framework for IMRT optimization. The continuous viewpoint and DTPO nature of the algorithm eliminate the need for beamlets and lead to better plan quality. The computation parallelization on a GPU instead of a computer cluster significantly reduces hardware and service costs. Compared with using the current VBS framework on a computer cluster, the planning time is significantly reduced using the NVBB framework on a single workstation with a GPU card.

Effect of surrounding vasculature on intravoxel BOLD signal.

PubMed

Chen, Zikuan; Caprihan, Arvind; Calhoun, Vince

2010-04-01

The nonlocal influence from distant magnetization will affect the magnetic field at a voxel in question. Existing reports on BOLD simulation only consider vasculature inside a single voxel, thus omitting the contribution from the surrounding regions. In this article, the authors study the effect of the surrounding vasculature on the magnetic field and the BOLD signal at a cortical voxel by numerical simulation. A cortical voxel is generated as a cubic bin filled with randomly networked capillary vessels. First, the authors generate a cortical voxel with a random vessel network and embed it in a greater voxel by filling its surrounding region with vasculatures by different strategies. Next, they calculate the blood-susceptibility-induced magnetic field (BOLD field) at the voxel of interest (VOI) by a Fourier transform technique for different surrounding scenarios and varying surrounding extent. The BOLD field inhomogeneity is described by a radial distribution with a collection of cubic shell masks. The surrounding extent is defined by a collection of concentric cubes, which encase the VOI. Given a BOLD field in the presence of surrounding vasculature, they calculate BOLD signals by intravoxel dephasing. The influence from the surroundings on the BOLD field at a voxel in question mainly happens at the boundary. The most influence to the BOLD signal is from the inner surroundings. For a 160 x 160 x 160 microm3 voxel embedded in a 480 x 480 x 480 microm3 greater region, the surroundings could disturb the magnetic field by an amount in the range of [-0.002, 0.010] ppmT and could change the BOLD signal ratio in the range of [2.5%, 10%]. (These results were generated from the setting of delta(chi b)B0 = 3 ppmT, capillary = {2.5,6,9} microm, and relaxation time = 60 ms). The surrounding vasculature will impose a magnetic field disturbance at the voxel in question due to the nonlocal influence of magnetization. Simulation results show that the surrounding vasculature significantly alters the magnetic field (up to 0.01 ppmT) and BOLD signal (typically no more than 10%) at the central voxel and thus should be considered in accurate BOLD modeling.

A study of MRI gradient echo signals from discrete magnetic particles with considerations of several parameters in simulations.

PubMed

Kokeny, Paul; Cheng, Yu-Chung N; Xie, He

2018-05-01

Modeling MRI signal behaviors in the presence of discrete magnetic particles is important, as magnetic particles appear in nanoparticle labeled cells, contrast agents, and other biological forms of iron. Currently, many models that take into account the discrete particle nature in a system have been used to predict magnitude signal decays in the form of R2* or R2' from one single voxel. Little work has been done for predicting phase signals. In addition, most calculations of phase signals rely on the assumption that a system containing discrete particles behaves as a continuous medium. In this work, numerical simulations are used to investigate MRI magnitude and phase signals from discrete particles, without diffusion effects. Factors such as particle size, number density, susceptibility, volume fraction, particle arrangements for their randomness, and field of view have been considered in simulations. The results are compared to either a ground truth model, theoretical work based on continuous mediums, or previous literature. Suitable parameters used to model particles in several voxels that lead to acceptable magnetic field distributions around particle surfaces and accurate MR signals are identified. The phase values as a function of echo time from a central voxel filled by particles can be significantly different from those of a continuous cubic medium. However, a completely random distribution of particles can lead to an R2' value which agrees with the prediction from the static dephasing theory. A sphere with a radius of at least 4 grid points used in simulations is found to be acceptable to generate MR signals equivalent from a larger sphere. Increasing number of particles with a fixed volume fraction in simulations reduces the resulting variance in the phase behavior, and converges to almost the same phase value for different particle numbers at each echo time. The variance of phase values is also reduced when increasing the number of particles in a fixed voxel. These results indicate that MRI signals from voxels containing discrete particles, even with a sufficient number of particles per voxel, cannot be properly modeled by a continuous medium with an equivalent susceptibility value in the voxel. Copyright © 2017 Elsevier Inc. All rights reserved.