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.

MIDAS: Regionally linear multivariate discriminative statistical mapping.

PubMed

Varol, Erdem; Sotiras, Aristeidis; Davatzikos, Christos

2018-07-01

Statistical parametric maps formed via voxel-wise mass-univariate tests, such as the general linear model, are commonly used to test hypotheses about regionally specific effects in neuroimaging cross-sectional studies where each subject is represented by a single image. Despite being informative, these techniques remain limited as they ignore multivariate relationships in the data. Most importantly, the commonly employed local Gaussian smoothing, which is important for accounting for registration errors and making the data follow Gaussian distributions, is usually chosen in an ad hoc fashion. Thus, it is often suboptimal for the task of detecting group differences and correlations with non-imaging variables. Information mapping techniques, such as searchlight, which use pattern classifiers to exploit multivariate information and obtain more powerful statistical maps, have become increasingly popular in recent years. However, existing methods may lead to important interpretation errors in practice (i.e., misidentifying a cluster as informative, or failing to detect truly informative voxels), while often being computationally expensive. To address these issues, we introduce a novel efficient multivariate statistical framework for cross-sectional studies, termed MIDAS, seeking highly sensitive and specific voxel-wise brain maps, while leveraging the power of regional discriminant analysis. In MIDAS, locally linear discriminative learning is applied to estimate the pattern that best discriminates between two groups, or predicts a variable of interest. This pattern is equivalent to local filtering by an optimal kernel whose coefficients are the weights of the linear discriminant. By composing information from all neighborhoods that contain a given voxel, MIDAS produces a statistic that collectively reflects the contribution of the voxel to the regional classifiers as well as the discriminative power of the classifiers. Critically, MIDAS efficiently assesses the statistical significance of the derived statistic by analytically approximating its null distribution without the need for computationally expensive permutation tests. The proposed framework was extensively validated using simulated atrophy in structural magnetic resonance imaging (MRI) and further tested using data from a task-based functional MRI study as well as a structural MRI study of cognitive performance. The performance of the proposed framework was evaluated against standard voxel-wise general linear models and other information mapping methods. The experimental results showed that MIDAS achieves relatively higher sensitivity and specificity in detecting group differences. Together, our results demonstrate the potential of the proposed approach to efficiently map effects of interest in both structural and functional data. Copyright © 2018. Published by Elsevier Inc.

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.

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.

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.

Secondary Progressive and Relapsing Remitting Multiple Sclerosis Leads to Motor-Related Decreased Anatomical Connectivity

PubMed Central

Lyksborg, Mark; Siebner, Hartwig R.; Sørensen, Per S.; Blinkenberg, Morten; Parker, Geoff J. M.; Dogonowski, Anne-Marie; Garde, Ellen; Larsen, Rasmus; Dyrby, Tim B.

2014-01-01

Multiple sclerosis (MS) damages central white matter pathways which has considerable impact on disease-related disability. To identify disease-related alterations in anatomical connectivity, 34 patients (19 with relapsing remitting MS (RR-MS), 15 with secondary progressive MS (SP-MS) and 20 healthy subjects underwent diffusion magnetic resonance imaging (dMRI) of the brain. Based on the dMRI, anatomical connectivity mapping (ACM) yielded a voxel-based metric reflecting the connectivity shared between each individual voxel and all other brain voxels. To avoid biases caused by inter-individual brain-shape differences, they were estimated in a spatially normalized space. Voxel-based statistical analyses using ACM were compared with analyses based on the localized microstructural indices of fractional anisotropy (FA). In both RR-MS and SP-MS patients, considerable portions of the motor-related white matter revealed decreases in ACM and FA when compared with healthy subjects. Patients with SP-MS exhibited reduced ACM values relative to RR-MS in the motor-related tracts, whereas there were no consistent decreases in FA between SP-MS and RR-MS patients. Regional ACM statistics exhibited moderate correlation with clinical disability as reflected by the expanded disability status scale (EDSS). The correlation between these statistics and EDSS was either similar to or stronger than the correlation between FA statistics and the EDSS. Together, the results reveal an improved relationship between ACM, the clinical phenotype, and impairment. This highlights the potential of the ACM connectivity indices to be used as a marker which can identify disease related-alterations due to MS which may not be seen using localized microstructural indices. PMID:24748023

What do results from coordinate-based meta-analyses tell us?

PubMed

Albajes-Eizagirre, Anton; Radua, Joaquim

2018-08-01

Coordinate-based meta-analyses (CBMA) methods, such as Activation Likelihood Estimation (ALE) and Seed-based d Mapping (SDM), have become an invaluable tool for summarizing the findings of voxel-based neuroimaging studies. However, the progressive sophistication of these methods may have concealed two particularities of their statistical tests. Common univariate voxelwise tests (such as the t/z-tests used in SPM and FSL) detect voxels that activate, or voxels that show differences between groups. Conversely, the tests conducted in CBMA test for "spatial convergence" of findings, i.e., they detect regions where studies report "more peaks than in most regions", regions that activate "more than most regions do", or regions that show "larger differences between groups than most regions do". The first particularity is that these tests rely on two spatial assumptions (voxels are independent and have the same probability to have a "false" peak), whose violation may make their results either conservative or liberal, though fortunately current versions of ALE, SDM and some other methods consider these assumptions. The second particularity is that the use of these tests involves an important paradox: the statistical power to detect a given effect is higher if there are no other effects in the brain, whereas lower in presence of multiple effects. Copyright © 2018 Elsevier Inc. All rights reserved.

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.

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.

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

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.

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

TU-F-CAMPUS-J-04: Impact of Voxel Anisotropy On Statistic Texture Features of Oncologic PET: A Simulation Study

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

Yang, F; Byrd, D; Bowen, S

2015-06-15

Purpose: Texture metrics extracted from oncologic PET have been investigated with respect to their usefulness as definitive indicants for prognosis in a variety of cancer. Metric calculation is often based on cubic voxels. Most commonly used PET scanners, however, produce rectangular voxels, which may change texture metrics. The objective of this study was to examine the variability of PET texture feature metrics resulting from voxel anisotropy. Methods: Sinograms of NEMA NU-2 phantom for 18F-FDG were simulated using the ASIM simulation tool. The obtained projection data was reconstructed (3D-OSEM) on grids of cubic and rectangular voxels, producing PET images of resolutionmore » of 2.73x2.73x3.27mm3 and 3.27x3.27x3.27mm3, respectively. An interpolated dataset obtained from resampling the rectangular voxel data for isotropic voxel dimension (3.27mm) was also considered. For each image dataset, 28 texture parameters based on grey-level co-occurrence matrices (GLCOM), intensity histograms (GLIH), neighborhood difference matrices (GLNDM), and zone size matrices (GLZSM) were evaluated within lesions of diameter of 33, 28, 22, and 17mm. Results: In reference to the isotopic image data, texture features appearing on the rectangular voxel data varied with a range of -34-10% for GLCOM based, -31-39% for GLIH based, -80 -161% for GLNDM based, and −6–45% for GLZSM based while varied with a range of -35-23% for GLCOM based, -27-35% for GLIH based, -65-86% for GLNDM based, and -22 -18% for GLZSM based for the interpolated image data. For the anisotropic data, GLNDM-cplx exhibited the largest extent of variation (161%) while GLZSM-zp showed the least (<1%). As to the interpolated data, GLNDM-busy varied the most (86%) while GLIH-engy varied the least (<1%). Conclusion: Variability of texture appearance on oncologic PET with respect to voxel representation is substantial and feature-dependent. It necessitates consideration of standardized voxel representation for inter-institution studies attempting to validate prognostic values of PET texture features in cancer treatment.« less

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.

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.

Image Statistics and the Representation of Material Properties in the Visual Cortex

PubMed Central

Baumgartner, Elisabeth; Gegenfurtner, Karl R.

2016-01-01

We explored perceived material properties (roughness, texturedness, and hardness) with a novel approach that compares perception, image statistics and brain activation, as measured with fMRI. We initially asked participants to rate 84 material images with respect to the above mentioned properties, and then scanned 15 of the participants with fMRI while they viewed the material images. The images were analyzed with a set of image statistics capturing their spatial frequency and texture properties. Linear classifiers were then applied to the image statistics as well as the voxel patterns of visually responsive voxels and early visual areas to discriminate between images with high and low perceptual ratings. Roughness and texturedness could be classified above chance level based on image statistics. Roughness and texturedness could also be classified based on the brain activation patterns in visual cortex, whereas hardness could not. Importantly, the agreement in classification based on image statistics and brain activation was also above chance level. Our results show that information about visual material properties is to a large degree contained in low-level image statistics, and that these image statistics are also partially reflected in brain activity patterns induced by the perception of material images. PMID:27582714

Image Statistics and the Representation of Material Properties in the Visual Cortex.

PubMed

Baumgartner, Elisabeth; Gegenfurtner, Karl R

2016-01-01

We explored perceived material properties (roughness, texturedness, and hardness) with a novel approach that compares perception, image statistics and brain activation, as measured with fMRI. We initially asked participants to rate 84 material images with respect to the above mentioned properties, and then scanned 15 of the participants with fMRI while they viewed the material images. The images were analyzed with a set of image statistics capturing their spatial frequency and texture properties. Linear classifiers were then applied to the image statistics as well as the voxel patterns of visually responsive voxels and early visual areas to discriminate between images with high and low perceptual ratings. Roughness and texturedness could be classified above chance level based on image statistics. Roughness and texturedness could also be classified based on the brain activation patterns in visual cortex, whereas hardness could not. Importantly, the agreement in classification based on image statistics and brain activation was also above chance level. Our results show that information about visual material properties is to a large degree contained in low-level image statistics, and that these image statistics are also partially reflected in brain activity patterns induced by the perception of material images.

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.

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-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.

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.

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.

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.

Magnetoencephalography with temporal spread imaging to visualize propagation of epileptic activity.

PubMed

Shibata, Sumiya; Matsuhashi, Masao; Kunieda, Takeharu; Yamao, Yukihiro; Inano, Rika; Kikuchi, Takayuki; Imamura, Hisaji; Takaya, Shigetoshi; Matsumoto, Riki; Ikeda, Akio; Takahashi, Ryosuke; Mima, Tatsuya; Fukuyama, Hidenao; Mikuni, Nobuhiro; Miyamoto, Susumu

2017-05-01

We describe temporal spread imaging (TSI) that can identify the spatiotemporal pattern of epileptic activity using Magnetoencephalography (MEG). A three-dimensional grid of voxels covering the brain is created. The array-gain minimum-variance spatial filter is applied to an interictal spike to estimate the magnitude of the source and the time (Ta) when the magnitude exceeds a predefined threshold at each voxel. This calculation is performed through all spikes. Each voxel has the mean Ta () and spike number (N sp ), which is the number of spikes whose source exceeds the threshold. Then, a random resampling method is used to determine the cutoff value of N sp for the statistically reproducible pattern of the activity. Finally, all the voxels where the source exceeds the threshold reproducibly shown on the MRI with a color scale representing . Four patients with intractable mesial temporal lobe epilepsy (MTLE) were analyzed. In three patients, the common pattern of the overlap between the propagation and the hypometabolism shown by fluorodeoxyglucose-positron emission tomography (FDG-PET) was identified. TSI can visualize statistically reproducible patterns of the temporal and spatial spread of epileptic activity. TSI can assess the statistical significance of the spatiotemporal pattern based on its reproducibility. Copyright © 2017 International Federation of Clinical Neurophysiology. Published by Elsevier B.V. All rights reserved.

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

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

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.

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

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

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.

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.

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.

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

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.

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.

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

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

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.

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.

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.

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

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.

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

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.

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.

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.

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).

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

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Cluster mass inference via random field theory.

PubMed

Zhang, Hui; Nichols, Thomas E; Johnson, Timothy D

2009-01-01

Cluster extent and voxel intensity are two widely used statistics in neuroimaging inference. Cluster extent is sensitive to spatially extended signals while voxel intensity is better for intense but focal signals. In order to leverage strength from both statistics, several nonparametric permutation methods have been proposed to combine the two methods. Simulation studies have shown that of the different cluster permutation methods, the cluster mass statistic is generally the best. However, to date, there is no parametric cluster mass inference available. In this paper, we propose a cluster mass inference method based on random field theory (RFT). We develop this method for Gaussian images, evaluate it on Gaussian and Gaussianized t-statistic images and investigate its statistical properties via simulation studies and real data. Simulation results show that the method is valid under the null hypothesis and demonstrate that it can be more powerful than the cluster extent inference method. Further, analyses with a single subject and a group fMRI dataset demonstrate better power than traditional cluster size inference, and good accuracy relative to a gold-standard permutation test.

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.

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.

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.

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).

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

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.

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.

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).

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.

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.

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.

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

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

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.

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

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.

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

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.

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.

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.

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.

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.

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.

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.

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.

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

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

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

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.

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.

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.

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.

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.

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.

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

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.

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

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

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.

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.

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.

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.

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.

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.

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.

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.

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.

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

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

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.

White Matter Fiber-based Analysis of T1w/T2w Ratio Map.

PubMed

Chen, Haiwei; Budin, Francois; Noel, Jean; Prieto, Juan Carlos; Gilmore, John; Rasmussen, Jerod; Wadhwa, Pathik D; Entringer, Sonja; Buss, Claudia; Styner, Martin

2017-02-01

To develop, test, evaluate and apply a novel tool for the white matter fiber-based analysis of T1w/T2w ratio maps quantifying myelin content. The cerebral white matter in the human brain develops from a mostly non-myelinated state to a nearly fully mature white matter myelination within the first few years of life. High resolution T1w/T2w ratio maps are believed to be effective in quantitatively estimating myelin content on a voxel-wise basis. We propose the use of a fiber-tract-based analysis of such T1w/T2w ratio data, as it allows us to separate fiber bundles that a common regional analysis imprecisely groups together, and to associate effects to specific tracts rather than large, broad regions. We developed an intuitive, open source tool to facilitate such fiber-based studies of T1w/T2w ratio maps. Via its Graphical User Interface (GUI) the tool is accessible to non-technical users. The framework uses calibrated T1w/T2w ratio maps and a prior fiber atlas as an input to generate profiles of T1w/T2w values. The resulting fiber profiles are used in a statistical analysis that performs along-tract functional statistical analysis. We applied this approach to a preliminary study of early brain development in neonates. We developed an open-source tool for the fiber based analysis of T1w/T2w ratio maps and tested it in a study of brain development.

White matter fiber-based analysis of T1w/T2w ratio map

NASA Astrophysics Data System (ADS)

Chen, Haiwei; Budin, Francois; Noel, Jean; Prieto, Juan Carlos; Gilmore, John; Rasmussen, Jerod; Wadhwa, Pathik D.; Entringer, Sonja; Buss, Claudia; Styner, Martin

2017-02-01

Purpose: To develop, test, evaluate and apply a novel tool for the white matter fiber-based analysis of T1w/T2w ratio maps quantifying myelin content. Background: The cerebral white matter in the human brain develops from a mostly non-myelinated state to a nearly fully mature white matter myelination within the first few years of life. High resolution T1w/T2w ratio maps are believed to be effective in quantitatively estimating myelin content on a voxel-wise basis. We propose the use of a fiber-tract-based analysis of such T1w/T2w ratio data, as it allows us to separate fiber bundles that a common regional analysis imprecisely groups together, and to associate effects to specific tracts rather than large, broad regions. Methods: We developed an intuitive, open source tool to facilitate such fiber-based studies of T1w/T2w ratio maps. Via its Graphical User Interface (GUI) the tool is accessible to non-technical users. The framework uses calibrated T1w/T2w ratio maps and a prior fiber atlas as an input to generate profiles of T1w/T2w values. The resulting fiber profiles are used in a statistical analysis that performs along-tract functional statistical analysis. We applied this approach to a preliminary study of early brain development in neonates. Results: We developed an open-source tool for the fiber based analysis of T1w/T2w ratio maps and tested it in a study of brain development.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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

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.

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.

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.

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.

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

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.

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 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

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.

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.

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

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.

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.

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.

Monitoring of human brain functions in risk decision-making task by diffuse optical tomography using voxel-wise general linear model

NASA Astrophysics Data System (ADS)

Lin, Zi-Jing; Li, Lin; Cazzell, Marry; Liu, Hanli

2013-03-01

Functional near-infrared spectroscopy (fNIRS) is a non-invasive imaging technique which measures the hemodynamic changes that reflect the brain activity. Diffuse optical tomography (DOT), a variant of fNIRS with multi-channel NIRS measurements, has demonstrated capability of three dimensional (3D) reconstructions of hemodynamic changes due to the brain activity. Conventional method of DOT image analysis to define the brain activation is based upon the paired t-test between two different states, such as resting-state versus task-state. However, it has limitation because the selection of activation and post-activation period is relatively subjective. General linear model (GLM) based analysis can overcome this limitation. In this study, we combine the 3D DOT image reconstruction with GLM-based analysis (i.e., voxel-wise GLM analysis) to investigate the brain activity that is associated with the risk-decision making process. Risk decision-making is an important cognitive process and thus is an essential topic in the field of neuroscience. The balloon analogue risk task (BART) is a valid experimental model and has been commonly used in behavioral measures to assess human risk taking action and tendency while facing risks. We have utilized the BART paradigm with a blocked design to investigate brain activations in the prefrontal and frontal cortical areas during decision-making. Voxel-wise GLM analysis was performed on 18human participants (10 males and 8females).In this work, we wish to demonstrate the feasibility of using voxel-wise GLM analysis to image and study cognitive functions in response to risk decision making by DOT. Results have shown significant changes in the dorsal lateral prefrontal cortex (DLPFC) during the active choice mode and a different hemodynamic pattern between genders, which are in good agreements with published literatures in functional magnetic resonance imaging (fMRI) and fNIRS studies.

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.

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.

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

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

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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

Diffusion tensor imaging of nigral degeneration in Parkinson's disease: A region-of-interest and voxel-based study at 3 T and systematic review with meta-analysis☆

PubMed Central

Schwarz, Stefan T.; Abaei, Maryam; Gontu, Vamsi; Morgan, Paul S.; Bajaj, Nin; Auer, Dorothee P.

2013-01-01

There is increasing interest in developing a reliable, affordable and accessible disease biomarker of Parkinson's disease (PD) to facilitate disease modifying PD-trials. Imaging biomarkers using magnetic resonance imaging (MRI) and diffusion tensor imaging (DTI) can describe parameters such as fractional anisotropy (FA), mean diffusivity (MD) or apparent diffusion coefficient (ADC). These parameters, when measured in the substantia nigra (SN), have not only shown promising but also varying and controversial results. To clarify the potential diagnostic value of nigral DTI in PD and its dependency on selection of region-of-interest, we undertook a high resolution DTI study at 3 T. 59 subjects (32 PD patients, 27 age and sex matched healthy controls) were analysed using manual outlining of SN and substructures, and voxel-based analysis (VBA). We also performed a systematic literature review and meta-analysis to estimate the effect size (DES) of disease related nigral DTI changes. We found a regional increase in nigral mean diffusivity in PD (mean ± SD, PD 0.80 ± 0.10 vs. controls 0.73 ± 0.06 · 10− 3 mm2/s, p = 0.002), but no difference using a voxel based approach. No significant disease effect was seen using meta-analysis of nigral MD changes (10 studies, DES = + 0.26, p = 0.17, I2 = 30%). None of the nigral regional or voxel based analyses of this study showed altered fractional anisotropy. Meta-analysis of 11 studies on nigral FA changes revealed a significant PD induced FA decrease. There was, however, a very large variation in results (I2 = 86%) comparing all studies. After exclusion of five studies with unusual high values of nigral FA in the control group, an acceptable heterogeneity was reached, but there was non-significant disease effect (DES = − 0.5, p = 0.22, I2 = 28%). The small PD related nigral MD changes in conjunction with the negative findings on VBA and meta-analysis limit the usefulness of nigral MD measures as biomarker of Parkinson's disease. The negative results of nigral FA measurements at regional, sub-regional and voxel level in conjunction with the results of the meta-analysis of nigral FA changes question the stability and validity of this measure as a PD biomarker. PMID:24273730

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.

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

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.

What Do Differences Between Multi-voxel and Univariate Analysis Mean? How Subject-, Voxel-, and Trial-level Variance Impact fMRI Analysis

PubMed Central

Davis, Tyler; LaRocque, Karen F.; Mumford, Jeanette; Norman, Kenneth A.; Wagner, Anthony D.; Poldrack, Russell A.

2014-01-01

Multi-voxel pattern analysis (MVPA) has led to major changes in how fMRI data are analyzed and interpreted. Many studies now report both MVPA results and results from standard univariate voxel-wise analysis, often with the goal of drawing different conclusions from each. Because MVPA results can be sensitive to latent multidimensional representations and processes whereas univariate voxel-wise analysis cannot, one conclusion that is often drawn when MVPA and univariate results differ is that the activation patterns underlying MVPA results contain a multidimensional code. In the current study, we conducted simulations to formally test this assumption. Our findings reveal that MVPA tests are sensitive to the magnitude of voxel-level variability in the effect of a condition within subjects, even when the same linear relationship is coded in all voxels. We also find that MVPA is insensitive to subject-level variability in mean activation across an ROI, which is the primary variance component of interest in many standard univariate tests. Together, these results illustrate that differences between MVPA and univariate tests do not afford conclusions about the nature or dimensionality of the neural code. Instead, targeted tests of the informational content and/or dimensionality of activation patterns are critical for drawing strong conclusions about the representational codes that are indicated by significant MVPA results. PMID:24768930

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.

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

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

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.

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.

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.

A hybrid correlation analysis with application to imaging genetics

NASA Astrophysics Data System (ADS)

Hu, Wenxing; Fang, Jian; Calhoun, Vince D.; Wang, Yu-Ping

2018-03-01

Investigating the association between brain regions and genes continues to be a challenging topic in imaging genetics. Current brain region of interest (ROI)-gene association studies normally reduce data dimension by averaging the value of voxels in each ROI. This averaging may lead to a loss of information due to the existence of functional sub-regions. Pearson correlation is widely used for association analysis. However, it only detects linear correlation whereas nonlinear correlation may exist among ROIs. In this work, we introduced distance correlation to ROI-gene association analysis, which can detect both linear and nonlinear correlations and overcome the limitation of averaging operations by taking advantage of the information at each voxel. Nevertheless, distance correlation usually has a much lower value than Pearson correlation. To address this problem, we proposed a hybrid correlation analysis approach, by applying canonical correlation analysis (CCA) to the distance covariance matrix instead of directly computing distance correlation. Incorporating CCA into distance correlation approach may be more suitable for complex disease study because it can detect highly associated pairs of ROI and gene groups, and may improve the distance correlation level and statistical power. In addition, we developed a novel nonlinear CCA, called distance kernel CCA, which seeks the optimal combination of features with the most significant dependence. This approach was applied to imaging genetic data from the Philadelphia Neurodevelopmental Cohort (PNC). Experiments showed that our hybrid approach produced more consistent results than conventional CCA across resampling and both the correlation and statistical significance were increased compared to distance correlation analysis. Further gene enrichment analysis and region of interest (ROI) analysis confirmed the associations of the identified genes with brain ROIs. Therefore, our approach provides a powerful tool for finding the correlation between brain imaging and genomic data.

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.

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.

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.

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.

Voxel based morphometry in optical coherence tomography: validation and core findings

NASA Astrophysics Data System (ADS)

Antony, Bhavna J.; Chen, Min; Carass, Aaron; Jedynak, Bruno M.; Al-Louzi, Omar; Solomon, Sharon D.; Saidha, Shiv; Calabresi, Peter A.; Prince, Jerry L.

2016-03-01

Optical coherence tomography (OCT) of the human retina is now becoming established as an important modality for the detection and tracking of various ocular diseases. Voxel based morphometry (VBM) is a long standing neuroimaging analysis technique that allows for the exploration of the regional differences in the brain. There has been limited work done in developing registration based methods for OCT, which has hampered the advancement of VBM analyses in OCT based population studies. Following on from our recent development of an OCT registration method, we explore the potential benefits of VBM analysis in cohorts of healthy controls (HCs) and multiple sclerosis (MS) patients. Specifically, we validate the stability of VBM analysis in two pools of HCs showing no significant difference between the two populations. Additionally, we also present a retrospective study of age and sex matched HCs and relapsing remitting MS patients, demonstrating results consistent with the reported literature while providing insight into the retinal changes associated with this MS subtype.

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

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.

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.

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 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.

Structural and functional changes in the somatosensory cortex in euthymic females with bipolar disorder.

PubMed

Minuzzi, Luciano; Syan, Sabrina K; Smith, Mara; Hall, Alexander; Hall, Geoffrey Bc; Frey, Benicio N

2017-12-01

Current evidence from neuroimaging data suggests possible dysfunction of the fronto-striatal-limbic circuits in individuals with bipolar disorder. Somatosensory cortical function has been implicated in emotional recognition, risk-taking and affective responses through sensory modalities. This study investigates anatomy and function of the somatosensory cortex in euthymic bipolar women. In total, 68 right-handed euthymic women (bipolar disorder = 32 and healthy controls = 36) between 16 and 45 years of age underwent high-resolution anatomical and functional magnetic resonance imaging during the mid-follicular menstrual phase. The somatosensory cortex was used as a seed region for resting-state functional connectivity analysis. Voxel-based morphometry was used to evaluate somatosensory cortical gray matter volume between groups. We found increased resting-state functional connectivity between the somatosensory cortex and insular cortex, inferior prefrontal gyrus and frontal orbital cortex in euthymic bipolar disorder subjects compared to healthy controls. Voxel-based morphometry analysis showed decreased gray matter in the left somatosensory cortex in the bipolar disorder group. Whole-brain voxel-based morphometry analysis controlled by age did not reveal any additional significant difference between groups. This study is the first to date to evaluate anatomy and function of the somatosensory cortex in a well-characterized sample of euthymic bipolar disorder females. Anatomical and functional changes in the somatosensory cortex in this population might contribute to the pathophysiology of bipolar disorder.

Quantitative susceptibility mapping across two clinical field strengths: Contrast-to-noise ratio enhancement at 1.5T.

PubMed

Ippoliti, Matteo; Adams, Lisa C; Winfried, Brenner; Hamm, Bernd; Spincemaille, Pascal; Wang, Yi; Makowski, Marcus R

2018-04-16

Quantitative susceptibility mapping (QSM) is an MRI postprocessing technique that allows quantification of the spatial distribution of tissue magnetic susceptibility in vivo. Contributing sources include iron, blood products, calcium, myelin, and lipid content. To evaluate the reproducibility and consistency of QSM across clinical field strengths of 1.5T and 3T and to optimize the contrast-to-noise ratio (CNR) at 1.5T through bandwidth tuning. Prospective. Sixteen healthy volunteers (10 men, 6 women; age range 24-37; mean age 27.8 ± 3.2 years). 1.5T and 3T systems from the same vendor. Four spoiled gradient echo (SPGR) sequences were designed with different acquisition bandwidths. QSM reconstruction was achieved through a nonlinear morphology-enabled dipole inversion (MEDI) algorithm employing L1 regularization. CNR was calculated in seven regions of interest (ROIs), while reproducibility and consistency of QSM measurements were evaluated through voxel-based and region-specific linear correlation analyses and Bland-Altman plots. Interclass correlation, Wilcoxon rank sum test, linear regression analysis, Bland-Altman analysis, Welch's t-test. CNR analysis showed a statistically significant (P < 0.05) increase in four out of seven ROIs for the lowest bandwidth employed with respect to the highest (25.18% increase in CNR of caudate nucleus). All sequences reported an excellent correlation across field strength and bandwidth variation (R ≥ 0.96, widest limits of agreement from -18.7 to 25.8 ppb) in the ROI-based analysis, while the correlation was found to be good for the voxel-based analysis of averaged maps (R ≥ 0.90, widest limits of agreement from -9.3 to 9.1 ppb). CNR of QSM images reconstructed from 1.5T acquisitions can be enhanced through bandwidth tuning. MEDI-based QSM reconstruction demonstrated to be reproducible and consistent both across field strengths (1.5T and 3T) and bandwidth variation. 1 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2018. © 2018 International Society for Magnetic Resonance in Medicine.

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.

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.

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.

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.

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.

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

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 white matter fractional anisotropy analysis on patients with MRI negative partial epilepsy.

PubMed

Duning, Thomas; Kellinghaus, Christoph; Mohammadi, Siawoosh; Schiffbauer, Hagen; Keller, Simon; Ringelstein, E Bernd; Knecht, Stefan; Deppe, Michael

2010-02-01

Conventional structural MRI fails to identify a cerebral lesion in 25% of patients with cryptogenic partial epilepsy (CPE). Diffusion tensor imaging is an MRI technique sensitive to microstructural abnormalities of cerebral white matter (WM) by quantification of fractional anisotropy (FA). The objectives of the present study were to identify focal FA abnormalities in patients with CPE who were deemed MRI negative during routine presurgical evaluation. Diffusion tensor imaging at 3 T was performed in 12 patients with CPE and normal conventional MRI and in 67 age matched healthy volunteers. WM integrity was compared between groups on the basis of automated voxel-wise statistics of FA maps using an analysis of covariance. Volumetric measurements from high resolution T1-weighted images were also performed. Significant FA reductions in WM regions encompassing diffuse areas of the brain were observed when all patients as a group were compared with controls. On an individual basis, voxel based analyses revealed widespread symmetrical FA reduction in CPE patients. Furthermore, asymmetrical temporal lobe FA reduction was consistently ipsilateral to the electroclinical focus. No significant correlations were found between FA alterations and clinical data. There were no differences in brain volumes of CPE patients compared with controls. Despite normal conventional MRI, WM integrity abnormalities in CPE patients extend far beyond the epileptogenic zone. Given that unilateral temporal lobe FA abnormalities were consistently observed ipsilateral to the seizure focus, analysis of temporal FA may provide an informative in vivo investigation into the localisation of the epileptogenic zone in MRI negative patients.

Identification of a common neurobiological substrate for mental illness.

PubMed

Goodkind, Madeleine; Eickhoff, Simon B; Oathes, Desmond J; Jiang, Ying; Chang, Andrew; Jones-Hagata, Laura B; Ortega, Brissa N; Zaiko, Yevgeniya V; Roach, Erika L; Korgaonkar, Mayuresh S; Grieve, Stuart M; Galatzer-Levy, Isaac; Fox, Peter T; Etkin, Amit

2015-04-01

Psychiatric diagnoses are currently distinguished based on sets of specific symptoms. However, genetic and clinical analyses find similarities across a wide variety of diagnoses, suggesting that a common neurobiological substrate may exist across mental illness. To conduct a meta-analysis of structural neuroimaging studies across multiple psychiatric diagnoses, followed by parallel analyses of 3 large-scale healthy participant data sets to help interpret structural findings in the meta-analysis. PubMed was searched to identify voxel-based morphometry studies through July 2012 comparing psychiatric patients to healthy control individuals for the meta-analysis. The 3 parallel healthy participant data sets included resting-state functional magnetic resonance imaging, a database of activation foci across thousands of neuroimaging experiments, and a data set with structural imaging and cognitive task performance data. Studies were included in the meta-analysis if they reported voxel-based morphometry differences between patients with an Axis I diagnosis and control individuals in stereotactic coordinates across the whole brain, did not present predominantly in childhood, and had at least 10 studies contributing to that diagnosis (or across closely related diagnoses). The meta-analysis was conducted on peak voxel coordinates using an activation likelihood estimation approach. We tested for areas of common gray matter volume increase or decrease across Axis I diagnoses, as well as areas differing between diagnoses. Follow-up analyses on other healthy participant data sets tested connectivity related to regions arising from the meta-analysis and the relationship of gray matter volume to cognition. Based on the voxel-based morphometry meta-analysis of 193 studies comprising 15 892 individuals across 6 diverse diagnostic groups (schizophrenia, bipolar disorder, depression, addiction, obsessive-compulsive disorder, and anxiety), we found that gray matter loss converged across diagnoses in 3 regions: the dorsal anterior cingulate, right insula, and left insula. By contrast, there were few diagnosis-specific effects, distinguishing only schizophrenia and depression from other diagnoses. In the parallel follow-up analyses of the 3 independent healthy participant data sets, we found that the common gray matter loss regions formed a tightly interconnected network during tasks and at resting and that lower gray matter in this network was associated with poor executive functioning. We identified a concordance across psychiatric diagnoses in terms of integrity of an anterior insula/dorsal anterior cingulate-based network, which may relate to executive function deficits observed across diagnoses. This concordance provides an organizing model that emphasizes the importance of shared neural substrates across psychopathology, despite likely diverse etiologies, which is currently not an explicit component of psychiatric nosology.

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.

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.

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.

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.

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

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

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.

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.

Characteristics of voxel prediction power in full-brain Granger causality analysis of fMRI data

NASA Astrophysics Data System (ADS)

Garg, Rahul; Cecchi, Guillermo A.; Rao, A. Ravishankar

2011-03-01

Functional neuroimaging research is moving from the study of "activations" to the study of "interactions" among brain regions. Granger causality analysis provides a powerful technique to model spatio-temporal interactions among brain regions. We apply this technique to full-brain fMRI data without aggregating any voxel data into regions of interest (ROIs). We circumvent the problem of dimensionality using sparse regression from machine learning. On a simple finger-tapping experiment we found that (1) a small number of voxels in the brain have very high prediction power, explaining the future time course of other voxels in the brain; (2) these voxels occur in small sized clusters (of size 1-4 voxels) distributed throughout the brain; (3) albeit small, these clusters overlap with most of the clusters identified with the non-temporal General Linear Model (GLM); and (4) the method identifies clusters which, while not determined by the task and not detectable by GLM, still influence brain activity.

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.

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.

NiftyPET: a High-throughput Software Platform for High Quantitative Accuracy and Precision PET Imaging and Analysis.

PubMed

Markiewicz, Pawel J; Ehrhardt, Matthias J; Erlandsson, Kjell; Noonan, Philip J; Barnes, Anna; Schott, Jonathan M; Atkinson, David; Arridge, Simon R; Hutton, Brian F; Ourselin, Sebastien

2018-01-01

We present a standalone, scalable and high-throughput software platform for PET image reconstruction and analysis. We focus on high fidelity modelling of the acquisition processes to provide high accuracy and precision quantitative imaging, especially for large axial field of view scanners. All the core routines are implemented using parallel computing available from within the Python package NiftyPET, enabling easy access, manipulation and visualisation of data at any processing stage. The pipeline of the platform starts from MR and raw PET input data and is divided into the following processing stages: (1) list-mode data processing; (2) accurate attenuation coefficient map generation; (3) detector normalisation; (4) exact forward and back projection between sinogram and image space; (5) estimation of reduced-variance random events; (6) high accuracy fully 3D estimation of scatter events; (7) voxel-based partial volume correction; (8) region- and voxel-level image analysis. We demonstrate the advantages of this platform using an amyloid brain scan where all the processing is executed from a single and uniform computational environment in Python. The high accuracy acquisition modelling is achieved through span-1 (no axial compression) ray tracing for true, random and scatter events. Furthermore, the platform offers uncertainty estimation of any image derived statistic to facilitate robust tracking of subtle physiological changes in longitudinal studies. The platform also supports the development of new reconstruction and analysis algorithms through restricting the axial field of view to any set of rings covering a region of interest and thus performing fully 3D reconstruction and corrections using real data significantly faster. All the software is available as open source with the accompanying wiki-page and test data.

LRP-1 polymorphism is associated with global and regional amyloid load in Alzheimer's disease in humans in-vivo

PubMed Central

Grimmer, Timo; Goldhardt, Oliver; Guo, Liang-Hao; Yousefi, Behrooz H.; Förster, Stefan; Drzezga, Alexander; Sorg, Christian; Alexopoulos, Panagiotis; Förstl, Hans; Kurz, Alexander; Perneczky, Robert

2014-01-01

Objective Impaired amyloid clearance has been proposed to contribute to β-amyloid deposition in sporadic late-onset Alzheimer's disease (AD). Low density lipoprotein receptor-related protein 1 (LRP-1) is involved in the active outward transport of β-amyloid across the blood–brain barrier (BBB). The C667T polymorphism (rs1799986) of the LRP-1 gene has been inconsistently associated with AD in genetic studies. We aimed to elucidate the association of this polymorphism with in-vivo brain amyloid load of AD patients using amyloid PET with [11C]PiB. Materials and methods 72 patients with very mild to moderate AD were examined with amyloid PET and C667T polymorphism was obtained using TaqMan PCR assays. The association of C667T polymorphism with global and regional amyloid load was calculated using linear regression and voxel based analysis, respectively. The effect of the previously identified modulator of amyloid uptake, the apolipoprotein E genotype, on this association was also determined. Results The regression analysis between amyloid load and C667T polymorphism was statistically significant (p = 0.046, β = 0.236). In an additional analysis ApoE genotype and gender were identified to explain further variability of amyloid load. Voxel based analysis revealed a significant (p < 0.05) association between C667T polymorphism and amyloid uptake in the temporo-parietal cortex bilaterally. ApoE did not interact significantly with the LRP-1 polymorphism. Discussion In conclusion, C667T polymorphism of LRP-1 is moderately but significantly associated with global and regional amyloid deposition in AD. The relationship appears to be independent of the ApoE genotype. This finding is compatible with the hypothesis that impaired amyloid clearance contributes to amyloid deposition in late-onset sporadic AD. PMID:24596678

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

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.

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

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.

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.

Relationship between aging and T1 relaxation time in deep gray matter: A voxel-based analysis.

PubMed

Okubo, Gosuke; Okada, Tomohisa; Yamamoto, Akira; Fushimi, Yasutaka; Okada, Tsutomu; Murata, Katsutoshi; Togashi, Kaori

2017-09-01

To investigate age-related changes in T 1 relaxation time in deep gray matter structures in healthy volunteers using magnetization-prepared 2 rapid acquisition gradient echoes (MP2RAGE). In all, 70 healthy volunteers (aged 20-76, mean age 42.6 years) were scanned at 3T magnetic resonance imaging (MRI). A MP2RAGE sequence was employed to quantify T 1 relaxation times. After the spatial normalization of T 1 maps with the diffeomorphic anatomical registration using the exponentiated Lie algebra algorithm, voxel-based regression analysis was conducted. In addition, linear and quadratic regression analyses of regions of interest (ROIs) were also performed. With aging, voxel-based analysis (VBA) revealed significant T 1 value decreases in the ventral-inferior putamen, nucleus accumbens, and amygdala, whereas T 1 values significantly increased in the thalamus and white matter as well (P < 0.05 at cluster level, false discovery rate). ROI analysis revealed that T 1 values in the nucleus accumbens linearly decreased with aging (P = 0.0016), supporting the VBA result. T 1 values in the thalamus (P < 0.0001), substantia nigra (P = 0.0003), and globus pallidus (P < 0.0001) had a best fit to quadratic curves, with the minimum T 1 values observed between 30 and 50 years of age. Age-related changes in T 1 relaxation time vary by location in deep gray matter. 2 Technical Efficacy: Stage 2 J. MAGN. RESON. IMAGING 2017;46:724-731. © 2017 International Society for Magnetic Resonance in Medicine.

Microstructural white matter tract alteration in Prader-Willi syndrome: A diffusion tensor imaging study.

PubMed

Rice, Lauren J; Lagopoulos, Jim; Brammer, Michael; Einfeld, Stewart L

2017-09-01

Prader-Willi Syndrome (PWS) is a genetic disorder characterized by infantile hypotonia, hyperphagia, hypogonadism, growth hormone deficiency, intellectual disability, and severe emotional and behavioral problems. The brain mechanisms that underpin these disturbances are unknown. Diffusion tensor imaging (DTI) enables in vivo investigation of the microstructural integrity of white matter pathways. To date, only one study has used DTI to examine white matter alterations in PWS. However, that study used selected regions of interest, rather than a whole brain analysis. In the present study, we used diffusion tensor and magnetic resonance (T 1-weighted) imaging to examine microstructural white matter changes in 15 individuals with PWS (17-30 years) and 15 age-and-gender-matched controls. Whole-brain voxel-wise statistical analysis of FA was carried out using tract-based spatial statistics (TBSS). Significantly decreased fractional anisotropy was found localized to the left hemisphere in individuals with PWS within the splenium of the corpus callosum, the internal capsule including the posterior thalamic radiation and the inferior frontal occipital fasciculus (IFOF). Reduced integrity of these white matter pathways in individuals with PWS may relate to orientating attention, emotion recognition, semantic processing, and sensorimotor dysfunction. © 2017 Wiley Periodicals, Inc.

Voxel-by-voxel analysis of brain SPECT perfusion in Fibromyalgia

NASA Astrophysics Data System (ADS)

Guedj, Eric; Taïeb, David; Cammilleri, Serge; Lussato, David; de Laforte, Catherine; Niboyet, Jean; Mundler, Olivier

2007-02-01

We evaluated brain perfusion SPECT at rest, without noxious stiumuli, in a homogeneous group of hyperalgesic FM patients. We performed a voxel-based analysis in comparison to a control group, matched for age and gender. Under such conditions, we made the assumption that significant cerebral perfusion abnormalities could be demonstrated, evidencing altered cerebral processing associated with spontaneous pain in FM patients. The secondary objective was to study the reversibility and the prognostic value of such possible perfusion abnormalities under specific treatment. Eighteen hyperalgesic FM women (mean age 48 yr; range 25-63 yr; ACR criteria) and 10 healthy women matched for age were enrolled in the study. A voxel-by-voxel group analysis was performed using SPM2 ( p<0.05, corrected for multiple comparisons). All brain SPECT were performed before any change was made in therapy in the pain care unit. A second SPECT was performed a month later after specific treatment by Ketamine. Compared to control subjects, we observed individual brain SPECT abnormalities in FM patients, confirmed by SPM2 analysis with hyperperfusion of the somatosensory cortex and hypoperfusion of the frontal, cingulate, medial temporal and cerebellar cortices. We also found that a medial frontal and anterior cingulate hypoperfusions were highly predictive (PPV=83%; NPV=91%) of non-response on Ketamine, and that only responders showed significant modification of brain perfusion, after treatment. In the present study performed without noxious stimuli in hyperalgesic FM patients, we found significant hyperperfusion in regions of the brain known to be involved in sensory dimension of pain processing and significant hypoperfusion in areas assumed to be associated with the affective dimension. As current pharmacological and non-pharmacological therapies act differently on both components of pain, we hypothesize that SPECT could be a valuable and readily available tool to guide individual therapeutic strategy and provide objective follow-up of pain-processing recovery under treatment.

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.

Adaptive Kalman filtering for real-time mapping of the visual field

PubMed Central

Ward, B. Douglas; Janik, John; Mazaheri, Yousef; Ma, Yan; DeYoe, Edgar A.

2013-01-01

This paper demonstrates the feasibility of real-time mapping of the visual field for clinical applications. Specifically, three aspects of this problem were considered: (1) experimental design, (2) statistical analysis, and (3) display of results. Proper experimental design is essential to achieving a successful outcome, particularly for real-time applications. A random-block experimental design was shown to have less sensitivity to measurement noise, as well as greater robustness to error in modeling of the hemodynamic impulse response function (IRF) and greater flexibility than common alternatives. In addition, random encoding of the visual field allows for the detection of voxels that are responsive to multiple, not necessarily contiguous, regions of the visual field. Due to its recursive nature, the Kalman filter is ideally suited for real-time statistical analysis of visual field mapping data. An important feature of the Kalman filter is that it can be used for nonstationary time series analysis. The capability of the Kalman filter to adapt, in real time, to abrupt changes in the baseline arising from subject motion inside the scanner and other external system disturbances is important for the success of clinical applications. The clinician needs real-time information to evaluate the success or failure of the imaging run and to decide whether to extend, modify, or terminate the run. Accordingly, the analytical software provides real-time displays of (1) brain activation maps for each stimulus segment, (2) voxel-wise spatial tuning profiles, (3) time plots of the variability of response parameters, and (4) time plots of activated volume. PMID:22100663

Relationship between white matter lesions and regional cerebral blood flow changes during longitudinal follow up in Alzheimer's disease.

PubMed

Hanaoka, Takuya; Kimura, Noriyuki; Aso, Yasuhiro; Takemaru, Makoto; Kimura, Yuki; Ishibashi, Masato; Matsubara, Etsuro

2016-07-01

The aim of the present study was to evaluate the relationship between baseline white matter lesions (WML) and changes in regional cerebral blood flow during longitudinal follow up of patients with Alzheimer's disease (AD). A total of 38 patients with AD were included in the study (16 men, 22 women; mean age 77.8 years). All patients were evaluated using the Mini-Mental State Examination and brain perfusion single-photon emission computed tomography at baseline with an approximately 2-year follow up. The patients were divided into two subgroups according to the presence of WML on magnetic resonance imaging. Single-photon emission computed tomography data were analyzed using a voxel-by-voxel group analysis with Statistical Parametric Mapping 8 and region of interest analysis using FineSRT. Changes in Mini-Mental State Examination scores and regional cerebral blood flow were analyzed using the Wilcoxon signed-rank test. Mean Mini-Mental State Examination scores in AD patients with WML significantly decreased from 19.4 ± 4.8 to 15.5 ± 6.5 (P = 0.003). Statistical Parametric Mapping 8 and FineSRT analysis showed more severe and widespread regional cerebral blood flow reduction, mainly in the frontal and mesial temporal regions in AD patients with WML compared with those without WML. Baseline WML could predict a rapid progression of cognitive and brain functional impairment during longitudinal follow up in AD. Geriatr Gerontol Int 2016; 16: 836-842. © 2015 Japan Geriatrics Society.

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.

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.

Multiclass fMRI data decoding and visualization using supervised self-organizing maps.

PubMed

Hausfeld, Lars; Valente, Giancarlo; Formisano, Elia

2014-08-01

When multivariate pattern decoding is applied to fMRI studies entailing more than two experimental conditions, a most common approach is to transform the multiclass classification problem into a series of binary problems. Furthermore, for decoding analyses, classification accuracy is often the only outcome reported although the topology of activation patterns in the high-dimensional features space may provide additional insights into underlying brain representations. Here we propose to decode and visualize voxel patterns of fMRI datasets consisting of multiple conditions with a supervised variant of self-organizing maps (SSOMs). Using simulations and real fMRI data, we evaluated the performance of our SSOM-based approach. Specifically, the analysis of simulated fMRI data with varying signal-to-noise and contrast-to-noise ratio suggested that SSOMs perform better than a k-nearest-neighbor classifier for medium and large numbers of features (i.e. 250 to 1000 or more voxels) and similar to support vector machines (SVMs) for small and medium numbers of features (i.e. 100 to 600voxels). However, for a larger number of features (>800voxels), SSOMs performed worse than SVMs. When applied to a challenging 3-class fMRI classification problem with datasets collected to examine the neural representation of three human voices at individual speaker level, the SSOM-based algorithm was able to decode speaker identity from auditory cortical activation patterns. Classification performances were similar between SSOMs and other decoding algorithms; however, the ability to visualize decoding models and underlying data topology of SSOMs promotes a more comprehensive understanding of classification outcomes. We further illustrated this visualization ability of SSOMs with a re-analysis of a dataset examining the representation of visual categories in the ventral visual cortex (Haxby et al., 2001). This analysis showed that SSOMs could retrieve and visualize topography and neighborhood relations of the brain representation of eight visual categories. We conclude that SSOMs are particularly suited for decoding datasets consisting of more than two classes and are optimally combined with approaches that reduce the number of voxels used for classification (e.g. region-of-interest or searchlight approaches). Copyright © 2014. Published by Elsevier Inc.

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%.

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.

Large-scale brain network associated with creative insight: combined voxel-based morphometry and resting-state functional connectivity analyses.

PubMed

Ogawa, Takeshi; Aihara, Takatsugu; Shimokawa, Takeaki; Yamashita, Okito

2018-04-24

Creative insight occurs with an "Aha!" experience when solving a difficult problem. Here, we investigated large-scale networks associated with insight problem solving. We recruited 232 healthy participants aged 21-69 years old. Participants completed a magnetic resonance imaging study (MRI; structural imaging and a 10 min resting-state functional MRI) and an insight test battery (ITB) consisting of written questionnaires (matchstick arithmetic task, remote associates test, and insight problem solving task). To identify the resting-state functional connectivity (RSFC) associated with individual creative insight, we conducted an exploratory voxel-based morphometry (VBM)-constrained RSFC analysis. We identified positive correlations between ITB score and grey matter volume (GMV) in the right insula and middle cingulate cortex/precuneus, and a negative correlation between ITB score and GMV in the left cerebellum crus 1 and right supplementary motor area. We applied seed-based RSFC analysis to whole brain voxels using the seeds obtained from the VBM and identified insight-positive/negative connections, i.e. a positive/negative correlation between the ITB score and individual RSFCs between two brain regions. Insight-specific connections included motor-related regions whereas creative-common connections included a default mode network. Our results indicate that creative insight requires a coupling of multiple networks, such as the default mode, semantic and cerebral-cerebellum networks.

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.

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.

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.

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

A generalized parametric response mapping method for analysis of multi-parametric imaging: A feasibility study with application to glioblastoma.

PubMed

Lausch, Anthony; Yeung, Timothy Pok-Chi; Chen, Jeff; Law, Elton; Wang, Yong; Urbini, Benedetta; Donelli, Filippo; Manco, Luigi; Fainardi, Enrico; Lee, Ting-Yim; Wong, Eugene

2017-11-01

Parametric response map (PRM) analysis of functional imaging has been shown to be an effective tool for early prediction of cancer treatment outcomes and may also be well-suited toward guiding personalized adaptive radiotherapy (RT) strategies such as sub-volume boosting. However, the PRM method was primarily designed for analysis of longitudinally acquired pairs of single-parameter image data. The purpose of this study was to demonstrate the feasibility of a generalized parametric response map analysis framework, which enables analysis of multi-parametric data while maintaining the key advantages of the original PRM method. MRI-derived apparent diffusion coefficient (ADC) and relative cerebral blood volume (rCBV) maps acquired at 1 and 3-months post-RT for 19 patients with high-grade glioma were used to demonstrate the algorithm. Images were first co-registered and then standardized using normal tissue image intensity values. Tumor voxels were then plotted in a four-dimensional Cartesian space with coordinate values equal to a voxel's image intensity in each of the image volumes and an origin defined as the multi-parametric mean of normal tissue image intensity values. Voxel positions were orthogonally projected onto a line defined by the origin and a pre-determined response vector. The voxels are subsequently classified as positive, negative or nil, according to whether projected positions along the response vector exceeded a threshold distance from the origin. The response vector was selected by identifying the direction in which the standard deviation of tumor image intensity values was maximally different between responding and non-responding patients within a training dataset. Voxel classifications were visualized via familiar three-class response maps and then the fraction of tumor voxels associated with each of the classes was investigated for predictive utility analogous to the original PRM method. Independent PRM and MPRM analyses of the contrast-enhancing lesion (CEL) and a 1 cm shell of surrounding peri-tumoral tissue were performed. Prediction using tumor volume metrics was also investigated. Leave-one-out cross validation (LOOCV) was used in combination with permutation testing to assess preliminary predictive efficacy and estimate statistically robust P-values. The predictive endpoint was overall survival (OS) greater than or equal to the median OS of 18.2 months. Single-parameter PRM and multi-parametric response maps (MPRMs) were generated for each patient and used to predict OS via the LOOCV. Tumor volume metrics (P ≥ 0.071 ± 0.01) and single-parameter PRM analyses (P ≥ 0.170 ± 0.01) were not found to be predictive of OS within this study. MPRM analysis of the peri-tumoral region but not the CEL was found to be predictive of OS with a classification sensitivity, specificity and accuracy of 80%, 100%, and 89%, respectively (P = 0.001 ± 0.01). The feasibility of a generalized MPRM analysis framework was demonstrated with improved prediction of overall survival compared to the original single-parameter method when applied to a glioblastoma dataset. The proposed algorithm takes the spatial heterogeneity in multi-parametric response into consideration and enables visualization. MPRM analysis of peri-tumoral regions was shown to have predictive potential supporting further investigation of a larger glioblastoma dataset. © 2017 American Association of Physicists in Medicine.

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

Atlas-guided volumetric diffuse optical tomography enhanced by generalized linear model analysis to image risk decision-making responses in young adults.

PubMed

Lin, Zi-Jing; Li, Lin; Cazzell, Mary; Liu, Hanli

2014-08-01

Diffuse optical tomography (DOT) is a variant of functional near infrared spectroscopy and has the capability of mapping or reconstructing three dimensional (3D) hemodynamic changes due to brain activity. Common methods used in DOT image analysis to define brain activation have limitations because the selection of activation period is relatively subjective. General linear model (GLM)-based analysis can overcome this limitation. In this study, we combine the atlas-guided 3D DOT image reconstruction with GLM-based analysis (i.e., voxel-wise GLM analysis) to investigate the brain activity that is associated with risk decision-making processes. Risk decision-making is an important cognitive process and thus is an essential topic in the field of neuroscience. The Balloon Analog Risk Task (BART) is a valid experimental model and has been commonly used to assess human risk-taking actions and tendencies while facing risks. We have used the BART paradigm with a blocked design to investigate brain activations in the prefrontal and frontal cortical areas during decision-making from 37 human participants (22 males and 15 females). Voxel-wise GLM analysis was performed after a human brain atlas template and a depth compensation algorithm were combined to form atlas-guided DOT images. In this work, we wish to demonstrate the excellence of using voxel-wise GLM analysis with DOT to image and study cognitive functions in response to risk decision-making. Results have shown significant hemodynamic changes in the dorsal lateral prefrontal cortex (DLPFC) during the active-choice mode and a different activation pattern between genders; these findings correlate well with published literature in functional magnetic resonance imaging (fMRI) and fNIRS studies. Copyright © 2014 The Authors. Human Brain Mapping Published by Wiley Periodicals, Inc.

[Utility of axial images in an early Alzheimer disease diagnosis support system (VSRAD)].

PubMed

Goto, Masami; Aoki, Shigeki; Abe, Osamu; Masumoto, Tomohiko; Watanabe, Yasushi; Satake, Yoshiroh; Nishida, Katsuji; Ino, Kenji; Yano, Keiichi; Iida, Kyohhito; Mima, Kazuo; Ohtomo, Kuni

2006-09-20

In recent years, voxel-based morphometry (VBM) has become a popular tool for the early diagnosis of Alzheimer disease. The Voxel-Based Specific Regional Analysis System for Alzheimer's Disease (VSRAD), a VBM system that uses MRI, has been reported to be clinically useful. The able-bodied person database (DB) of VSRAD, which employs sagittal plane imaging, is not suitable for analysis by axial plane imaging. However, axial plane imaging is useful for avoiding motion artifacts from the eyeball. Therefore, we created an able-bodied person DB by axial plane imaging and examined its utility. We also analyzed groups of able-bodied persons and persons with dementia by axial plane imaging and reviewed the validity. After using the DB of axial plane imaging, the Z-score of the intrahippocampal region improved by 8 in 13 instances. In all brains, the Z-score improved by 13 in all instances.

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.

Alterations in white matter volume and its correlation with neuropsychological scales in patients with Alzheimer's disease: a DARTEL-based voxel-based morphometry study.

PubMed

Moon, Chung-Man; Shin, Il-Seon; Jeong, Gwang-Woo

2017-02-01

Background Non-invasive imaging markers can be used to diagnose Alzheimer's disease (AD) in its early stages, but an optimized quantification analysis to measure the brain integrity has been less studied. Purpose To evaluate white matter volume change and its correlation with neuropsychological scales in patients with AD using a diffeomorphic anatomical registration through exponentiated lie algebra (DARTEL)-based voxel-based morphometry (VBM). Material and Methods The 21 participants comprised 11 patients with AD and 10 age-matched healthy controls. High-resolution magnetic resonance imaging (MRI) data were processed by VBM analysis based on DARTEL algorithm. Results The patients showed significant white matter volume reductions in the posterior limb of the internal capsule, cerebral peduncle of the midbrain, and parahippocampal gyrus compared to healthy controls. In correlation analysis, the parahippocampal volume was positively correlated with the Korean-mini mental state examination score in AD. Conclusion This study provides an evidence for localized white matter volume deficits in conjunction with cognitive dysfunction in AD. These findings would be helpful to understand the neuroanatomical mechanisms in AD and to robust the diagnostic accuracy for AD.

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.

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.

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.

The common traits of the ACC and PFC in anxiety disorders in the DSM-5: meta-analysis of voxel-based morphometry studies.

PubMed

Shang, Jing; Fu, Yuchuan; Ren, Zhengjia; Zhang, Tao; Du, Mingying; Gong, Qiyong; Lui, Su; Zhang, Wei

2014-01-01

The core domains of social anxiety disorder (SAD), generalized anxiety disorder (GAD), panic disorder (PD) with and without agoraphobia (GA), and specific phobia (SP) are cognitive and physical symptoms that are related to the experience of fear and anxiety. It remains unclear whether these highly comorbid conditions that constitute the anxiety disorder subgroups of the Diagnostic and Statistical Manual for Mental Disorders--Fifth Edition (DSM-5) represent distinct disorders or alternative presentations of a single underlying pathology. A systematic search of voxel-based morphometry (VBM) studies of SAD, GAD, PD, GA, and SP was performed with an effect-size signed differential mapping (ES-SDM) meta-analysis to estimate the clusters of significant gray matter differences between patients and controls. Twenty-four studies were eligible for inclusion in the meta-analysis. Reductions in the right anterior cingulate gyrus and the left inferior frontal gyrus gray matter volumes (GMVs) were noted in patients with anxiety disorders when potential confounders, such as comorbid major depressive disorder (MDD), age, and antidepressant use were controlled for. We also demonstrated increased GMVs in the right dorsolateral prefrontal cortex (DLPFC) in comorbid depression-anxiety (CDA), drug-naïve and adult patients. Furthermore, we identified a reduced left middle temporal gyrus and right precentral gyrus in anxiety patients without comorbid MDD. Our findings indicate that a reduced volume of the right ventral anterior cingulate gyrus and left inferior frontal gyrus is common in anxiety disorders and is independent of comorbid depression, medication use, and age. This generic effect supports the notion that the four types of anxiety disorders have a clear degree of overlap that may reflect shared etiological mechanisms. The results are consistent with neuroanatomical DLPFC models of physiological responses, such as worry and fear, and the importance of the ventral anterior cingulate (ACC)/medial prefrontal cortex (mPFC) in mediating anxiety symptoms.

Evaluation of structural connectivity changes in betel-quid chewers using generalized q-sampling MRI.

PubMed

Weng, Jun-Cheng; Kao, Te-Wei; Huang, Guo-Joe; Tyan, Yeu-Sheng; Tseng, Hsien-Chun; Ho, Ming-Chou

2017-07-01

Betel quid (BQ) is a common addictive substance in many Asian countries. However, few studies have focused on the influences of BQ on the brain. It remains unclear how BQ can affect structural brain abnormalities in BQ chewers. We aimed to use generalized q-sampling imaging (GQI) to evaluate the impact of the neurological structure of white matter caused by BQ. The study population comprised 16 BQ chewers, 15 tobacco and alcohol controls, and 17 healthy controls. We used GQI with voxel-based statistical analysis (VBA) to evaluate structural brain and connectivity abnormalities in the BQ chewers compared to the tobacco and alcohol controls and the healthy controls. Graph theoretical analysis (GTA) and network-based statistical (NBS) analysis were also performed to identify the structural network differences among the three groups. Using GQI, we found increases in diffusion anisotropy in the right anterior cingulate cortex (ACC), the midbrain, the bilateral angular gyrus, the right superior temporal gyrus (rSTG), the bilateral superior occipital gyrus, the left middle occipital gyrus, the bilateral superior and inferior parietal lobule, and the bilateral postcentral and precentral gyrus in the BQ chewers when compared to the tobacco and alcohol controls and the healthy controls. In GTA and NBS analyses, we found more connections in connectivity among the BQ chewers, particularly in the bilateral anterior cingulum. Our results provided further evidence indicating that BQ chewing may lead to brain structure and connectivity changes in BQ chewers.

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.

Differentiation of Recurrent Glioblastoma from Delayed Radiation Necrosis by Using Voxel-based Multiparametric Analysis of MR Imaging Data.

PubMed

Yoon, Ra Gyoung; Kim, Ho Sung; Koh, Myeong Ju; Shim, Woo Hyun; Jung, Seung Chai; Kim, Sang Joon; Kim, Jeong Hoon

2017-10-01

Purpose To assess a volume-weighted voxel-based multiparametric (MP) clustering method as an imaging biomarker to differentiate recurrent glioblastoma from delayed radiation necrosis. Materials and Methods The institutional review board approved this retrospective study and waived the informed consent requirement. Seventy-five patients with pathologic analysis-confirmed recurrent glioblastoma (n = 42) or radiation necrosis (n = 33) who presented with enlarged contrast material-enhanced lesions at magnetic resonance (MR) imaging after they completed concurrent chemotherapy and radiation therapy were enrolled. The diagnostic performance of the total MP cluster score was determined by using the area under the receiver operating characteristic curve (AUC) with cross-validation and compared with those of single parameter measurements (10% histogram cutoffs of apparent diffusion coefficient [ADC10] or 90% histogram cutoffs of normalized cerebral blood volume and initial time-signal intensity AUC). Results Receiver operating characteristic curve analysis showed that an AUC for differentiating recurrent glioblastoma from delayed radiation necrosis was highest in the total MP cluster score and lowest for ADC10 for both readers. The total MP cluster score had significantly better diagnostic accuracy than any single parameter (corrected P = .001-.039 for reader 1; corrected P = .005-.041 for reader 2). The total MP cluster score was the best predictor of recurrent glioblastoma (cross-validated AUCs, 0.942-0.946 for both readers), with a sensitivity of 95.2% for reader 1 and 97.6% for reader 2. Conclusion Quantitative analysis with volume-weighted voxel-based MP clustering appears to be superior to the use of single imaging parameters to differentiate recurrent glioblastoma from delayed radiation necrosis. © RSNA, 2017 Online supplemental material is available for this article.

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.

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.

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.

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.

The sequential structure of brain activation predicts skill.

PubMed

Anderson, John R; Bothell, Daniel; Fincham, Jon M; Moon, Jungaa

2016-01-29

In an fMRI study, participants were trained to play a complex video game. They were scanned early and then again after substantial practice. While better players showed greater activation in one region (right dorsal striatum) their relative skill was better diagnosed by considering the sequential structure of whole brain activation. Using a cognitive model that played this game, we extracted a characterization of the mental states that are involved in playing a game and the statistical structure of the transitions among these states. There was a strong correspondence between this measure of sequential structure and the skill of different players. Using multi-voxel pattern analysis, it was possible to recognize, with relatively high accuracy, the cognitive states participants were in during particular scans. We used the sequential structure of these activation-recognized states to predict the skill of individual players. These findings indicate that important features about information-processing strategies can be identified from a model-based analysis of the sequential structure of brain activation. Copyright © 2015 Elsevier Ltd. All rights reserved.

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

A new leg voxel model in two different positions for simulation of the non-uniform distribution of (241)Am in leg bones.

PubMed

Khalaf, Majid; Brey, Richard R; Meldrum, Jeff

2013-01-01

A new leg voxel model in two different positions (straight and bent) has been developed for in vivo measurement calibration purposes. This voxel phantom is a representation of a human leg that may provide a substantial enhancement to Monte Carlo modeling because it more accurately models different geometric leg positions and the non-uniform distribution of Am throughout the leg bones instead of assuming a one-position geometry and a uniform distribution of radionuclides. This was accomplished by performing a radiochemical analysis on small sections of the leg bones from the U.S. Transuranium and Uranium Registries (USTUR) case 0846. USTUR case 0846 represents an individual who was repeatedly contaminated by Am via chronic inhalation. To construct the voxel model, high resolution (2 mm) computed tomography (CT) images of the USTUR case 0846 leg were obtained in different positions. Thirty-six (36) objects (universes) were segmented manually from the CT images using 3D-Doctor software. Bones were divided into 30 small sections with an assigned weight exactly equal to the weight of bone sections obtained from radiochemical analysis of the USTUR case 0846 leg. The segmented images were then converted into a boundary file, and the Human Monitoring Laboratory (HML) voxelizer was used to convert the boundary file into the leg voxel phantom. Excluding the surrounding air regions, the straight leg phantom consists of 592,023 voxels, while the bent leg consists of 337,567 voxels. The resulting leg voxel model is now ready for use as an MCNPX input file to simulate in vivo measurement of bone-seeking radionuclides.

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

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.

Validation of voxel-based morphometry (VBM) based on MRI

NASA Astrophysics Data System (ADS)

Yang, Xueyu; Chen, Kewei; Guo, Xiaojuan; Yao, Li

2007-03-01

Voxel-based morphometry (VBM) is an automated and objective image analysis technique for detecting differences in regional concentration or volume of brain tissue composition based on structural magnetic resonance (MR) images. VBM has been used widely to evaluate brain morphometric differences between different populations, but there isn't an evaluation system for its validation until now. In this study, a quantitative and objective evaluation system was established in order to assess VBM performance. We recruited twenty normal volunteers (10 males and 10 females, age range 20-26 years, mean age 22.6 years). Firstly, several focal lesions (hippocampus, frontal lobe, anterior cingulate, back of hippocampus, back of anterior cingulate) were simulated in selected brain regions using real MRI data. Secondly, optimized VBM was performed to detect structural differences between groups. Thirdly, one-way ANOVA and post-hoc test were used to assess the accuracy and sensitivity of VBM analysis. The results revealed that VBM was a good detective tool in majority of brain regions, even in controversial brain region such as hippocampus in VBM study. Generally speaking, much more severity of focal lesion was, better VBM performance was. However size of focal lesion had little effects on VBM analysis.

A multiscale approach for the reconstruction of the fiber architecture of the human brain based on 3D-PLI

PubMed Central

Reckfort, Julia; Wiese, Hendrik; Pietrzyk, Uwe; Zilles, Karl; Amunts, Katrin; Axer, Markus

2015-01-01

Structural connectivity of the brain can be conceptionalized as a multiscale organization. The present study is built on 3D-Polarized Light Imaging (3D-PLI), a neuroimaging technique targeting the reconstruction of nerve fiber orientations and therefore contributing to the analysis of brain connectivity. Spatial orientations of the fibers are derived from birefringence measurements of unstained histological sections that are interpreted by means of a voxel-based analysis. This implies that a single fiber orientation vector is obtained for each voxel, which reflects the net effect of all comprised fibers. We have utilized two polarimetric setups providing an object space resolution of 1.3 μm/px (microscopic setup) and 64 μm/px (macroscopic setup) to carry out 3D-PLI and retrieve fiber orientations of the same tissue samples, but at complementary voxel sizes (i.e., scales). The present study identifies the main sources which cause a discrepancy of the measured fiber orientations observed when measuring the same sample with the two polarimetric systems. As such sources the differing optical resolutions and diverging retardances of the implemented waveplates were identified. A methodology was implemented that enables the compensation of measured different systems' responses to the same birefringent sample. This opens up new ways to conduct multiscale analysis in brains by means of 3D-PLI and to provide a reliable basis for the transition between different scales of the nerve fiber architecture. PMID:26388744

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.

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).

The influence of voxel size on atom probe tomography data.

PubMed

Torres, K L; Daniil, M; Willard, M A; Thompson, G B

2011-05-01

A methodology for determining the optimal voxel size for phase thresholding in nanostructured materials was developed using an atom simulator and a model system of a fixed two-phase composition and volume fraction. The voxel size range was banded by the atom count within each voxel. Some voxel edge lengths were found to be too large, resulting in an averaging of compositional fluctuations; others were too small with concomitant decreases in the signal-to-noise ratio for phase identification. The simulated methodology was then applied to the more complex experimentally determined data set collected from a (Co(0.95)Fe(0.05))(88)Zr(6)Hf(1)B(4)Cu(1) two-phase nanocomposite alloy to validate the approach. In this alloy, Zr and Hf segregated to an intergranular amorphous phase while Fe preferentially segregated to a crystalline phase during the isothermal annealing step that promoted primary crystallization. The atom probe data analysis of the volume fraction was compared to transmission electron microscopy (TEM) dark-field imaging analysis and a lever rule analysis of the volume fraction within the amorphous and crystalline phases of the ribbon. Copyright © 2011 Elsevier B.V. All rights reserved.

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.

Tensor Based Representation and Analysis of Diffusion-Weighted Magnetic Resonance Images

ERIC Educational Resources Information Center

Barmpoutis, Angelos

2009-01-01

Cartesian tensor bases have been widely used to model spherical functions. In medical imaging, tensors of various orders can approximate the diffusivity function at each voxel of a diffusion-weighted MRI data set. This approximation produces tensor-valued datasets that contain information about the underlying local structure of the scanned tissue.…

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

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.

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.

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.

The neuronal correlates of intranasal trigeminal function – An ALE meta-analysis of human functional brain imaging data

PubMed Central

Albrecht, Jessica; Kopietz, Rainer; Frasnelli, Johannes; Wiesmann, Martin; Hummel, Thomas; Lundström, Johan N.

2009-01-01

Almost every odor we encounter in daily life has the capacity to produce a trigeminal sensation. Surprisingly, few functional imaging studies exploring human neuronal correlates of intranasal trigeminal function exist, and results are to some degree inconsistent. We utilized activation likelihood estimation (ALE), a quantitative voxel-based meta-analysis tool, to analyze functional imaging data (fMRI/PET) following intranasal trigeminal stimulation with carbon dioxide (CO2), a stimulus known to exclusively activate the trigeminal system. Meta-analysis tools are able to identify activations common across studies, thereby enabling activation mapping with higher certainty. Activation foci of nine studies utilizing trigeminal stimulation were included in the meta-analysis. We found significant ALE scores, thus indicating consistent activation across studies, in the brainstem, ventrolateral posterior thalamic nucleus, anterior cingulate cortex, insula, precentral gyrus, as well as in primary and secondary somatosensory cortices – a network known for the processing of intranasal nociceptive stimuli. Significant ALE values were also observed in the piriform cortex, insula, and the orbitofrontal cortex, areas known to process chemosensory stimuli, and in association cortices. Additionally, the trigeminal ALE statistics were directly compared with ALE statistics originating from olfactory stimulation, demonstrating considerable overlap in activation. In conclusion, the results of this meta-analysis map the human neuronal correlates of intranasal trigeminal stimulation with high statistical certainty and demonstrate that the cortical areas recruited during the processing of intranasal CO2 stimuli include those outside traditional trigeminal areas. Moreover, through illustrations of the considerable overlap between brain areas that process trigeminal and olfactory information; these results demonstrate the interconnectivity of flavor processing. PMID:19913573

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.

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.

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.

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.

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.

Automated liver sampling using a gradient dual-echo Dixon-based technique.

PubMed

Bashir, Mustafa R; Dale, Brian M; Merkle, Elmar M; Boll, Daniel T

2012-05-01

Magnetic resonance spectroscopy of the liver requires input from a physicist or physician at the time of acquisition to insure proper voxel selection, while in multiecho chemical shift imaging, numerous regions of interest must be manually selected in order to ensure analysis of a representative portion of the liver parenchyma. A fully automated technique could improve workflow by selecting representative portions of the liver prior to human analysis. Complete volumes from three-dimensional gradient dual-echo acquisitions with two-point Dixon reconstruction acquired at 1.5 and 3 T were analyzed in 100 subjects, using an automated liver sampling algorithm, based on ratio pairs calculated from signal intensity image data as fat-only/water-only and log(in-phase/opposed-phase) on a voxel-by-voxel basis. Using different gridding variations of the algorithm, the average correct liver volume samples ranged from 527 to 733 mL. The average percentage of sample located within the liver ranged from 95.4 to 97.1%, whereas the average incorrect volume selected was 16.5-35.4 mL (2.9-4.6%). Average run time was 19.7-79.0 s. The algorithm consistently selected large samples of the hepatic parenchyma with small amounts of erroneous extrahepatic sampling, and run times were feasible for execution on an MRI system console during exam acquisition. Copyright © 2011 Wiley Periodicals, Inc.

Sparse representation based biomarker selection for schizophrenia with integrated analysis of fMRI and SNPs.

PubMed

Cao, Hongbao; Duan, Junbo; Lin, Dongdong; Shugart, Yin Yao; Calhoun, Vince; Wang, Yu-Ping

2014-11-15

Integrative analysis of multiple data types can take advantage of their complementary information and therefore may provide higher power to identify potential biomarkers that would be missed using individual data analysis. Due to different natures of diverse data modality, data integration is challenging. Here we address the data integration problem by developing a generalized sparse model (GSM) using weighting factors to integrate multi-modality data for biomarker selection. As an example, we applied the GSM model to a joint analysis of two types of schizophrenia data sets: 759,075 SNPs and 153,594 functional magnetic resonance imaging (fMRI) voxels in 208 subjects (92 cases/116 controls). To solve this small-sample-large-variable problem, we developed a novel sparse representation based variable selection (SRVS) algorithm, with the primary aim to identify biomarkers associated with schizophrenia. To validate the effectiveness of the selected variables, we performed multivariate classification followed by a ten-fold cross validation. We compared our proposed SRVS algorithm with an earlier sparse model based variable selection algorithm for integrated analysis. In addition, we compared with the traditional statistics method for uni-variant data analysis (Chi-squared test for SNP data and ANOVA for fMRI data). Results showed that our proposed SRVS method can identify novel biomarkers that show stronger capability in distinguishing schizophrenia patients from healthy controls. Moreover, better classification ratios were achieved using biomarkers from both types of data, suggesting the importance of integrative analysis. Copyright © 2014 Elsevier Inc. All rights reserved.

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.

Comparison of imaging biomarkers for Alzheimer's disease: amyloid imaging with [18F]florbetapir positron emission tomography and magnetic resonance imaging voxel-based analysis for entorhinal cortex atrophy.

PubMed

Tateno, Amane; Sakayori, Takeshi; Kawashima, Yoshitaka; Higuchi, Makoto; Suhara, Tetsuya; Mizumura, Sunao; Mintun, Mark A; Skovronsky, Daniel M; Honjo, Kazuyoshi; Ishihara, Keiichi; Kumita, Shinichiro; Suzuki, Hidenori; Okubo, Yoshiro

2015-05-01

We compared amyloid positron emission tomography (PET) and magnetic resonance imaging (MRI) in subjects clinically diagnosed with Alzheimer's disease (AD), mild cognitive impairment (MCI), and older healthy controls (OHC) in order to test how these imaging biomarkers represent cognitive decline in AD. Fifteen OHC, 19 patients with MCI, and 19 patients with AD were examined by [(18)F]florbetapir PET to quantify the standard uptake value ratio (SUVR) as the degree of amyloid accumulation, by MRI and the voxel-based specific regional analysis system for AD to calculate z-score as the degree of entorhinal cortex atrophy, and by mini-mental state examination (MMSE) and Alzheimer's Disease Assessment Scale-cognitive component--Japanese version (ADAS-Jcog) for cognitive functions. Both cutoff values for measuring AD-like levels of amyloid (1.099 for SUVR) and entorhinal cortex atrophy (1.60 for z-score) were well differentially diagnosed and clinically defined AD from OHC (84.2% for SUVR and 86.7% for z-score). Subgroup analysis based on beta-amyloid positivity revealed that z-score significantly correlated with MMSE (r = -0.626, p < 0.01) and ADAS-Jcog (r = 0.691, p < 0.01) only among subjects with beta-amyloid. This is the first study to compare [(18)F]florbetapir PET and MRI voxel-based analysis of entorhinal cortex atrophy for AD. Both [(18)F]florbetapir PET and MRI detected changes in AD compared with OHC. Considering that entorhinal cortex atrophy correlated well with cognitive decline only among subjects with beta-amyloid, [18F]florbetapir PET makes it possible to detect AD pathology in the early stage, whereas MRI morphometry for subjects with beta-amyloid provides a good biomarker to assess the severity of AD in the later stage. Copyright © 2014 John Wiley & Sons, Ltd.

Wavelet-based fMRI analysis: 3-D denoising, signal separation, and validation metrics

PubMed Central

Khullar, Siddharth; Michael, Andrew; Correa, Nicolle; Adali, Tulay; Baum, Stefi A.; Calhoun, Vince D.

2010-01-01

We present a novel integrated wavelet-domain based framework (w-ICA) for 3-D de-noising functional magnetic resonance imaging (fMRI) data followed by source separation analysis using independent component analysis (ICA) in the wavelet domain. We propose the idea of a 3-D wavelet-based multi-directional de-noising scheme where each volume in a 4-D fMRI data set is sub-sampled using the axial, sagittal and coronal geometries to obtain three different slice-by-slice representations of the same data. The filtered intensity value of an arbitrary voxel is computed as an expected value of the de-noised wavelet coefficients corresponding to the three viewing geometries for each sub-band. This results in a robust set of de-noised wavelet coefficients for each voxel. Given the decorrelated nature of these de-noised wavelet coefficients; it is possible to obtain more accurate source estimates using ICA in the wavelet domain. The contributions of this work can be realized as two modules. First, the analysis module where we combine a new 3-D wavelet denoising approach with better signal separation properties of ICA in the wavelet domain, to yield an activation component that corresponds closely to the true underlying signal and is maximally independent with respect to other components. Second, we propose and describe two novel shape metrics for post-ICA comparisons between activation regions obtained through different frameworks. We verified our method using simulated as well as real fMRI data and compared our results against the conventional scheme (Gaussian smoothing + spatial ICA: s-ICA). The results show significant improvements based on two important features: (1) preservation of shape of the activation region (shape metrics) and (2) receiver operating characteristic (ROC) curves. It was observed that the proposed framework was able to preserve the actual activation shape in a consistent manner even for very high noise levels in addition to significant reduction in false positives voxels. PMID:21034833

Global scaling for semi-quantitative analysis in FP-CIT SPECT.

PubMed

Kupitz, D; Apostolova, I; Lange, C; Ulrich, G; Amthauer, H; Brenner, W; Buchert, R

2014-01-01

Semi-quantitative characterization of dopamine transporter availability from single photon emission computed tomography (SPECT) with 123I-ioflupane (FP-CIT) is based on uptake ratios relative to a reference region. The aim of this study was to evaluate the whole brain as reference region for semi-quantitative analysis of FP-CIT SPECT. The rationale was that this might reduce statistical noise associated with the estimation of non-displaceable FP-CIT uptake. 150 FP-CIT SPECTs were categorized as neurodegenerative or non-neurodegenerative by an expert. Semi-quantitative analysis of specific binding ratios (SBR) was performed with a custom-made tool based on the Statistical Parametric Mapping software package using predefined regions of interest (ROIs) in the anatomical space of the Montreal Neurological Institute. The following reference regions were compared: predefined ROIs for frontal and occipital lobe and whole brain (without striata, thalamus and brainstem). Tracer uptake in the reference region was characterized by the mean, median or 75th percentile of its voxel intensities. The area (AUC) under the receiver operating characteristic curve was used as performance measure. The highest AUC of 0.973 was achieved by the SBR of the putamen with the 75th percentile in the whole brain as reference. The lowest AUC for the putamen SBR of 0.937 was obtained with the mean in the frontal lobe as reference. We recommend the 75th percentile in the whole brain as reference for semi-quantitative analysis in FP-CIT SPECT. This combination provided the best agreement of the semi-quantitative analysis with visual evaluation of the SPECT images by an expert and, therefore, is appropriate to support less experienced physicians.

Enhanced cortical thickness measurements for rodent brains via Lagrangian-based RK4 streamline computation

NASA Astrophysics Data System (ADS)

Lee, Joohwi; Kim, Sun Hyung; Oguz, Ipek; Styner, Martin

2016-03-01

The cortical thickness of the mammalian brain is an important morphological characteristic that can be used to investigate and observe the brain's developmental changes that might be caused by biologically toxic substances such as ethanol or cocaine. Although various cortical thickness analysis methods have been proposed that are applicable for human brain and have developed into well-validated open-source software packages, cortical thickness analysis methods for rodent brains have not yet become as robust and accurate as those designed for human brains. Based on a previously proposed cortical thickness measurement pipeline for rodent brain analysis,1 we present an enhanced cortical thickness pipeline in terms of accuracy and anatomical consistency. First, we propose a Lagrangian-based computational approach in the thickness measurement step in order to minimize local truncation error using the fourth-order Runge-Kutta method. Second, by constructing a line object for each streamline of the thickness measurement, we can visualize the way the thickness is measured and achieve sub-voxel accuracy by performing geometric post-processing. Last, with emphasis on the importance of an anatomically consistent partial differential equation (PDE) boundary map, we propose an automatic PDE boundary map generation algorithm that is specific to rodent brain anatomy, which does not require manual labeling. The results show that the proposed cortical thickness pipeline can produce statistically significant regions that are not observed in the previous cortical thickness analysis pipeline.

Director Field Analysis (DFA): Exploring Local White Matter Geometric Structure in Diffusion MRI.

PubMed

Cheng, Jian; Basser, Peter J

2018-01-01

In Diffusion Tensor Imaging (DTI) or High Angular Resolution Diffusion Imaging (HARDI), a tensor field or a spherical function field (e.g., an orientation distribution function field), can be estimated from measured diffusion weighted images. In this paper, inspired by the microscopic theoretical treatment of phases in liquid crystals, we introduce a novel mathematical framework, called Director Field Analysis (DFA), to study local geometric structural information of white matter based on the reconstructed tensor field or spherical function field: (1) We propose a set of mathematical tools to process general director data, which consists of dyadic tensors that have orientations but no direction. (2) We propose Orientational Order (OO) and Orientational Dispersion (OD) indices to describe the degree of alignment and dispersion of a spherical function in a single voxel or in a region, respectively; (3) We also show how to construct a local orthogonal coordinate frame in each voxel exhibiting anisotropic diffusion; (4) Finally, we define three indices to describe three types of orientational distortion (splay, bend, and twist) in a local spatial neighborhood, and a total distortion index to describe distortions of all three types. To our knowledge, this is the first work to quantitatively describe orientational distortion (splay, bend, and twist) in general spherical function fields from DTI or HARDI data. The proposed DFA and its related mathematical tools can be used to process not only diffusion MRI data but also general director field data, and the proposed scalar indices are useful for detecting local geometric changes of white matter for voxel-based or tract-based analysis in both DTI and HARDI acquisitions. The related codes and a tutorial for DFA will be released in DMRITool. Copyright © 2017 Elsevier B.V. All rights reserved.

The dorsal stream contribution to phonological retrieval in object naming

PubMed Central

Faseyitan, Olufunsho; Kim, Junghoon; Coslett, H. Branch

2012-01-01

Meaningful speech, as exemplified in object naming, calls on knowledge of the mappings between word meanings and phonological forms. Phonological errors in naming (e.g. GHOST named as ‘goath’) are commonly seen in persisting post-stroke aphasia and are thought to signal impairment in retrieval of phonological form information. We performed a voxel-based lesion-symptom mapping analysis of 1718 phonological naming errors collected from 106 individuals with diverse profiles of aphasia. Voxels in which lesion status correlated with phonological error rates localized to dorsal stream areas, in keeping with classical and contemporary brain-language models. Within the dorsal stream, the critical voxels were concentrated in premotor cortex, pre- and postcentral gyri and supramarginal gyrus with minimal extension into auditory-related posterior temporal and temporo-parietal cortices. This challenges the popular notion that error-free phonological retrieval requires guidance from sensory traces stored in posterior auditory regions and points instead to sensory-motor processes located further anterior in the dorsal stream. In a separate analysis, we compared the lesion maps for phonological and semantic errors and determined that there was no spatial overlap, demonstrating that the brain segregates phonological and semantic retrieval operations in word production. PMID:23171662

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

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.

SPM analysis of parametric (R)-[11C]PK11195 binding images: plasma input versus reference tissue parametric methods.

PubMed

Schuitemaker, Alie; van Berckel, Bart N M; Kropholler, Marc A; Veltman, Dick J; Scheltens, Philip; Jonker, Cees; Lammertsma, Adriaan A; Boellaard, Ronald

2007-05-01

(R)-[11C]PK11195 has been used for quantifying cerebral microglial activation in vivo. In previous studies, both plasma input and reference tissue methods have been used, usually in combination with a region of interest (ROI) approach. Definition of ROIs, however, can be labourious and prone to interobserver variation. In addition, results are only obtained for predefined areas and (unexpected) signals in undefined areas may be missed. On the other hand, standard pharmacokinetic models are too sensitive to noise to calculate (R)-[11C]PK11195 binding on a voxel-by-voxel basis. Linearised versions of both plasma input and reference tissue models have been described, and these are more suitable for parametric imaging. The purpose of this study was to compare the performance of these plasma input and reference tissue parametric methods on the outcome of statistical parametric mapping (SPM) analysis of (R)-[11C]PK11195 binding. Dynamic (R)-[11C]PK11195 PET scans with arterial blood sampling were performed in 7 younger and 11 elderly healthy subjects. Parametric images of volume of distribution (Vd) and binding potential (BP) were generated using linearised versions of plasma input (Logan) and reference tissue (Reference Parametric Mapping) models. Images were compared at the group level using SPM with a two-sample t-test per voxel, both with and without proportional scaling. Parametric BP images without scaling provided the most sensitive framework for determining differences in (R)-[11C]PK11195 binding between younger and elderly subjects. Vd images could only demonstrate differences in (R)-[11C]PK11195 binding when analysed with proportional scaling due to intersubject variation in K1/k2 (blood-brain barrier transport and non-specific binding).

Automated Voxel-Based Analysis of Volumetric Dynamic Contrast-Enhanced CT Data Improves Measurement of Serial Changes in Tumor Vascular Biomarkers

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

Coolens, Catherine, E-mail: catherine.coolens@rmp.uhn.on.ca; Department of Radiation Oncology, University of Toronto, Toronto, Ontario; Institute of Biomaterials and Biomedical Engineering, University of Toronto, Toronto, Ontario

2015-01-01

Objectives: Development of perfusion imaging as a biomarker requires more robust methodologies for quantification of tumor physiology that allow assessment of volumetric tumor heterogeneity over time. This study proposes a parametric method for automatically analyzing perfused tissue from volumetric dynamic contrast-enhanced (DCE) computed tomography (CT) scans and assesses whether this 4-dimensional (4D) DCE approach is more robust and accurate than conventional, region-of-interest (ROI)-based CT methods in quantifying tumor perfusion with preliminary evaluation in metastatic brain cancer. Methods and Materials: Functional parameter reproducibility and analysis of sensitivity to imaging resolution and arterial input function were evaluated in image sets acquired from amore » 320-slice CT with a controlled flow phantom and patients with brain metastases, whose treatments were planned for stereotactic radiation surgery and who consented to a research ethics board-approved prospective imaging biomarker study. A voxel-based temporal dynamic analysis (TDA) methodology was used at baseline, at day 7, and at day 20 after treatment. The ability to detect changes in kinetic parameter maps in clinical data sets was investigated for both 4D TDA and conventional 2D ROI-based analysis methods. Results: A total of 7 brain metastases in 3 patients were evaluated over the 3 time points. The 4D TDA method showed improved spatial efficacy and accuracy of perfusion parameters compared to ROI-based DCE analysis (P<.005), with a reproducibility error of less than 2% when tested with DCE phantom data. Clinically, changes in transfer constant from the blood plasma into the extracellular extravascular space (K{sub trans}) were seen when using TDA, with substantially smaller errors than the 2D method on both day 7 post radiation surgery (±13%; P<.05) and by day 20 (±12%; P<.04). Standard methods showed a decrease in K{sub trans} but with large uncertainty (111.6 ± 150.5) %. Conclusions: Parametric voxel-based analysis of 4D DCE CT data resulted in greater accuracy and reliability in measuring changes in perfusion CT-based kinetic metrics, which have the potential to be used as biomarkers in patients with metastatic brain cancer.« less

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

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.

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.

A meta-analysis of sex differences in human brain structure☆

PubMed Central

Ruigrok, Amber N.V.; Salimi-Khorshidi, Gholamreza; Lai, Meng-Chuan; Baron-Cohen, Simon; Lombardo, Michael V.; Tait, Roger J.; Suckling, John

2014-01-01

The prevalence, age of onset, and symptomatology of many neuropsychiatric conditions differ between males and females. To understand the causes and consequences of sex differences it is important to establish where they occur in the human brain. We report the first meta-analysis of typical sex differences on global brain volume, a descriptive account of the breakdown of studies of each compartmental volume by six age categories, and whole-brain voxel-wise meta-analyses on brain volume and density. Gaussian-process regression coordinate-based meta-analysis was used to examine sex differences in voxel-based regional volume and density. On average, males have larger total brain volumes than females. Examination of the breakdown of studies providing total volumes by age categories indicated a bias towards the 18–59 year-old category. Regional sex differences in volume and tissue density include the amygdala, hippocampus and insula, areas known to be implicated in sex-biased neuropsychiatric conditions. Together, these results suggest candidate regions for investigating the asymmetric effect that sex has on the developing brain, and for understanding sex-biased neurological and psychiatric conditions. PMID:24374381

An SPM8-based approach for attenuation correction combining segmentation and nonrigid template formation: application to simultaneous PET/MR brain imaging.

PubMed

Izquierdo-Garcia, David; Hansen, Adam E; Förster, Stefan; Benoit, Didier; Schachoff, Sylvia; Fürst, Sebastian; Chen, Kevin T; Chonde, Daniel B; Catana, Ciprian

2014-11-01

We present an approach for head MR-based attenuation correction (AC) based on the Statistical Parametric Mapping 8 (SPM8) software, which combines segmentation- and atlas-based features to provide a robust technique to generate attenuation maps (μ maps) from MR data in integrated PET/MR scanners. Coregistered anatomic MR and CT images of 15 glioblastoma subjects were used to generate the templates. The MR images from these subjects were first segmented into 6 tissue classes (gray matter, white matter, cerebrospinal fluid, bone, soft tissue, and air), which were then nonrigidly coregistered using a diffeomorphic approach. A similar procedure was used to coregister the anatomic MR data for a new subject to the template. Finally, the CT-like images obtained by applying the inverse transformations were converted to linear attenuation coefficients to be used for AC of PET data. The method was validated on 16 new subjects with brain tumors (n = 12) or mild cognitive impairment (n = 4) who underwent CT and PET/MR scans. The μ maps and corresponding reconstructed PET images were compared with those obtained using the gold standard CT-based approach and the Dixon-based method available on the Biograph mMR scanner. Relative change (RC) images were generated in each case, and voxel- and region-of-interest-based analyses were performed. The leave-one-out cross-validation analysis of the data from the 15 atlas-generation subjects showed small errors in brain linear attenuation coefficients (RC, 1.38% ± 4.52%) compared with the gold standard. Similar results (RC, 1.86% ± 4.06%) were obtained from the analysis of the atlas-validation datasets. The voxel- and region-of-interest-based analysis of the corresponding reconstructed PET images revealed quantification errors of 3.87% ± 5.0% and 2.74% ± 2.28%, respectively. The Dixon-based method performed substantially worse (the mean RC values were 13.0% ± 10.25% and 9.38% ± 4.97%, respectively). Areas closer to the skull showed the largest improvement. We have presented an SPM8-based approach for deriving the head μ map from MR data to be used for PET AC in integrated PET/MR scanners. Its implementation is straightforward and requires only the morphologic data acquired with a single MR sequence. The method is accurate and robust, combining the strengths of both segmentation- and atlas-based approaches while minimizing their drawbacks. © 2014 by the Society of Nuclear Medicine and Molecular Imaging, Inc.

An SPM8-based Approach for Attenuation Correction Combining Segmentation and Non-rigid Template Formation: Application to Simultaneous PET/MR Brain Imaging

PubMed Central

Izquierdo-Garcia, David; Hansen, Adam E.; Förster, Stefan; Benoit, Didier; Schachoff, Sylvia; Fürst, Sebastian; Chen, Kevin T.; Chonde, Daniel B.; Catana, Ciprian

2014-01-01

We present an approach for head MR-based attenuation correction (MR-AC) based on the Statistical Parametric Mapping (SPM8) software that combines segmentation- and atlas-based features to provide a robust technique to generate attenuation maps (µ-maps) from MR data in integrated PET/MR scanners. Methods Coregistered anatomical MR and CT images acquired in 15 glioblastoma subjects were used to generate the templates. The MR images from these subjects were first segmented into 6 tissue classes (gray and white matter, cerebro-spinal fluid, bone and soft tissue, and air), which were then non-rigidly coregistered using a diffeomorphic approach. A similar procedure was used to coregister the anatomical MR data for a new subject to the template. Finally, the CT-like images obtained by applying the inverse transformations were converted to linear attenuation coefficients (LACs) to be used for AC of PET data. The method was validated on sixteen new subjects with brain tumors (N=12) or mild cognitive impairment (N=4) who underwent CT and PET/MR scans. The µ-maps and corresponding reconstructed PET images were compared to those obtained using the gold standard CT-based approach and the Dixon-based method available on the Siemens Biograph mMR scanner. Relative change (RC) images were generated in each case and voxel- and region of interest (ROI)-based analyses were performed. Results The leave-one-out cross-validation analysis of the data from the 15 atlas-generation subjects showed small errors in brain LACs (RC=1.38%±4.52%) compared to the gold standard. Similar results (RC=1.86±4.06%) were obtained from the analysis of the atlas-validation datasets. The voxel- and ROI-based analysis of the corresponding reconstructed PET images revealed quantification errors of 3.87±5.0% and 2.74±2.28%, respectively. The Dixon-based method performed substantially worse (the mean RC values were 13.0±10.25% and 9.38±4.97%, respectively). Areas closer to skull showed the largest improvement. Conclusion We have presented an SPM8-based approach for deriving the head µ-map from MR data to be used for PET AC in integrated PET/MR scanners. Its implementation is straightforward and only requires the morphological data acquired with a single MR sequence. The method is very accurate and robust, combining the strengths of both segmentation- and atlas-based approaches while minimizing their drawbacks. PMID:25278515

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).

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.

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.

Impact of MR Acquisition Parameters on DTI Scalar Indexes: A Tractography Based Approach.

PubMed

Barrio-Arranz, Gonzalo; de Luis-García, Rodrigo; Tristán-Vega, Antonio; Martín-Fernández, Marcos; Aja-Fernández, Santiago

2015-01-01

Acquisition parameters play a crucial role in Diffusion Tensor Imaging (DTI), as they have a major impact on the values of scalar measures such as Fractional Anisotropy (FA) or Mean Diffusivity (MD) that are usually the focus of clinical studies based on white matter analysis. This paper presents an analysis on the impact of the variation of several acquisition parameters on these scalar measures with a novel double focus. First, a tractography-based approach is employed, motivated by the significant number of clinical studies that are carried out using this technique. Second, the consequences of simultaneous changes in multiple parameters are analyzed: number of gradient directions, b-value and voxel resolution. Results indicate that the FA is most affected by changes in the number of gradients and voxel resolution, while MD is specially influenced by variations in the b-value. Even if the choice of a tractography algorithm has an effect on the numerical values of the final scalar measures, the evolution of these measures when acquisition parameters are modified is parallel.

Impact of MR Acquisition Parameters on DTI Scalar Indexes: A Tractography Based Approach

PubMed Central

Barrio-Arranz, Gonzalo; de Luis-García, Rodrigo; Tristán-Vega, Antonio; Martín-Fernández, Marcos; Aja-Fernández, Santiago

2015-01-01

Acquisition parameters play a crucial role in Diffusion Tensor Imaging (DTI), as they have a major impact on the values of scalar measures such as Fractional Anisotropy (FA) or Mean Diffusivity (MD) that are usually the focus of clinical studies based on white matter analysis. This paper presents an analysis on the impact of the variation of several acquisition parameters on these scalar measures with a novel double focus. First, a tractography-based approach is employed, motivated by the significant number of clinical studies that are carried out using this technique. Second, the consequences of simultaneous changes in multiple parameters are analyzed: number of gradient directions, b-value and voxel resolution. Results indicate that the FA is most affected by changes in the number of gradients and voxel resolution, while MD is specially influenced by variations in the b-value. Even if the choice of a tractography algorithm has an effect on the numerical values of the final scalar measures, the evolution of these measures when acquisition parameters are modified is parallel. PMID:26457415

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.

Type I and Type II error concerns in fMRI research: re-balancing the scale

PubMed Central

Cunningham, William A.

2009-01-01

Statistical thresholding (i.e. P-values) in fMRI research has become increasingly conservative over the past decade in an attempt to diminish Type I errors (i.e. false alarms) to a level traditionally allowed in behavioral science research. In this article, we examine the unintended negative consequences of this single-minded devotion to Type I errors: increased Type II errors (i.e. missing true effects), a bias toward studying large rather than small effects, a bias toward observing sensory and motor processes rather than complex cognitive and affective processes and deficient meta-analyses. Power analyses indicate that the reductions in acceptable P-values over time are producing dramatic increases in the Type II error rate. Moreover, the push for a mapwide false discovery rate (FDR) of 0.05 is based on the assumption that this is the FDR in most behavioral research; however, this is an inaccurate assessment of the conventions in actual behavioral research. We report simulations demonstrating that combined intensity and cluster size thresholds such as P < 0.005 with a 10 voxel extent produce a desirable balance between Types I and II error rates. This joint threshold produces high but acceptable Type II error rates and produces a FDR that is comparable to the effective FDR in typical behavioral science articles (while a 20 voxel extent threshold produces an actual FDR of 0.05 with relatively common imaging parameters). We recommend a greater focus on replication and meta-analysis rather than emphasizing single studies as the unit of analysis for establishing scientific truth. From this perspective, Type I errors are self-erasing because they will not replicate, thus allowing for more lenient thresholding to avoid Type II errors. PMID:20035017

Structural covariance of neostriatal and limbic regions in patients with obsessive-compulsive disorder.

PubMed

Subirà, Marta; Cano, Marta; de Wit, Stella J; Alonso, Pino; Cardoner, Narcís; Hoexter, Marcelo Q; Kwon, Jun Soo; Nakamae, Takashi; Lochner, Christine; Sato, João R; Jung, Wi Hoon; Narumoto, Jin; Stein, Dan J; Pujol, Jesus; Mataix-Cols, David; Veltman, Dick J; Menchón, José M; van den Heuvel, Odile A; Soriano-Mas, Carles

2016-03-01

Frontostriatal and frontoamygdalar connectivity alterations in patients with obsessive-compulsive disorder (OCD) have been typically described in functional neuroimaging studies. However, structural covariance, or volumetric correlations across distant brain regions, also provides network-level information. Altered structural covariance has been described in patients with different psychiatric disorders, including OCD, but to our knowledge, alterations within frontostriatal and frontoamygdalar circuits have not been explored. We performed a mega-analysis pooling structural MRI scans from the Obsessive-compulsive Brain Imaging Consortium and assessed whole-brain voxel-wise structural covariance of 4 striatal regions (dorsal and ventral caudate nucleus, and dorsal-caudal and ventral-rostral putamen) and 2 amygdalar nuclei (basolateral and centromedial-superficial). Images were preprocessed with the standard pipeline of voxel-based morphometry studies using Statistical Parametric Mapping software. Our analyses involved 329 patients with OCD and 316 healthy controls. Patients showed increased structural covariance between the left ventral-rostral putamen and the left inferior frontal gyrus/frontal operculum region. This finding had a significant interaction with age; the association held only in the subgroup of older participants. Patients with OCD also showed increased structural covariance between the right centromedial-superficial amygdala and the ventromedial prefrontal cortex. This was a cross-sectional study. Because this is a multisite data set analysis, participant recruitment and image acquisition were performed in different centres. Most patients were taking medication, and treatment protocols differed across centres. Our results provide evidence for structural network-level alterations in patients with OCD involving 2 frontosubcortical circuits of relevance for the disorder and indicate that structural covariance contributes to fully characterizing brain alterations in patients with psychiatric disorders.

fMRat: an extension of SPM for a fully automatic analysis of rodent brain functional magnetic resonance series.

PubMed

Chavarrías, Cristina; García-Vázquez, Verónica; Alemán-Gómez, Yasser; Montesinos, Paula; Pascau, Javier; Desco, Manuel

2016-05-01

The purpose of this study was to develop a multi-platform automatic software tool for full processing of fMRI rodent studies. Existing tools require the usage of several different plug-ins, a significant user interaction and/or programming skills. Based on a user-friendly interface, the tool provides statistical parametric brain maps (t and Z) and percentage of signal change for user-provided regions of interest. The tool is coded in MATLAB (MathWorks(®)) and implemented as a plug-in for SPM (Statistical Parametric Mapping, the Wellcome Trust Centre for Neuroimaging). The automatic pipeline loads default parameters that are appropriate for preclinical studies and processes multiple subjects in batch mode (from images in either Nifti or raw Bruker format). In advanced mode, all processing steps can be selected or deselected and executed independently. Processing parameters and workflow were optimized for rat studies and assessed using 460 male-rat fMRI series on which we tested five smoothing kernel sizes and three different hemodynamic models. A smoothing kernel of FWHM = 1.2 mm (four times the voxel size) yielded the highest t values at the somatosensorial primary cortex, and a boxcar response function provided the lowest residual variance after fitting. fMRat offers the features of a thorough SPM-based analysis combined with the functionality of several SPM extensions in a single automatic pipeline with a user-friendly interface. The code and sample images can be downloaded from https://github.com/HGGM-LIM/fmrat .

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.

Gray matter and white matter abnormalities in online game addiction.

PubMed

Weng, Chuan-Bo; Qian, Ruo-Bing; Fu, Xian-Ming; Lin, Bin; Han, Xiao-Peng; Niu, Chao-Shi; Wang, Ye-Han

2013-08-01

Online game addiction (OGA) has attracted greater attention as a serious public mental health issue. However, there are only a few brain magnetic resonance imaging studies on brain structure about OGA. In the current study, we used voxel-based morphometry (VBM) analysis and tract-based spatial statistics (TBSS) to investigate the microstructural changes in OGA and assessed the relationship between these morphology changes and the Young's Internet Addiction Scale (YIAS) scores within the OGA group. Compared with healthy subjects, OGA individuals showed significant gray matter atrophy in the right orbitofrontal cortex, bilateral insula, and right supplementary motor area. According to TBSS analysis, OGA subjects had significantly reduced FA in the right genu of corpus callosum, bilateral frontal lobe white matter, and right external capsule. Gray matter volumes (GMV) of the right orbitofrontal cortex, bilateral insula and FA values of the right external capsule were significantly positively correlated with the YIAS scores in the OGA subjects. Our findings suggested that microstructure abnormalities of gray and white matter were present in OGA subjects. This finding may provide more insights into the understanding of the underlying neural mechanisms of OGA. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

[Glossary of terms used by radiologists in image processing].

PubMed

Rolland, Y; Collorec, R; Bruno, A; Ramée, A; Morcet, N; Haigron, P

1995-01-01

We give the definition of 166 words used in image processing. Adaptivity, aliazing, analog-digital converter, analysis, approximation, arc, artifact, artificial intelligence, attribute, autocorrelation, bandwidth, boundary, brightness, calibration, class, classification, classify, centre, cluster, coding, color, compression, contrast, connectivity, convolution, correlation, data base, decision, decomposition, deconvolution, deduction, descriptor, detection, digitization, dilation, discontinuity, discretization, discrimination, disparity, display, distance, distorsion, distribution dynamic, edge, energy, enhancement, entropy, erosion, estimation, event, extrapolation, feature, file, filter, filter floaters, fitting, Fourier transform, frequency, fusion, fuzzy, Gaussian, gradient, graph, gray level, group, growing, histogram, Hough transform, Houndsfield, image, impulse response, inertia, intensity, interpolation, interpretation, invariance, isotropy, iterative, JPEG, knowledge base, label, laplacian, learning, least squares, likelihood, matching, Markov field, mask, matching, mathematical morphology, merge (to), MIP, median, minimization, model, moiré, moment, MPEG, neural network, neuron, node, noise, norm, normal, operator, optical system, optimization, orthogonal, parametric, pattern recognition, periodicity, photometry, pixel, polygon, polynomial, prediction, pulsation, pyramidal, quantization, raster, reconstruction, recursive, region, rendering, representation space, resolution, restoration, robustness, ROC, thinning, transform, sampling, saturation, scene analysis, segmentation, separable function, sequential, smoothing, spline, split (to), shape, threshold, tree, signal, speckle, spectrum, spline, stationarity, statistical, stochastic, structuring element, support, syntaxic, synthesis, texture, truncation, variance, vision, voxel, windowing.

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

Meta-analysis of gray matter abnormalities in autism spectrum disorder: should Asperger disorder be subsumed under a broader umbrella of autistic spectrum disorder?

PubMed

Via, Esther; Radua, Joaquim; Cardoner, Narcis; Happé, Francesca; Mataix-Cols, David

2011-04-01

Studies investigating abnormalities of regional gray matter volume in autism spectrum disorder (ASD) have yielded contradictory results. It is unclear whether the current subtyping of ASD into autistic disorder and Asperger disorder is neurobiologically valid. To conduct a quantitative meta-analysis of voxel-based morphometry studies exploring gray matter volume abnormalities in ASD, to examine potential neurobiological differences among ASD subtypes, and to create an online database to facilitate replication and further analyses by other researchers. We retrieved studies from PubMed, ScienceDirect, Scopus, and Web of Knowledge databases between June 3, 1999, the date of the first voxel-based morphometry study in ASD, and October 31, 2010. Studies were also retrieved from reference lists and review articles. We contacted authors soliciting additional data. Twenty-four data sets met inclusion criteria, comprising 496 participants with ASD and 471 healthy control individuals. Peak coordinates of clusters of regional gray matter differences between participants with ASD and controls, as well as demographic, clinical, and methodologic variables, were extracted from each study or obtained from the authors. No differences in overall gray matter volume were found between participants with ASD and healthy controls. Participants with ASD were found to have robust decreases of gray matter volume in the bilateral amygdala-hippocampus complex and the bilateral precuneus. A small increase of gray matter volume in the middle-inferior frontal gyrus was also found. No significant differences in overall or regional gray matter volumes were found between autistic disorder and Asperger disorder. Decreases of gray matter volume in the right precuneus were statistically higher in adults than in adolescents with ASD. These results confirm the crucial involvement of structures linked to social cognition in ASD. The absence of significant differences between ASD subtypes may have important nosologic implications for the DSM-5. The publically available database will be a useful resource for future research.

Visual System Involvement in Patients with Newly Diagnosed Parkinson Disease.

PubMed

Arrigo, Alessandro; Calamuneri, Alessandro; Milardi, Demetrio; Mormina, Enricomaria; Rania, Laura; Postorino, Elisa; Marino, Silvia; Di Lorenzo, Giuseppe; Anastasi, Giuseppe Pio; Ghilardi, Maria Felice; Aragona, Pasquale; Quartarone, Angelo; Gaeta, Michele

2017-12-01

Purpose To assess intracranial visual system changes of newly diagnosed Parkinson disease in drug-naïve patients. Materials and Methods Twenty patients with newly diagnosed Parkinson disease and 20 age-matched control subjects were recruited. Magnetic resonance (MR) imaging (T1-weighted and diffusion-weighted imaging) was performed with a 3-T MR imager. White matter changes were assessed by exploring a white matter diffusion profile by means of diffusion-tensor imaging-based parameters and constrained spherical deconvolution-based connectivity analysis and by means of white matter voxel-based morphometry (VBM). Alterations in occipital gray matter were investigated by means of gray matter VBM. Morphologic analysis of the optic chiasm was based on manual measurement of regions of interest. Statistical testing included analysis of variance, t tests, and permutation tests. Results In the patients with Parkinson disease, significant alterations were found in optic radiation connectivity distribution, with decreased lateral geniculate nucleus V2 density (F, -8.28; P < .05), a significant increase in optic radiation mean diffusivity (F, 7.5; P = .014), and a significant reduction in white matter concentration. VBM analysis also showed a significant reduction in visual cortical volumes (P < .05). Moreover, the chiasmatic area and volume were significantly reduced (P < .05). Conclusion The findings show that visual system alterations can be detected in early stages of Parkinson disease and that the entire intracranial visual system can be involved. © RSNA, 2017 Online supplemental material is available for this article.

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.

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.

Characterization of reactive flow-induced evolution of carbonate rocks using digital core analysis- part 1: Assessment of pore-scale mineral dissolution and deposition.

PubMed

Qajar, Jafar; Arns, Christoph H

2016-09-01

The application of X-ray micro-computed tomography (μ-CT) for quantitatively characterizing reactive-flow induced pore structure evolution including local particle detachment, displacement and deposition in carbonate rocks is investigated. In the studies conducted in this field of research, the experimental procedure has involved alternating steps of imaging and ex-situ core sample alteration. Practically, it is impossible to return the sample, with micron precision, to the same position and orientation. Furthermore, successive images of a sample in pre- and post-alteration states are usually taken at different conditions such as different scales, resolutions and signal-to-noise ratios. These conditions accompanying with subresolution features in the images make voxel-by-voxel comparisons of successive images problematic. In this paper, we first address the respective challenges in voxel-wise interpretation of successive images of carbonate rocks subject to reactive flow. Reactive coreflood in two carbonate cores with different rock types are considered. For the first rock, we used the experimental and imaging results published by Qajar et al. (2013) which showed a quasi-uniform dissolution regime. A similar reactive core flood was conducted in the second rock which resulted in wormhole-like dissolution regime. We particularly examine the major image processing operations such as transformation of images to the same grey-scale, noise filtering and segmentation thresholding and propose quantitative methods to evaluate the effectiveness of these operations in voxel-wise analysis of successive images of a sample. In the second part, we generalize the methodology based on the three-phase segmentation of normalized images, microporosity assignment and 2D histogram of image intensities to estimate grey-scale changes of individual image voxels for a general case where the greyscale images are segmented into arbitrary number of phases. The results show that local (voxel-based) porosity changes can be decomposed into local mineral dissolution and deposition. Moreover, it is found that the microporosity evolutions are differently distributed in the samples after the reactive coreflood experiments. In the last part of the paper, for the case of quasi-uniform dissolution, we combine the tomographic images with numerical calculations of permeability along the core to characterize the relationship between changes in permeability and the fractions of the mineral dissolved and deposited. A consistency is found between the calculated longitudinal permeability changes and the quantified distribution of mineral dissolved and deposited along the sample. Copyright © 2016 Elsevier B.V. All rights reserved.

Characterization of reactive flow-induced evolution of carbonate rocks using digital core analysis- part 1: Assessment of pore-scale mineral dissolution and deposition

NASA Astrophysics Data System (ADS)

Qajar, Jafar; Arns, Christoph H.

2016-09-01

The application of X-ray micro-computed tomography (μ-CT) for quantitatively characterizing reactive-flow induced pore structure evolution including local particle detachment, displacement and deposition in carbonate rocks is investigated. In the studies conducted in this field of research, the experimental procedure has involved alternating steps of imaging and ex-situ core sample alteration. Practically, it is impossible to return the sample, with micron precision, to the same position and orientation. Furthermore, successive images of a sample in pre- and post-alteration states are usually taken at different conditions such as different scales, resolutions and signal-to-noise ratios. These conditions accompanying with subresolution features in the images make voxel-by-voxel comparisons of successive images problematic. In this paper, we first address the respective challenges in voxel-wise interpretation of successive images of carbonate rocks subject to reactive flow. Reactive coreflood in two carbonate cores with different rock types are considered. For the first rock, we used the experimental and imaging results published by Qajar et al. (2013) which showed a quasi-uniform dissolution regime. A similar reactive core flood was conducted in the second rock which resulted in wormhole-like dissolution regime. We particularly examine the major image processing operations such as transformation of images to the same grey-scale, noise filtering and segmentation thresholding and propose quantitative methods to evaluate the effectiveness of these operations in voxel-wise analysis of successive images of a sample. In the second part, we generalize the methodology based on the three-phase segmentation of normalized images, microporosity assignment and 2D histogram of image intensities to estimate grey-scale changes of individual image voxels for a general case where the greyscale images are segmented into arbitrary number of phases. The results show that local (voxel-based) porosity changes can be decomposed into local mineral dissolution and deposition. Moreover, it is found that the microporosity evolutions are differently distributed in the samples after the reactive coreflood experiments. In the last part of the paper, for the case of quasi-uniform dissolution, we combine the tomographic images with numerical calculations of permeability along the core to characterize the relationship between changes in permeability and the fractions of the mineral dissolved and deposited. A consistency is found between the calculated longitudinal permeability changes and the quantified distribution of mineral dissolved and deposited along the sample.

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.

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.

Alterations in White Matter Integrity in Young Adults with Smartphone Dependence

PubMed Central

Hu, Yuanming; Long, Xiaojing; Lyu, Hanqing; Zhou, Yangyang; Chen, Jianxiang

2017-01-01

Smartphone dependence (SPD) is increasingly regarded as a psychological problem, however, the underlying neural substrates of SPD is still not clear. High resolution magnetic resonance imaging provides a useful tool to help understand and manage the disorder. In this study, a tract-based spatial statistics (TBSS) analysis on diffusion tensor imaging (DTI) was used to measure white matter integrity in young adults with SPD. A total of 49 subjects were recruited and categorized into SPD and control group based on their clinical behavioral tests. To localize regions with abnormal white matter integrity in SPD, the voxel-wise analysis of fractional anisotropy (FA) and mean diffusivity (MD) on the whole brain was performed by TBSS. The correlation between the quantitative variables of brain structures and the behavior measures were performed. Our result demonstrated that SPD had significantly lower white matter integrity than controls in superior longitudinal fasciculus (SLF), superior corona radiata (SCR), internal capsule, external capsule, sagittal stratum, fornix/stria terminalis and midbrain structures. Correlation analysis showed that the observed abnormalities in internal capsule and stria terminalis were correlated with the severity of dependence and behavioral assessments. Our finding facilitated a primary understanding of white matter characteristics in SPD and indicated that the structural deficits might link to behavioral impairments. PMID:29163108

Morphometric analysis and neuroanatomical mapping of the zebrafish brain.

PubMed

Gupta, Tripti; Marquart, Gregory D; Horstick, Eric J; Tabor, Kathryn M; Pajevic, Sinisa; Burgess, Harold A

2018-06-21

Large-scale genomic studies have recently identified genetic variants causative for major neurodevelopmental disorders, such as intellectual disability and autism. However, determining how underlying developmental processes are affected by these mutations remains a significant challenge in the field. Zebrafish is an established model system in developmental neurogenetics that may be useful in uncovering the mechanisms of these mutations. Here we describe the use of voxel-intensity, deformation field, and volume-based morphometric techniques for the systematic and unbiased analysis of gene knock-down and environmental exposure-induced phenotypes in zebrafish. We first present a computational method for brain segmentation based on transgene expression patterns to create a comprehensive neuroanatomical map. This map allowed us to disclose statistically significant changes in brain microstructure and composition in neurodevelopmental models. We demonstrate the effectiveness of morphometric techniques in measuring changes in the relative size of neuroanatomical subdivisions in atoh7 morphant larvae and in identifying phenotypes in larvae treated with valproic acid, a chemical demonstrated to increase the risk of autism in humans. These tools enable rigorous evaluation of the effects of gene mutations and environmental exposures on neural development, providing an entry point for cellular and molecular analysis of basic developmental processes as well as neurodevelopmental and neurodegenerative disorders. Published by Elsevier 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

Rat brain digital stereotaxic white matter atlas with fine tract delineation in Paxinos space and its automated applications in DTI data analysis.

PubMed

Liang, Shengxiang; Wu, Shang; Huang, Qi; Duan, Shaofeng; Liu, Hua; Li, Yuxiao; Zhao, Shujun; Nie, Binbin; Shan, Baoci

2017-11-01

To automatically analyze diffusion tensor images of the rat brain via both voxel-based and ROI-based approaches, we constructed a new white matter atlas of the rat brain with fine tracts delineation in the Paxinos and Watson space. Unlike in previous studies, we constructed a digital atlas image from the latest edition of the Paxinos and Watson. This atlas contains 111 carefully delineated white matter fibers. A white matter network of rat brain based on anatomy was constructed by locating the intersection of all these tracts and recording the nuclei on the pathway of each white matter tract. Moreover, a compatible rat brain template from DTI images was created and standardized into the atlas space. To evaluate the automated application of the atlas in DTI data analysis, a group of rats with right-side middle cerebral artery occlusion (MCAO) and those without were enrolled in this study. The voxel-based analysis result shows that the brain region showing significant declines in signal in the MCAO rats was consistent with the occlusion position. We constructed a stereotaxic white matter atlas of the rat brain with fine tract delineation and a compatible template for the data analysis of DTI images of the rat brain. Copyright © 2017 Elsevier Inc. All rights reserved.

Strategies for Interactive Visualization of Large Scale Climate Simulations

NASA Astrophysics Data System (ADS)

Xie, J.; Chen, C.; Ma, K.; Parvis

2011-12-01

With the advances in computational methods and supercomputing technology, climate scientists are able to perform large-scale simulations at unprecedented resolutions. These simulations produce data that are time-varying, multivariate, and volumetric, and the data may contain thousands of time steps with each time step having billions of voxels and each voxel recording dozens of variables. Visualizing such time-varying 3D data to examine correlations between different variables thus becomes a daunting task. We have been developing strategies for interactive visualization and correlation analysis of multivariate data. The primary task is to find connection and correlation among data. Given the many complex interactions among the Earth's oceans, atmosphere, land, ice and biogeochemistry, and the sheer size of observational and climate model data sets, interactive exploration helps identify which processes matter most for a particular climate phenomenon. We may consider time-varying data as a set of samples (e.g., voxels or blocks), each of which is associated with a vector of representative or collective values over time. We refer to such a vector as a temporal curve. Correlation analysis thus operates on temporal curves of data samples. A temporal curve can be treated as a two-dimensional function where the two dimensions are time and data value. It can also be treated as a point in the high-dimensional space. In this case, to facilitate effective analysis, it is often necessary to transform temporal curve data from the original space to a space of lower dimensionality. Clustering and segmentation of temporal curve data in the original or transformed space provides us a way to categorize and visualize data of different patterns, which reveals connection or correlation of data among different variables or at different spatial locations. We have employed the power of GPU to enable interactive correlation visualization for studying the variability and correlations of a single or a pair of variables. It is desired to create a succinct volume classification that summarizes the connection among all correlation volumes with respect to various reference locations. Providing a reference location must correspond to a voxel position, the number of correlation volumes equals the total number of voxels. A brute-force solution takes all correlation volumes as the input and classifies their corresponding voxels according to their correlation volumes' distance. For large-scale time-varying multivariate data, calculating all these correlation volumes on-the-fly and analyzing the relationships among them is not feasible. We have developed a sampling-based approach for volume classification in order to reduce the computation cost of computing the correlation volumes. Users are able to employ their domain knowledge in selecting important samples. The result is a static view that captures the essence of correlation relationships; i.e., for all voxels in the same cluster, their corresponding correlation volumes are similar. This sampling-based approach enables us to obtain an approximation of correlation relations in a cost-effective manner, thus leading to a scalable solution to investigate large-scale data sets. These techniques empower climate scientists to study large data from their simulations.

Multiparametric voxel-based analyses of standardized uptake values and apparent diffusion coefficients of soft-tissue tumours with a positron emission tomography/magnetic resonance system: Preliminary results.

PubMed

Sagiyama, Koji; Watanabe, Yuji; Kamei, Ryotaro; Hong, Sungtak; Kawanami, Satoshi; Matsumoto, Yoshihiro; Honda, Hiroshi

2017-12-01

To investigate the usefulness of voxel-based analysis of standardized uptake values (SUVs) and apparent diffusion coefficients (ADCs) for evaluating soft-tissue tumour malignancy with a PET/MR system. Thirty-five subjects with either ten low/intermediate-grade tumours or 25 high-grade tumours were prospectively enrolled. Zoomed diffusion-weighted and fluorodeoxyglucose ( 18 FDG)-PET images were acquired along with fat-suppressed T2-weighted images (FST2WIs). Regions of interest (ROIs) were drawn on FST2WIs including the tumour in all slices. ROIs were pasted onto PET and ADC-maps to measure SUVs and ADCs within tumour ROIs. Tumour volume, SUVmax, ADCminimum, the heterogeneity and the correlation coefficients of SUV and ADC were recorded. The parameters of high- and low/intermediate-grade groups were compared, and receiver operating characteristic (ROC) analysis was also performed. The mean correlation coefficient for SUV and ADC in high-grade sarcomas was lower than that of low/intermediate-grade tumours (-0.41 ± 0.25 vs. -0.08 ± 0.34, P < 0.01). Other parameters did not differ significantly. ROC analysis demonstrated that correlation coefficient showed the best diagnostic performance for differentiating the two groups (AUC 0.79, sensitivity 96.0%, specificity 60%, accuracy 85.7%). SUV and ADC determined via PET/MR may be useful for differentiating between high-grade and low/intermediate-grade soft tissue tumours. • PET/MR allows voxel-based comparison of SUVs and ADCs in soft-tissue tumours. • A comprehensive assessment of internal heterogeneity was performed with scatter plots. • SUVmax or ADCminimum could not differentiate high-grade sarcoma from low/intermediate-grade tumours. • Only the correlation coefficient between SUV and ADC differentiated the two groups. • The correlation coefficient showed the best diagnostic performance by ROC analysis.

Brainstem Involvement as a Cause of Central Sleep Apnea: Pattern of Microstructural Cerebral Damage in Patients with Cerebral Microangiopathy

PubMed Central

Duning, Thomas; Deppe, Michael; Brand, Eva; Stypmann, Jörg; Becht, Charlotte; Heidbreder, Anna; Young, Peter

2013-01-01

Background The exact underlying pathomechanism of central sleep apnea with Cheyne-Stokes respiration (CSA-CSR) is still unclear. Recent studies have demonstrated an association between cerebral white matter changes and CSA. A dysfunction of central respiratory control centers in the brainstem was suggested by some authors. Novel MR-imaging analysis tools now allow far more subtle assessment of microstructural cerebral changes. The aim of this study was to investigate whether and what severity of subtle structural cerebral changes could lead to CSA-CSR, and whether there is a specific pattern of neurodegenerative changes that cause CSR. Therefore, we examined patients with Fabry disease (FD), an inherited, lysosomal storage disease. White matter lesions are early and frequent findings in FD. Thus, FD can serve as a "model disease" of cerebral microangiopathy to study in more detail the impact of cerebral lesions on central sleep apnea. Patients and Methods Genetically proven FD patients (n = 23) and age-matched healthy controls (n = 44) underwent a cardio-respiratory polysomnography and brain MRI at 3.0 Tesla. We applied different MR-imaging techniques, ranging from semiquantitative measurement of white matter lesion (WML) volumes and automated calculation of brain tissue volumes to VBM of gray matter and voxel-based diffusion tensor imaging (DTI) analysis. Results In 5 of 23 Fabry patients (22%) CSA-CSR was detected. Voxel-based DTI analysis revealed widespread structural changes in FD patients when compared to the healthy controls. When calculated as a separate group, DTI changes of CSA-CSR patients were most prominent in the brainstem. Voxel-based regression analysis revealed a significant association between CSR severity and microstructural DTI changes within the brainstem. Conclusion Subtle microstructural changes in the brainstem might be a neuroanatomical correlate of CSA-CSR in patients at risk of WML. DTI is more sensitive and specific than conventional structural MRI and other advanced MR analyses tools in demonstrating these abnormalities. PMID:23637744

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.

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.

Construction and evaluation of quantitative small-animal PET probabilistic atlases for [¹⁸F]FDG and [¹⁸F]FECT functional mapping of the mouse brain.

PubMed

Casteels, Cindy; Vunckx, Kathleen; Aelvoet, Sarah-Ann; Baekelandt, Veerle; Bormans, Guy; Van Laere, Koen; Koole, Michel

2013-01-01

Automated voxel-based or pre-defined volume-of-interest (VOI) analysis of small-animal PET data in mice is necessary for optimal information usage as the number of available resolution elements is limited. We have mapped metabolic ([(18)F]FDG) and dopamine transporter ([(18)F]FECT) small-animal PET data onto a 3D Magnetic Resonance Microscopy (MRM) mouse brain template and aligned them in space to the Paxinos co-ordinate system. In this way, ligand-specific templates for sensitive analysis and accurate anatomical localization were created. Next, using a pre-defined VOI approach, test-retest and intersubject variability of various quantification methods were evaluated. Also, the feasibility of mouse brain statistical parametric mapping (SPM) was explored for [(18)F]FDG and [(18)F]FECT imaging of 6-hydroxydopamine-lesioned (6-OHDA) mice. Twenty-three adult C57BL6 mice were scanned with [(18)F]FDG and [(18)F]FECT. Registrations and affine spatial normalizations were performed using SPM8. [(18)F]FDG data were quantified using (1) an image-derived-input function obtained from the liver (cMRglc), using (2) standardized uptake values (SUVglc) corrected for blood glucose levels and by (3) normalizing counts to the whole-brain uptake. Parametric [(18)F]FECT binding images were constructed by reference to the cerebellum. Registration accuracy was determined using random simulated misalignments and vectorial mismatch determination. Registration accuracy was between 0.21-1.11 mm. Regional intersubject variabilities of cMRglc ranged from 15.4% to 19.2%, while test-retest values were between 5.0% and 13.0%. For [(18)F]FECT uptake in the caudate-putamen, these values were 13.0% and 10.3%, respectively. Regional values of cMRglc positively correlated to SUVglc measured within the 45-60 min time frame (spearman r = 0.71). Next, SPM analysis of 6-OHDA-lesioned mice showed hypometabolism in the bilateral caudate-putamen and cerebellum, and an unilateral striatal decrease in DAT availability. MRM-based small-animal PET templates facilitate accurate assessment and spatial localization of mouse brain function using VOI or voxel-based analysis. Regional intersubject- and test-retest variations indicate that for these targets accuracy comparable to humans can be achieved.

Analysis of alterations in white matter integrity of adult patients with comitant exotropia.

PubMed

Li, Dan; Li, Shenghong; Zeng, Xianjun

2018-05-01

Objective This study was performed to investigate structural abnormalities of the white matter in patients with comitant exotropia using the tract-based spatial statistics (TBSS) method. Methods Diffusion tensor imaging data from magnetic resonance images of the brain were collected from 20 patients with comitant exotropia and 20 age- and sex-matched healthy controls. The FMRIB Software Library was used to compute the diffusion measures, including fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD). These measures were obtained using voxel-wise statistics with threshold-free cluster enhancement. Results The FA values in the right inferior fronto-occipital fasciculus (IFO) and right inferior longitudinal fasciculus were significantly higher and the RD values in the bilateral IFO, forceps minor, left anterior corona radiata, and left anterior thalamic radiation were significantly lower in the comitant exotropia group than in the healthy controls. No significant differences in the MD or AD values were found between the two groups. Conclusions Alterations in FA and RD values may indicate the underlying neuropathologic mechanism of comitant exotropia. The TBSS method can be a useful tool to investigate neuronal tract participation in patients with this disease.

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.

Simultaneous Control of Error Rates in fMRI Data Analysis

PubMed Central

Kang, Hakmook; Blume, Jeffrey; Ombao, Hernando; Badre, David

2015-01-01

The key idea of statistical hypothesis testing is to fix, and thereby control, the Type I error (false positive) rate across samples of any size. Multiple comparisons inflate the global (family-wise) Type I error rate and the traditional solution to maintaining control of the error rate is to increase the local (comparison-wise) Type II error (false negative) rates. However, in the analysis of human brain imaging data, the number of comparisons is so large that this solution breaks down: the local Type II error rate ends up being so large that scientifically meaningful analysis is precluded. Here we propose a novel solution to this problem: allow the Type I error rate to converge to zero along with the Type II error rate. It works because when the Type I error rate per comparison is very small, the accumulation (or global) Type I error rate is also small. This solution is achieved by employing the Likelihood paradigm, which uses likelihood ratios to measure the strength of evidence on a voxel-by-voxel basis. In this paper, we provide theoretical and empirical justification for a likelihood approach to the analysis of human brain imaging data. In addition, we present extensive simulations that show the likelihood approach is viable, leading to ‘cleaner’ looking brain maps and operationally superiority (lower average error rate). Finally, we include a case study on cognitive control related activation in the prefrontal cortex of the human brain. PMID:26272730

Sexual differentiation of the adolescent rat brain: A longitudinal voxel-based morphometry study.

PubMed

Sumiyoshi, Akira; Nonaka, Hiroi; Kawashima, Ryuta

2017-03-06

The sexual differentiation of the rat brain during the adolescent period has been well documented in post-mortem histological studies. However, to further understand the morphological changes occurring in the entire brain, a noninvasive neuroimaging method allowing an unbiased, comprehensive, and longitudinal investigation of brain morphology should be used. In this study, we investigated the sexual differentiation of the rat brain during the adolescent period using longitudinal voxel-based morphometry (VBM) analysis. Male and female Wistar rats (n=12 of each) were scanned in a 7.0-T MRI scanner at five time points from 6 to 10 weeks of age. The T2-weighted MRI images were segmented using the rat brain tissue priors that have been published by our laboratory. At the global level, the results of the VBM analysis showed greater increases in total gray matter volume in the males during the adolescent period, although we did not find significant differences in total white matter volume. At the voxel level, we found significant increases in the regional gray matter volume of the occipital cortex, amygdala, hippocampal formation, and cerebellum. At the regional level, only the occipital cortex in the females exhibited decreases during the adolescent period. These results were, at least in part, consistent with those of previous longitudinal VBM studies in humans, thus providing translational evidence of the sexual differentiation of the developing brain between rodents and humans. Copyright © 2017 Elsevier B.V. All rights reserved.

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.

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.

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

A genome-scale map of expression for a mouse brain section obtained using voxelation

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

Chin, Mark H.; Geng, Alex B.; Khan, Arshad H.

Gene expression signatures in the mammalian brain hold the key to understanding neural development and neurological diseases. We have reconstructed 2- dimensional images of gene expression for 20,000 genes in a coronal slice of the mouse brain at the level of the striatum by using microarrays in combination with voxelation at a resolution of 1 mm3. Good reliability of the microarray results were confirmed using multiple replicates, subsequent quantitative RT-PCR voxelation, mass spectrometry voxelation and publicly available in situ hybridization data. Known and novel genes were identified with expression patterns localized to defined substructures within the brain. In addition, genesmore » with unexpected patterns were identified and cluster analysis identified a set of genes with a gradient of dorsal/ventral expression not restricted to known anatomical boundaries. The genome-scale maps of gene expression obtained using voxelation will be a valuable tool for the neuroscience community.« less

Are morphological changes necessary to mediate the therapeutic effects of electroconvulsive therapy?

PubMed

Nickl-Jockschat, Thomas; Palomero Gallagher, Nicola; Kumar, Vinod; Hoffstaedter, Felix; Brügmann, Elisabeth; Habel, Ute; Eickhoff, Simon B; Grözinger, Michael

2016-04-01

The neurotrophic hypothesis has become the favorite model to explain the antidepressant properties of electroconvulsive therapy (ECT). It is based on the assumption that a restoration of previously defective neural networks drives therapeutic effects. Recent data in rather young patients suggest that neurotrophic effects of ECT might be detectable by diffusion tensor imaging. We here aimed to investigate whether the therapeutic response to ECT necessarily goes along with mesoscopic effects in gray matter (GM) or white matter (WM) in our patients in advanced age. Patients (n = 21, 15 males and 7 females) suffering from major depressive disorder were treated with ECT. Before the start of treatment and after the completion of the index series, they underwent magnetic resonance imaging, including a diffusion-weighed sequence. We used voxel-based morphometry to assess GM changes and tract-based spatial statistics and an SPM-based whole-brain analysis to detect WM changes in the course of treatment. Patients significantly improved clinically during the course of ECT. This was, however, not accompanied by GM or WM changes. This result challenges the notion that mesoscopic brain structure changes are an obligatory prerequisite for the antidepressant effects of ECT.

Fast fMRI provides high statistical power in the analysis of epileptic networks.

PubMed

Jacobs, Julia; Stich, Julia; Zahneisen, Benjamin; Assländer, Jakob; Ramantani, Georgia; Schulze-Bonhage, Andreas; Korinthenberg, Rudolph; Hennig, Jürgen; LeVan, Pierre

2014-03-01

EEG-fMRI is a unique method to combine the high temporal resolution of EEG with the high spatial resolution of MRI to study generators of intrinsic brain signals such as sleep grapho-elements or epileptic spikes. While the standard EPI sequence in fMRI experiments has a temporal resolution of around 2.5-3s a newly established fast fMRI sequence called MREG (Magnetic-Resonance-Encephalography) provides a temporal resolution of around 100ms. This technical novelty promises to improve statistics, facilitate correction of physiological artifacts and improve the understanding of epileptic networks in fMRI. The present study compares simultaneous EEG-EPI and EEG-MREG analyzing epileptic spikes to determine the yield of fast MRI in the analysis of intrinsic brain signals. Patients with frequent interictal spikes (>3/20min) underwent EEG-MREG and EEG-EPI (3T, 20min each, voxel size 3×3×3mm, EPI TR=2.61s, MREG TR=0.1s). Timings of the spikes were used in an event-related analysis to generate activation maps of t-statistics. (FMRISTAT, |t|>3.5, cluster size: 7 voxels, p<0.05 corrected). For both sequences, the amplitude and location of significant BOLD activations were compared with the spike topography. 13 patients were recorded and 33 different spike types could be analyzed. Peak T-values were significantly higher in MREG than in EPI (p<0.0001). Positive BOLD effects correlating with the spike topography were found in 8/29 spike types using the EPI and in 22/33 spikes types using the MREG sequence. Negative BOLD responses in the default mode network could be observed in 3/29 spike types with the EPI and in 19/33 with the MREG sequence. With the latter method, BOLD changes were observed even when few spikes occurred during the investigation. Simultaneous EEG-MREG thus is possible with good EEG quality and shows higher sensitivity in regard to the localization of spike-related BOLD responses than EEG-EPI. The development of new methods of analysis for this sequence such as modeling of physiological noise, temporal analysis of the BOLD signal and defining appropriate thresholds is required to fully profit from its high temporal resolution. © 2013.

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.

Female pelvic synthetic CT generation based on joint intensity and shape analysis

NASA Astrophysics Data System (ADS)

Liu, Lianli; Jolly, Shruti; Cao, Yue; Vineberg, Karen; Fessler, Jeffrey A.; Balter, James M.

2017-04-01

Using MRI for radiotherapy treatment planning and image guidance is appealing as it provides superior soft tissue information over CT scans and avoids possible systematic errors introduced by aligning MR to CT images. This study presents a method that generates Synthetic CT (MRCT) volumes by performing probabilistic tissue classification of voxels from MRI data using a single imaging sequence (T1 Dixon). The intensity overlap between different tissues on MR images, a major challenge for voxel-based MRCT generation methods, is addressed by adding bone shape information to an intensity-based classification scheme. A simple pelvic bone shape model, built from principal component analysis of pelvis shape from 30 CT image volumes, is fitted to the MR volumes. The shape model generates a rough bone mask that excludes air and covers bone along with some surrounding soft tissues. Air regions are identified and masked out from the tissue classification process by intensity thresholding outside the bone mask. A regularization term is added to the fuzzy c-means classification scheme that constrains voxels outside the bone mask from being assigned memberships in the bone class. MRCT image volumes are generated by multiplying the probability of each voxel being represented in each class with assigned attenuation values of the corresponding class and summing the result across all classes. The MRCT images presented intensity distributions similar to CT images with a mean absolute error of 13.7 HU for muscle, 15.9 HU for fat, 49.1 HU for intra-pelvic soft tissues, 129.1 HU for marrow and 274.4 HU for bony tissues across 9 patients. Volumetric modulated arc therapy (VMAT) plans were optimized using MRCT-derived electron densities, and doses were recalculated using corresponding CT-derived density grids. Dose differences to planning target volumes were small with mean/standard deviation of 0.21/0.42 Gy for D0.5cc and 0.29/0.33 Gy for D99%. The results demonstrate the accuracy of the method and its potential in supporting MRI only radiotherapy treatment planning.

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

Multi-modal magnetic resonance imaging in the acute and sub-acute phase of mild traumatic brain injury: can we see the difference?

PubMed

Toth, Arnold; Kovacs, Noemi; Perlaki, Gabor; Orsi, Gergely; Aradi, Mihaly; Komaromy, Hedvig; Ezer, Erzsebet; Bukovics, Peter; Farkas, Orsolya; Janszky, Jozsef; Doczi, Tamas; Buki, Andras; Schwarcz, Attila

2013-01-01

Advanced magnetic resonance imaging (MRI) methods were shown to be able to detect the subtle structural consequences of mild traumatic brain injury (mTBI). The objective of this study was to investigate the acute structural alterations and recovery after mTBI, using diffusion tensor imaging (DTI) to reveal axonal pathology, volumetric analysis, and susceptibility weighted imaging (SWI) to detect microhemorrhage. Fourteen patients with mTBI who had computed tomography with negative results underwent MRI within 3 days and 1 month after injury. High resolution T1-weighted imaging, DTI, and SWI, were performed at both time points. A control group of 14 matched volunteers were also examined following the same imaging protocol and time interval. Tract-Based Spatial Statistics (TBSS) were performed on DTI data to reveal group differences. T1-weighted images were fed into Freesurfer volumetric analysis. TBSS showed fractional anisotropy (FA) to be significantly (corrected p<0.05) lower, and mean diffusivity (MD) to be higher in the mTBI group in several white matter tracts (FA=40,737; MD=39,078 voxels) compared with controls at 72 hours after injury and still 1month later for FA. Longitudinal analysis revealed significant change (i.e., normalization) of FA and MD over 1 month dominantly in the left hemisphere (FA=3408; MD=7450 voxels). A significant (p<0.05) decrease in cortical volumes (mean 1%) and increase in ventricular volumes (mean 3.4%) appeared at 1 month after injury in the mTBI group. SWI did not reveal microhemorrhage in our patients. Our findings present dynamic micro- and macrostructural changes occurring in the acute to sub-acute phase in mTBI, in very mildly injured patients lacking microhemorrhage detectable by SWI. These results underscore the importance of strictly defined image acquisition time points when performing MRI studies on patients with mTBI.

INVESTIGATING DIFFERENCES IN BRAIN FUNCTIONAL NETWORKS USING HIERARCHICAL COVARIATE-ADJUSTED INDEPENDENT COMPONENT ANALYSIS.

PubMed

Shi, Ran; Guo, Ying

2016-12-01

Human brains perform tasks via complex functional networks consisting of separated brain regions. A popular approach to characterize brain functional networks in fMRI studies is independent component analysis (ICA), which is a powerful method to reconstruct latent source signals from their linear mixtures. In many fMRI studies, an important goal is to investigate how brain functional networks change according to specific clinical and demographic variabilities. Existing ICA methods, however, cannot directly incorporate covariate effects in ICA decomposition. Heuristic post-ICA analysis to address this need can be inaccurate and inefficient. In this paper, we propose a hierarchical covariate-adjusted ICA (hc-ICA) model that provides a formal statistical framework for estimating covariate effects and testing differences between brain functional networks. Our method provides a more reliable and powerful statistical tool for evaluating group differences in brain functional networks while appropriately controlling for potential confounding factors. We present an analytically tractable EM algorithm to obtain maximum likelihood estimates of our model. We also develop a subspace-based approximate EM that runs significantly faster while retaining high accuracy. To test the differences in functional networks, we introduce a voxel-wise approximate inference procedure which eliminates the need of computationally expensive covariance matrix estimation and inversion. We demonstrate the advantages of our methods over the existing method via simulation studies. We apply our method to an fMRI study to investigate differences in brain functional networks associated with post-traumatic stress disorder (PTSD).

Iterative approach of dual regression with a sparse prior enhances the performance of independent component analysis for group functional magnetic resonance imaging (fMRI) data.

PubMed

Kim, Yong-Hwan; Kim, Junghoe; Lee, Jong-Hwan

2012-12-01

This study proposes an iterative dual-regression (DR) approach with sparse prior regularization to better estimate an individual's neuronal activation using the results of an independent component analysis (ICA) method applied to a temporally concatenated group of functional magnetic resonance imaging (fMRI) data (i.e., Tc-GICA method). An ordinary DR approach estimates the spatial patterns (SPs) of neuronal activation and corresponding time courses (TCs) specific to each individual's fMRI data with two steps involving least-squares (LS) solutions. Our proposed approach employs iterative LS solutions to refine both the individual SPs and TCs with an additional a priori assumption of sparseness in the SPs (i.e., minimally overlapping SPs) based on L(1)-norm minimization. To quantitatively evaluate the performance of this approach, semi-artificial fMRI data were created from resting-state fMRI data with the following considerations: (1) an artificially designed spatial layout of neuronal activation patterns with varying overlap sizes across subjects and (2) a BOLD time series (TS) with variable parameters such as onset time, duration, and maximum BOLD levels. To systematically control the spatial layout variability of neuronal activation patterns across the "subjects" (n=12), the degree of spatial overlap across all subjects was varied from a minimum of 1 voxel (i.e., 0.5-voxel cubic radius) to a maximum of 81 voxels (i.e., 2.5-voxel radius) across the task-related SPs with a size of 100 voxels for both the block-based and event-related task paradigms. In addition, several levels of maximum percentage BOLD intensity (i.e., 0.5, 1.0, 2.0, and 3.0%) were used for each degree of spatial overlap size. From the results, the estimated individual SPs of neuronal activation obtained from the proposed iterative DR approach with a sparse prior showed an enhanced true positive rate and reduced false positive rate compared to the ordinary DR approach. The estimated TCs of the task-related SPs from our proposed approach showed greater temporal correlation coefficients with a reference hemodynamic response function than those of the ordinary DR approach. Moreover, the efficacy of the proposed DR approach was also successfully demonstrated by the results of real fMRI data acquired from left-/right-hand clenching tasks in both block-based and event-related task paradigms. Copyright © 2012 Elsevier Inc. All rights reserved.

Efficient Posterior Probability Mapping Using Savage-Dickey Ratios

PubMed Central

Penny, William D.; Ridgway, Gerard R.

2013-01-01

Statistical Parametric Mapping (SPM) is the dominant paradigm for mass-univariate analysis of neuroimaging data. More recently, a Bayesian approach termed Posterior Probability Mapping (PPM) has been proposed as an alternative. PPM offers two advantages: (i) inferences can be made about effect size thus lending a precise physiological meaning to activated regions, (ii) regions can be declared inactive. This latter facility is most parsimoniously provided by PPMs based on Bayesian model comparisons. To date these comparisons have been implemented by an Independent Model Optimization (IMO) procedure which separately fits null and alternative models. This paper proposes a more computationally efficient procedure based on Savage-Dickey approximations to the Bayes factor, and Taylor-series approximations to the voxel-wise posterior covariance matrices. Simulations show the accuracy of this Savage-Dickey-Taylor (SDT) method to be comparable to that of IMO. Results on fMRI data show excellent agreement between SDT and IMO for second-level models, and reasonable agreement for first-level models. This Savage-Dickey test is a Bayesian analogue of the classical SPM-F and allows users to implement model comparison in a truly interactive manner. PMID:23533640

From Cortical and Subcortical Grey Matter Abnormalities to Neurobehavioral Phenotype of Angelman Syndrome: A Voxel-Based Morphometry Study

PubMed Central

Aghakhanyan, Gayane; Bonanni, Paolo; Randazzo, Giovanna; Nappi, Sara; Tessarotto, Federica; De Martin, Lara; Frijia, Francesca; De Marchi, Daniele; De Masi, Francesco; Kuppers, Beate; Lombardo, Francesco; Caramella, Davide; Montanaro, Domenico

2016-01-01

Angelman syndrome (AS) is a rare neurogenetic disorder due to loss of expression of maternal ubiquitin-protein ligase E3A (UBE3A) gene. It is characterized by severe developmental delay, speech impairment, movement or balance disorder and typical behavioral uniqueness. Affected individuals show normal magnetic resonance imaging (MRI) findings, although mild dysmyelination may be observed. In this study, we adopted a quantitative MRI analysis with voxel-based morphometry (FSL-VBM) method to investigate disease-related changes in the cortical/subcortical grey matter (GM) structures. Since 2006 to 2013 twenty-six AS patients were assessed by our multidisciplinary team. From those, sixteen AS children with confirmed maternal 15q11-q13 deletions (mean age 7.7 ± 3.6 years) and twenty-one age-matched controls were recruited. The developmental delay and motor dysfunction were assessed using Bayley III and Gross Motor Function Measure (GMFM). Principal component analysis (PCA) was applied to the clinical and neuropsychological datasets. High-resolution T1-weighted images were acquired and FSL-VBM approach was applied to investigate differences in the local GM volume and to correlate clinical and neuropsychological changes in the regional distribution of GM. We found bilateral GM volume loss in AS compared to control children in the striatum, limbic structures, insular and orbitofrontal cortices. Voxel-wise correlation analysis with the principal components of the PCA output revealed a strong relationship with GM volume in the superior parietal lobule and precuneus on the left hemisphere. The anatomical distribution of cortical/subcortical GM changes plausibly related to several clinical features of the disease and may provide an important morphological underpinning for clinical and neurobehavioral symptoms in children with AS. PMID:27626634

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.

A meta-analysis of sex differences in human brain structure.

PubMed

Ruigrok, Amber N V; Salimi-Khorshidi, Gholamreza; Lai, Meng-Chuan; Baron-Cohen, Simon; Lombardo, Michael V; Tait, Roger J; Suckling, John

2014-02-01

The prevalence, age of onset, and symptomatology of many neuropsychiatric conditions differ between males and females. To understand the causes and consequences of sex differences it is important to establish where they occur in the human brain. We report the first meta-analysis of typical sex differences on global brain volume, a descriptive account of the breakdown of studies of each compartmental volume by six age categories, and whole-brain voxel-wise meta-analyses on brain volume and density. Gaussian-process regression coordinate-based meta-analysis was used to examine sex differences in voxel-based regional volume and density. On average, males have larger total brain volumes than females. Examination of the breakdown of studies providing total volumes by age categories indicated a bias towards the 18-59 year-old category. Regional sex differences in volume and tissue density include the amygdala, hippocampus and insula, areas known to be implicated in sex-biased neuropsychiatric conditions. Together, these results suggest candidate regions for investigating the asymmetric effect that sex has on the developing brain, and for understanding sex-biased neurological and psychiatric conditions. Copyright © 2014 The Authors. Published by Elsevier Ltd.. All rights reserved.

Intrinsic disruption of white matter microarchitecture in first-episode, drug-naive major depressive disorder: A voxel-based meta-analysis of diffusion tensor imaging.

PubMed

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

2017-06-02

Previous studies have demonstrated the influences of episodes and antidepressant drugs on white matter (WM) in patients with major depressive disorder (MDD). However, most diffusion tensor imaging (DTI) studies included highly heterogeneous individuals with different numbers of depressive episodes or medication status. To exclude the confounding effects of multiple episodes or medication, we conducted a quantitative voxel-based meta-analysis of fractional anisotropy (FA) in patients with first-episode, drug-naive MDD to identify the intrinsic WM alterations involved in the pathogenesis of MDD. The pooled meta-analysis revealed significant FA reductions in the body of the corpus callosum (CC), bilateral anterior limb of the internal capsule (ALIC), right inferior temporal gyrus (ITG) and right superior frontal gyrus (SFG) in MDD patients relative to healthy controls. Meta-regression analyses revealed that FA reduction in the right ALIC and right SFG was negatively correlated with symptom severity and duration of depression, respectively. Our findings provide robust evidence that the WM impairments in the interhemispheric connections and frontal-subcortical neuronal circuits may play an important role in MDD pathogenesis. Copyright © 2017. Published by Elsevier Inc.

Calculation of dose contributions of electron and charged heavy particles inside phantoms irradiated by monoenergetic neutron.

PubMed

Satoh, Daiki; Takahashi, Fumiaki; Endo, Akira; Ohmachi, Yasushi; Miyahara, Nobuyuki

2008-09-01

The radiation-transport code PHITS with an event generator mode has been applied to analyze energy depositions of electrons and charged heavy particles in two spherical phantoms and a voxel-based mouse phantom upon neutron irradiation. The calculations using the spherical phantoms quantitatively clarified the type and energy of charged particles which are released through interactions of neutrons with the phantom elements and contribute to the radiation dose. The relative contribution of electrons increased with an increase in the size of the phantom and with a decrease in the energy of the incident neutrons. Calculations with the voxel-based mouse phantom for 2.0-MeV neutron irradiation revealed that the doses to different locations inside the body are uniform, and that the energy is mainly deposited by recoil protons. The present study has demonstrated that analysis using PHITS can yield dose distributions that are accurate enough for RBE evaluation.

Robust membrane detection based on tensor voting for electron tomography.

PubMed

Martinez-Sanchez, Antonio; Garcia, Inmaculada; Asano, Shoh; Lucic, Vladan; Fernandez, Jose-Jesus

2014-04-01

Electron tomography enables three-dimensional (3D) visualization and analysis of the subcellular architecture at a resolution of a few nanometers. Segmentation of structural components present in 3D images (tomograms) is often necessary for their interpretation. However, it is severely hampered by a number of factors that are inherent to electron tomography (e.g. noise, low contrast, distortion). Thus, there is a need for new and improved computational methods to facilitate this challenging task. In this work, we present a new method for membrane segmentation that is based on anisotropic propagation of the local structural information using the tensor voting algorithm. The local structure at each voxel is then refined according to the information received from other voxels. Because voxels belonging to the same membrane have coherent structural information, the underlying global structure is strengthened. In this way, local information is easily integrated at a global scale to yield segmented structures. This method performs well under low signal-to-noise ratio typically found in tomograms of vitrified samples under cryo-tomography conditions and can bridge gaps present on membranes. The performance of the method is demonstrated by applications to tomograms of different biological samples and by quantitative comparison with standard template matching procedure. Copyright © 2014 Elsevier Inc. All rights reserved.

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

AUTOMATED CELL SEGMENTATION WITH 3D FLUORESCENCE MICROSCOPY IMAGES.

PubMed

Kong, Jun; Wang, Fusheng; Teodoro, George; Liang, Yanhui; Zhu, Yangyang; Tucker-Burden, Carol; Brat, Daniel J

2015-04-01

A large number of cell-oriented cancer investigations require an effective and reliable cell segmentation method on three dimensional (3D) fluorescence microscopic images for quantitative analysis of cell biological properties. In this paper, we present a fully automated cell segmentation method that can detect cells from 3D fluorescence microscopic images. Enlightened by fluorescence imaging techniques, we regulated the image gradient field by gradient vector flow (GVF) with interpolated and smoothed data volume, and grouped voxels based on gradient modes identified by tracking GVF field. Adaptive thresholding was then applied to voxels associated with the same gradient mode where voxel intensities were enhanced by a multiscale cell filter. We applied the method to a large volume of 3D fluorescence imaging data of human brain tumor cells with (1) small cell false detection and missing rates for individual cells; and (2) trivial over and under segmentation incidences for clustered cells. Additionally, the concordance of cell morphometry structure between automated and manual segmentation was encouraging. These results suggest a promising 3D cell segmentation method applicable to cancer studies.

Homeostatic and Circadian Abnormalities in Sleep and Arousal in Gulf War Syndrome

DTIC Science & Technology

2016-10-01

prefrontal cortex have been reported in patients with clinical sleep disorders, including insomnia and cataplexy.7-9 A recent hdEEG analysis of...and parietal gray matter in chronic insomnia : a voxel-based morphometric study. Biol Psychiatry 2010;67:182-5. 8. Joo EY, Tae WS, Kim ST, Hong SB

The neural substrates of procrastination: A voxel-based morphometry study.

PubMed

Hu, Yue; Liu, Peiwei; Guo, Yiqun; Feng, Tingyong

2018-03-01

Procrastination is a pervasive phenomenon across different cultures and brings about lots of serious consequences, including performance, subjective well-being, and even public policy. However, little is known about the neural substrates of procrastination. In order to shed light upon this question, we investigated the neuroanatomical substrates of procrastination across two independent samples using voxel-based morphometry (VBM) method. The whole-brain analysis showed procrastination was positively correlated with the graymatter (GM) volume of clusters in the parahippocampal gyrus (PHG) and the orbital frontal cortex (OFC), while negatively correlated with the GM volume of clusters in the inferior frontal gyrus (IFG) and the middle frontal gyrus (MFG) in sample one (151 participants). We further conducted a verification procedure on another sample (108 participants) using region-of-interest analysis to examine the reliability of these results. Results showed procrastination can be predicted by the GM volume of the OFC and the MFG. The present findings suggest that the MFG and OFC, which are the key regions of self-control and emotion regulation, may play an important role in procrastination. Copyright © 2018 Elsevier Inc. All rights reserved.

Discrete Wavelet Transform-Based Whole-Spectral and Subspectral Analysis for Improved Brain Tumor Clustering Using Single Voxel MR Spectroscopy.

PubMed

Yang, Guang; Nawaz, Tahir; Barrick, Thomas R; Howe, Franklyn A; Slabaugh, Greg

2015-12-01

Many approaches have been considered for automatic grading of brain tumors by means of pattern recognition with magnetic resonance spectroscopy (MRS). Providing an improved technique which can assist clinicians in accurately identifying brain tumor grades is our main objective. The proposed technique, which is based on the discrete wavelet transform (DWT) of whole-spectral or subspectral information of key metabolites, combined with unsupervised learning, inspects the separability of the extracted wavelet features from the MRS signal to aid the clustering. In total, we included 134 short echo time single voxel MRS spectra (SV MRS) in our study that cover normal controls, low grade and high grade tumors. The combination of DWT-based whole-spectral or subspectral analysis and unsupervised clustering achieved an overall clustering accuracy of 94.8% and a balanced error rate of 7.8%. To the best of our knowledge, it is the first study using DWT combined with unsupervised learning to cluster brain SV MRS. Instead of dimensionality reduction on SV MRS or feature selection using model fitting, our study provides an alternative method of extracting features to obtain promising clustering results.

Beyond mind-reading: multi-voxel pattern analysis of fMRI data.

PubMed

Norman, Kenneth A; Polyn, Sean M; Detre, Greg J; Haxby, James V

2006-09-01

A key challenge for cognitive neuroscience is determining how mental representations map onto patterns of neural activity. Recently, researchers have started to address this question by applying sophisticated pattern-classification algorithms to distributed (multi-voxel) patterns of functional MRI data, with the goal of decoding the information that is represented in the subject's brain at a particular point in time. This multi-voxel pattern analysis (MVPA) approach has led to several impressive feats of mind reading. More importantly, MVPA methods constitute a useful new tool for advancing our understanding of neural information processing. We review how researchers are using MVPA methods to characterize neural coding and information processing in domains ranging from visual perception to memory search.

Tensor-product kernel-based representation encoding joint MRI view similarity.

PubMed

Alvarez-Meza, A; Cardenas-Pena, D; Castro-Ospina, A E; Alvarez, M; Castellanos-Dominguez, G

2014-01-01

To support 3D magnetic resonance image (MRI) analysis, a marginal image similarity (MIS) matrix holding MR inter-slice relationship along every axis view (Axial, Coronal, and Sagittal) can be estimated. However, mutual inference from MIS view information poses a difficult task since relationships between axes are nonlinear. To overcome this issue, we introduce a Tensor-Product Kernel-based Representation (TKR) that allows encoding brain structure patterns due to patient differences, gathering all MIS matrices into a single joint image similarity framework. The TKR training strategy is carried out into a low dimensional projected space to get less influence of voxel-derived noise. Obtained results for classifying the considered patient categories (gender and age) on real MRI database shows that the proposed TKR training approach outperforms the conventional voxel-wise sum of squared differences. The proposed approach may be useful to support MRI clustering and similarity inference tasks, which are required on template-based image segmentation and atlas construction.

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…

4D Cone-beam CT reconstruction using a motion model based on principal component analysis

PubMed Central

Staub, David; Docef, Alen; Brock, Robert S.; Vaman, Constantin; Murphy, Martin J.

2011-01-01

Purpose: To provide a proof of concept validation of a novel 4D cone-beam CT (4DCBCT) reconstruction algorithm and to determine the best methods to train and optimize the algorithm. Methods: The algorithm animates a patient fan-beam CT (FBCT) with a patient specific parametric motion model in order to generate a time series of deformed CTs (the reconstructed 4DCBCT) that track the motion of the patient anatomy on a voxel by voxel scale. The motion model is constrained by requiring that projections cast through the deformed CT time series match the projections of the raw patient 4DCBCT. The motion model uses a basis of eigenvectors that are generated via principal component analysis (PCA) of a training set of displacement vector fields (DVFs) that approximate patient motion. The eigenvectors are weighted by a parameterized function of the patient breathing trace recorded during 4DCBCT. The algorithm is demonstrated and tested via numerical simulation. Results: The algorithm is shown to produce accurate reconstruction results for the most complicated simulated motion, in which voxels move with a pseudo-periodic pattern and relative phase shifts exist between voxels. The tests show that principal component eigenvectors trained on DVFs from a novel 2D/3D registration method give substantially better results than eigenvectors trained on DVFs obtained by conventionally registering 4DCBCT phases reconstructed via filtered backprojection. Conclusions: Proof of concept testing has validated the 4DCBCT reconstruction approach for the types of simulated data considered. In addition, the authors found the 2D/3D registration approach to be our best choice for generating the DVF training set, and the Nelder-Mead simplex algorithm the most robust optimization routine. PMID:22149852

Brain regions associated with cognitive impairment in patients with Parkinson disease: quantitative analysis of cerebral blood flow using 123I iodoamphetamine SPECT.

PubMed

Hattori, Naoya; Yabe, Ichiro; Hirata, Kenji; Shiga, Tohru; Sakushima, Ken; Tsuji-Akimoto, Sachiko; Sasaki, Hidenao; Tamaki, Nagara

2013-05-01

Cognitive impairment is a representative neuropsychiatric presentation that accompanies Parkinson disease (PD). The purpose of this study was to localize the cerebral regions associated with cognitive impairment in patients with PD using quantitative SPECT. Thirty-two patients with PD (mean [SD] age, 75 [8] years; 25 women; Hoehn-Yahr scores from 2 to 5) underwent quantitative brain SPECT using 123I iodoamphetamine. Parametric images of regional cerebral blood flow (rCBF) were spatially normalized to the standard brain atlas. First, voxel-by-voxel comparison between patients with PD with versus without cognitive impairment was performed to visualize overall trend of regional differences. Next, the individual quantitative rCBF values were extracted in representative cortical regions using a standard region-of-interest template to compare the quantitative rCBF values. Patients with cognitive impairment showed trends of lower rCBF in the left frontal and temporal cortices as well as in the bilateral medial frontal and anterior cingulate cortices in the voxel-by-voxel analyses. Region-of-interest-based analysis demonstrated significantly lower rCBF in the bilateral anterior cingulate cortices (right, 25.8 [5.5] vs 28.9 [5.7] mL per 100 g/min, P < 0.05; left, 25.8 [5.8] vs 29.1 [5.7] mL per 100 g/min, P < 0.05) associated with cognitive impairment. Patients with cognitive impairment showed lower rCBF in the left frontal and temporal cortices as well as in the bilateral medial frontal and anterior cingulate cortices. The results suggested dysexecutive function as an underlining mechanism of cognitive impairment in patients with PD.

Motion compensation using origin ensembles in awake small animal positron emission tomography

NASA Astrophysics Data System (ADS)

Gillam, John E.; Angelis, Georgios I.; Kyme, Andre Z.; Meikle, Steven R.

2017-02-01

In emission tomographic imaging, the stochastic origin ensembles algorithm provides unique information regarding the detected counts given the measured data. Precision in both voxel and region-wise parameters may be determined for a single data set based on the posterior distribution of the count density allowing uncertainty estimates to be allocated to quantitative measures. Uncertainty estimates are of particular importance in awake animal neurological and behavioral studies for which head motion, unique for each acquired data set, perturbs the measured data. Motion compensation can be conducted when rigid head pose is measured during the scan. However, errors in pose measurements used for compensation can degrade the data and hence quantitative outcomes. In this investigation motion compensation and detector resolution models were incorporated into the basic origin ensembles algorithm and an efficient approach to computation was developed. The approach was validated against maximum liklihood—expectation maximisation and tested using simulated data. The resultant algorithm was then used to analyse quantitative uncertainty in regional activity estimates arising from changes in pose measurement precision. Finally, the posterior covariance acquired from a single data set was used to describe correlations between regions of interest providing information about pose measurement precision that may be useful in system analysis and design. The investigation demonstrates the use of origin ensembles as a powerful framework for evaluating statistical uncertainty of voxel and regional estimates. While in this investigation rigid motion was considered in the context of awake animal PET, the extension to arbitrary motion may provide clinical utility where respiratory or cardiac motion perturb the measured data.

Separation of β-amyloid binding and white matter uptake of 18F-flutemetamol using spectral analysis

PubMed Central

Heurling, Kerstin; Buckley, Christopher; Vandenberghe, Rik; Laere, Koen Van; Lubberink, Mark

2015-01-01

The kinetic components of the β-amyloid ligand 18F-flutemetamol binding in grey and white matter were investigated through spectral analysis, and a method developed for creation of parametric images separating grey and white matter uptake. Tracer uptake in grey and white matter and cerebellar cortex was analyzed through spectral analysis in six subjects, with (n=4) or without (n=2) apparent β-amyloid deposition, having undergone dynamic 18F-flutemetamol scanning with arterial blood sampling. The spectra were divided into three components: slow, intermediate and fast basis function rates. The contribution of each of the components to total volume of distribution (VT) was assessed for different tissue types. The slow component dominated in white matter (average 90%), had a higher contribution to grey matter VT in subjects with β-amyloid deposition (average 44%) than without (average 6%) and was absent in cerebellar cortex, attributing the slow component of 18F-flutemetamol uptake in grey matter to β-amyloid binding. Parametric images of voxel-based spectral analysis were created for VT, the slow component and images segmented based on the slow component contribution; confirming that grey matter and white matter uptake can be discriminated on voxel-level using a threshold for the contribution from the slow component to VT. PMID:26550542

Behavior, sensitivity, and power of activation likelihood estimation characterized by massive empirical simulation.

PubMed

Eickhoff, Simon B; Nichols, Thomas E; Laird, Angela R; Hoffstaedter, Felix; Amunts, Katrin; Fox, Peter T; Bzdok, Danilo; Eickhoff, Claudia R

2016-08-15

Given the increasing number of neuroimaging publications, the automated knowledge extraction on brain-behavior associations by quantitative meta-analyses has become a highly important and rapidly growing field of research. Among several methods to perform coordinate-based neuroimaging meta-analyses, Activation Likelihood Estimation (ALE) has been widely adopted. In this paper, we addressed two pressing questions related to ALE meta-analysis: i) Which thresholding method is most appropriate to perform statistical inference? ii) Which sample size, i.e., number of experiments, is needed to perform robust meta-analyses? We provided quantitative answers to these questions by simulating more than 120,000 meta-analysis datasets using empirical parameters (i.e., number of subjects, number of reported foci, distribution of activation foci) derived from the BrainMap database. This allowed to characterize the behavior of ALE analyses, to derive first power estimates for neuroimaging meta-analyses, and to thus formulate recommendations for future ALE studies. We could show as a first consequence that cluster-level family-wise error (FWE) correction represents the most appropriate method for statistical inference, while voxel-level FWE correction is valid but more conservative. In contrast, uncorrected inference and false-discovery rate correction should be avoided. As a second consequence, researchers should aim to include at least 20 experiments into an ALE meta-analysis to achieve sufficient power for moderate effects. We would like to note, though, that these calculations and recommendations are specific to ALE and may not be extrapolated to other approaches for (neuroimaging) meta-analysis. Copyright © 2016 Elsevier Inc. All rights reserved.

Quantifying Individual Brain Connectivity with Functional Principal Component Analysis for Networks.

PubMed

Petersen, Alexander; Zhao, Jianyang; Carmichael, Owen; Müller, Hans-Georg

2016-09-01

In typical functional connectivity studies, connections between voxels or regions in the brain are represented as edges in a network. Networks for different subjects are constructed at a given graph density and are summarized by some network measure such as path length. Examining these summary measures for many density values yields samples of connectivity curves, one for each individual. This has led to the adoption of basic tools of functional data analysis, most commonly to compare control and disease groups through the average curves in each group. Such group differences, however, neglect the variability in the sample of connectivity curves. In this article, the use of functional principal component analysis (FPCA) is demonstrated to enrich functional connectivity studies by providing increased power and flexibility for statistical inference. Specifically, individual connectivity curves are related to individual characteristics such as age and measures of cognitive function, thus providing a tool to relate brain connectivity with these variables at the individual level. This individual level analysis opens a new perspective that goes beyond previous group level comparisons. Using a large data set of resting-state functional magnetic resonance imaging scans, relationships between connectivity and two measures of cognitive function-episodic memory and executive function-were investigated. The group-based approach was implemented by dichotomizing the continuous cognitive variable and testing for group differences, resulting in no statistically significant findings. To demonstrate the new approach, FPCA was implemented, followed by linear regression models with cognitive scores as responses, identifying significant associations of connectivity in the right middle temporal region with both cognitive scores.

Behavior, Sensitivity, and power of activation likelihood estimation characterized by massive empirical simulation

PubMed Central

Eickhoff, Simon B.; Nichols, Thomas E.; Laird, Angela R.; Hoffstaedter, Felix; Amunts, Katrin; Fox, Peter T.

2016-01-01

Given the increasing number of neuroimaging publications, the automated knowledge extraction on brain-behavior associations by quantitative meta-analyses has become a highly important and rapidly growing field of research. Among several methods to perform coordinate-based neuroimaging meta-analyses, Activation Likelihood Estimation (ALE) has been widely adopted. In this paper, we addressed two pressing questions related to ALE meta-analysis: i) Which thresholding method is most appropriate to perform statistical inference? ii) Which sample size, i.e., number of experiments, is needed to perform robust meta-analyses? We provided quantitative answers to these questions by simulating more than 120,000 meta-analysis datasets using empirical parameters (i.e., number of subjects, number of reported foci, distribution of activation foci) derived from the BrainMap database. This allowed to characterize the behavior of ALE analyses, to derive first power estimates for neuroimaging meta-analyses, and to thus formulate recommendations for future ALE studies. We could show as a first consequence that cluster-level family-wise error (FWE) correction represents the most appropriate method for statistical inference, while voxel-level FWE correction is valid but more conservative. In contrast, uncorrected inference and false-discovery rate correction should be avoided. As a second consequence, researchers should aim to include at least 20 experiments into an ALE meta-analysis to achieve sufficient power for moderate effects. We would like to note, though, that these calculations and recommendations are specific to ALE and may not be extrapolated to other approaches for (neuroimaging) meta-analysis. PMID:27179606

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.

Implementation errors in the GingerALE Software: Description and recommendations.

PubMed

Eickhoff, Simon B; Laird, Angela R; Fox, P Mickle; Lancaster, Jack L; Fox, Peter T

2017-01-01

Neuroscience imaging is a burgeoning, highly sophisticated field the growth of which has been fostered by grant-funded, freely distributed software libraries that perform voxel-wise analyses in anatomically standardized three-dimensional space on multi-subject, whole-brain, primary datasets. Despite the ongoing advances made using these non-commercial computational tools, the replicability of individual studies is an acknowledged limitation. Coordinate-based meta-analysis offers a practical solution to this limitation and, consequently, plays an important role in filtering and consolidating the enormous corpus of functional and structural neuroimaging results reported in the peer-reviewed literature. In both primary data and meta-analytic neuroimaging analyses, correction for multiple comparisons is a complex but critical step for ensuring statistical rigor. Reports of errors in multiple-comparison corrections in primary-data analyses have recently appeared. Here, we report two such errors in GingerALE, a widely used, US National Institutes of Health (NIH)-funded, freely distributed software package for coordinate-based meta-analysis. These errors have given rise to published reports with more liberal statistical inferences than were specified by the authors. The intent of this technical report is threefold. First, we inform authors who used GingerALE of these errors so that they can take appropriate actions including re-analyses and corrective publications. Second, we seek to exemplify and promote an open approach to error management. Third, we discuss the implications of these and similar errors in a scientific environment dependent on third-party software. Hum Brain Mapp 38:7-11, 2017. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

Early musical training is linked to gray matter structure in the ventral premotor cortex and auditory-motor rhythm synchronization performance.

PubMed

Bailey, Jennifer Anne; Zatorre, Robert J; Penhune, Virginia B

2014-04-01

Evidence in animals and humans indicates that there are sensitive periods during development, times when experience or stimulation has a greater influence on behavior and brain structure. Sensitive periods are the result of an interaction between maturational processes and experience-dependent plasticity mechanisms. Previous work from our laboratory has shown that adult musicians who begin training before the age of 7 show enhancements in behavior and white matter structure compared with those who begin later. Plastic changes in white matter and gray matter are hypothesized to co-occur; therefore, the current study investigated possible differences in gray matter structure between early-trained (ET; <7) and late-trained (LT; >7) musicians, matched for years of experience. Gray matter structure was assessed using voxel-wise analysis techniques (optimized voxel-based morphometry, traditional voxel-based morphometry, and deformation-based morphometry) and surface-based measures (cortical thickness, surface area and mean curvature). Deformation-based morphometry analyses identified group differences between ET and LT musicians in right ventral premotor cortex (vPMC), which correlated with performance on an auditory motor synchronization task and with age of onset of musical training. In addition, cortical surface area in vPMC was greater for ET musicians. These results are consistent with evidence that premotor cortex shows greatest maturational change between the ages of 6-9 years and that this region is important for integrating auditory and motor information. We propose that the auditory and motor interactions required by musical practice drive plasticity in vPMC and that this plasticity is greatest when maturation is near its peak.

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.

Sparse SPM: Group Sparse-dictionary learning in SPM framework for resting-state functional connectivity MRI analysis.

PubMed

Lee, Young-Beom; Lee, Jeonghyeon; Tak, Sungho; Lee, Kangjoo; Na, Duk L; Seo, Sang Won; Jeong, Yong; Ye, Jong Chul

2016-01-15

Recent studies of functional connectivity MR imaging have revealed that the default-mode network activity is disrupted in diseases such as Alzheimer's disease (AD). However, there is not yet a consensus on the preferred method for resting-state analysis. Because the brain is reported to have complex interconnected networks according to graph theoretical analysis, the independency assumption, as in the popular independent component analysis (ICA) approach, often does not hold. Here, rather than using the independency assumption, we present a new statistical parameter mapping (SPM)-type analysis method based on a sparse graph model where temporal dynamics at each voxel position are described as a sparse combination of global brain dynamics. In particular, a new concept of a spatially adaptive design matrix has been proposed to represent local connectivity that shares the same temporal dynamics. If we further assume that local network structures within a group are similar, the estimation problem of global and local dynamics can be solved using sparse dictionary learning for the concatenated temporal data across subjects. Moreover, under the homoscedasticity variance assumption across subjects and groups that is often used in SPM analysis, the aforementioned individual and group analyses using sparse dictionary learning can be accurately modeled by a mixed-effect model, which also facilitates a standard SPM-type group-level inference using summary statistics. Using an extensive resting fMRI data set obtained from normal, mild cognitive impairment (MCI), and Alzheimer's disease patient groups, we demonstrated that the changes in the default mode network extracted by the proposed method are more closely correlated with the progression of Alzheimer's disease. Copyright © 2015 Elsevier Inc. All rights reserved.

Reconstruction of Human Monte Carlo Geometry from Segmented Images

NASA Astrophysics Data System (ADS)

Zhao, Kai; Cheng, Mengyun; Fan, Yanchang; Wang, Wen; Long, Pengcheng; Wu, Yican

2014-06-01

Human computational phantoms have been used extensively for scientific experimental analysis and experimental simulation. This article presented a method for human geometry reconstruction from a series of segmented images of a Chinese visible human dataset. The phantom geometry could actually describe detailed structure of an organ and could be converted into the input file of the Monte Carlo codes for dose calculation. A whole-body computational phantom of Chinese adult female has been established by FDS Team which is named Rad-HUMAN with about 28.8 billion voxel number. For being processed conveniently, different organs on images were segmented with different RGB colors and the voxels were assigned with positions of the dataset. For refinement, the positions were first sampled. Secondly, the large sums of voxels inside the organ were three-dimensional adjacent, however, there were not thoroughly mergence methods to reduce the cell amounts for the description of the organ. In this study, the voxels on the organ surface were taken into consideration of the mergence which could produce fewer cells for the organs. At the same time, an indexed based sorting algorithm was put forward for enhancing the mergence speed. Finally, the Rad-HUMAN which included a total of 46 organs and tissues was described by the cuboids into the Monte Carlo Monte Carlo Geometry for the simulation. The Monte Carlo geometry was constructed directly from the segmented images and the voxels was merged exhaustively. Each organ geometry model was constructed without ambiguity and self-crossing, its geometry information could represent the accuracy appearance and precise interior structure of the organs. The constructed geometry largely retaining the original shape of organs could easily be described into different Monte Carlo codes input file such as MCNP. Its universal property was testified and high-performance was experimentally verified

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

Empirical validation of statistical parametric mapping for group imaging of fast neural activity using electrical impedance tomography.

PubMed

Packham, B; Barnes, G; Dos Santos, G Sato; Aristovich, K; Gilad, O; Ghosh, A; Oh, T; Holder, D

2016-06-01

Electrical impedance tomography (EIT) allows for the reconstruction of internal conductivity from surface measurements. A change in conductivity occurs as ion channels open during neural activity, making EIT a potential tool for functional brain imaging. EIT images can have  >10 000 voxels, which means statistical analysis of such images presents a substantial multiple testing problem. One way to optimally correct for these issues and still maintain the flexibility of complicated experimental designs is to use random field theory. This parametric method estimates the distribution of peaks one would expect by chance in a smooth random field of a given size. Random field theory has been used in several other neuroimaging techniques but never validated for EIT images of fast neural activity, such validation can be achieved using non-parametric techniques. Both parametric and non-parametric techniques were used to analyze a set of 22 images collected from 8 rats. Significant group activations were detected using both techniques (corrected p  <  0.05). Both parametric and non-parametric analyses yielded similar results, although the latter was less conservative. These results demonstrate the first statistical analysis of such an image set and indicate that such an analysis is an approach for EIT images of neural activity.

Empirical validation of statistical parametric mapping for group imaging of fast neural activity using electrical impedance tomography

PubMed Central

Packham, B; Barnes, G; dos Santos, G Sato; Aristovich, K; Gilad, O; Ghosh, A; Oh, T; Holder, D

2016-01-01

Abstract Electrical impedance tomography (EIT) allows for the reconstruction of internal conductivity from surface measurements. A change in conductivity occurs as ion channels open during neural activity, making EIT a potential tool for functional brain imaging. EIT images can have  >10 000 voxels, which means statistical analysis of such images presents a substantial multiple testing problem. One way to optimally correct for these issues and still maintain the flexibility of complicated experimental designs is to use random field theory. This parametric method estimates the distribution of peaks one would expect by chance in a smooth random field of a given size. Random field theory has been used in several other neuroimaging techniques but never validated for EIT images of fast neural activity, such validation can be achieved using non-parametric techniques. Both parametric and non-parametric techniques were used to analyze a set of 22 images collected from 8 rats. Significant group activations were detected using both techniques (corrected p  <  0.05). Both parametric and non-parametric analyses yielded similar results, although the latter was less conservative. These results demonstrate the first statistical analysis of such an image set and indicate that such an analysis is an approach for EIT images of neural activity. PMID:27203477

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

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.

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.

Distinct Cortical Pathways for Music and Speech Revealed by Hypothesis-Free Voxel Decomposition

PubMed Central

Norman-Haignere, Sam

2015-01-01

SUMMARY The organization of human auditory cortex remains unresolved, due in part to the small stimulus sets common to fMRI studies and the overlap of neural populations within voxels. To address these challenges, we measured fMRI responses to 165 natural sounds and inferred canonical response profiles (“components”) whose weighted combinations explained voxel responses throughout auditory cortex. This analysis revealed six components, each with interpretable response characteristics despite being unconstrained by prior functional hypotheses. Four components embodied selectivity for particular acoustic features (frequency, spectrotemporal modulation, pitch). Two others exhibited pronounced selectivity for music and speech, respectively, and were not explainable by standard acoustic features. Anatomically, music and speech selectivity concentrated in distinct regions of non-primary auditory cortex. However, music selectivity was weak in raw voxel responses, and its detection required a decomposition method. Voxel decomposition identifies primary dimensions of response variation across natural sounds, revealing distinct cortical pathways for music and speech. PMID:26687225

Distinct Cortical Pathways for Music and Speech Revealed by Hypothesis-Free Voxel Decomposition.

PubMed

Norman-Haignere, Sam; Kanwisher, Nancy G; McDermott, Josh H

2015-12-16

The organization of human auditory cortex remains unresolved, due in part to the small stimulus sets common to fMRI studies and the overlap of neural populations within voxels. To address these challenges, we measured fMRI responses to 165 natural sounds and inferred canonical response profiles ("components") whose weighted combinations explained voxel responses throughout auditory cortex. This analysis revealed six components, each with interpretable response characteristics despite being unconstrained by prior functional hypotheses. Four components embodied selectivity for particular acoustic features (frequency, spectrotemporal modulation, pitch). Two others exhibited pronounced selectivity for music and speech, respectively, and were not explainable by standard acoustic features. Anatomically, music and speech selectivity concentrated in distinct regions of non-primary auditory cortex. However, music selectivity was weak in raw voxel responses, and its detection required a decomposition method. Voxel decomposition identifies primary dimensions of response variation across natural sounds, revealing distinct cortical pathways for music and speech. Copyright © 2015 Elsevier Inc. All rights reserved.

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

Mapping neurotransmitter networks with PET: an example on serotonin and opioid systems.

PubMed

Tuominen, Lauri; Nummenmaa, Lauri; Keltikangas-Järvinen, Liisa; Raitakari, Olli; Hietala, Jarmo

2014-05-01

All functions of the human brain are consequences of altered activity of specific neural pathways and neurotransmitter systems. Although the knowledge of "system level" connectivity in the brain is increasing rapidly, we lack "molecular level" information on brain networks and connectivity patterns. We introduce novel voxel-based positron emission tomography (PET) methods for studying internal neurotransmitter network structure and intercorrelations of different neurotransmitter systems in the human brain. We chose serotonin transporter and μ-opioid receptor for this analysis because of their functional interaction at the cellular level and similar regional distribution in the brain. Twenty-one healthy subjects underwent two consecutive PET scans using [(11)C]MADAM, a serotonin transporter tracer, and [(11)C]carfentanil, a μ-opioid receptor tracer. First, voxel-by-voxel "intracorrelations" (hub and seed analyses) were used to study the internal structure of opioid and serotonin systems. Second, voxel-level opioid-serotonin intercorrelations (between neurotransmitters) were computed. Regional μ-opioid receptor binding potentials were uniformly correlated throughout the brain. However, our analyses revealed nonuniformity in the serotonin transporter intracorrelations and identified a highly connected local network (midbrain-striatum-thalamus-amygdala). Regionally specific intercorrelations between the opioid and serotonin tracers were found in anteromedial thalamus, amygdala, anterior cingulate cortex, dorsolateral prefrontal cortex, and left parietal cortex, i.e., in areas relevant for several neuropsychiatric disorders, especially affective disorders. This methodology enables in vivo mapping of connectivity patterns within and between neurotransmitter systems. Quantification of functional neurotransmitter balances may be a useful approach in etiological studies of neuropsychiatric disorders and also in drug development as a biomarker-based rationale for targeted modulation of neurotransmitter networks. Copyright © 2013 Wiley Periodicals, Inc.

Pairwise Classifier Ensemble with Adaptive Sub-Classifiers for fMRI Pattern Analysis.

PubMed

Kim, Eunwoo; Park, HyunWook

2017-02-01

The multi-voxel pattern analysis technique is applied to fMRI data for classification of high-level brain functions using pattern information distributed over multiple voxels. In this paper, we propose a classifier ensemble for multiclass classification in fMRI analysis, exploiting the fact that specific neighboring voxels can contain spatial pattern information. The proposed method converts the multiclass classification to a pairwise classifier ensemble, and each pairwise classifier consists of multiple sub-classifiers using an adaptive feature set for each class-pair. Simulated and real fMRI data were used to verify the proposed method. Intra- and inter-subject analyses were performed to compare the proposed method with several well-known classifiers, including single and ensemble classifiers. The comparison results showed that the proposed method can be generally applied to multiclass classification in both simulations and real fMRI analyses.

Time-Resolved and Spatio-Temporal Analysis of Complex Cognitive Processes and their Role in Disorders like Developmental Dyscalculia

PubMed Central

Mórocz, István Akos; Janoos, Firdaus; van Gelderen, Peter; Manor, David; Karni, Avi; Breznitz, Zvia; von Aster, Michael; Kushnir, Tammar; Shalev, Ruth

2012-01-01

The aim of this article is to report on the importance and challenges of a time-resolved and spatio-temporal analysis of fMRI data from complex cognitive processes and associated disorders using a study on developmental dyscalculia (DD). Participants underwent fMRI while judging the incorrectness of multiplication results, and the data were analyzed using a sequence of methods, each of which progressively provided more a detailed picture of the spatio-temporal aspect of this disease. Healthy subjects and subjects with DD performed alike behaviorally though they exhibited parietal disparities using traditional voxel-based group analyses. Further and more detailed differences, however, surfaced with a time-resolved examination of the neural responses during the experiment. While performing inter-group comparisons, a third group of subjects with dyslexia (DL) but with no arithmetic difficulties was included to test the specificity of the analysis and strengthen the statistical base with overall fifty-eight subjects. Surprisingly, the analysis showed a functional dissimilarity during an initial reading phase for the group of dyslexic but otherwise normal subjects, with respect to controls, even though only numerical digits and no alphabetic characters were presented. Thus our results suggest that time-resolved multi-variate analysis of complex experimental paradigms has the ability to yield powerful new clinical insights about abnormal brain function. Similarly, a detailed compilation of aberrations in the functional cascade may have much greater potential to delineate the core processing problems in mental disorders. PMID:22368322

Towards mapping the brain connectome in depression: functional connectivity by perfusion SPECT.

PubMed

Gardner, Ann; Åstrand, Disa; Öberg, Johanna; Jacobsson, Hans; Jonsson, Cathrine; Larsson, Stig; Pagani, Marco

2014-08-30

Several studies have demonstrated altered brain functional connectivity in the resting state in depression. However, no study has investigated interregional networking in patients with persistent depressive disorder (PDD). The aim of this study was to assess differences in brain perfusion distribution and connectivity between large groups of patients and healthy controls. Participants comprised 91 patients with PDD and 65 age- and sex-matched healthy controls. Resting state perfusion was investigated by single photon emission computed tomography, and group differences were assessed by Statistical Parametric Mapping. Brain connectivity was explored through a voxel-wise interregional correlation analysis using as covariate of interest the normalized values of clusters of voxels in which perfusion differences were found in group analysis. Significantly increased regional brain perfusion distribution covering a large part of the cerebellum was observed in patients as compared with controls. Patients showed a significant negative functional connectivity between the cerebellar cluster and caudate, bilaterally. This study demonstrated inverse relative perfusion between the cerebellum and the caudate in PDD. Functional uncoupling may be associated with a dysregulation between the role of the cerebellum in action control and of the caudate in action selection, initiation and decision making in the patients. The potential impact of the resting state condition and the possibility of mitochondrial impairment are discussed. Copyright © 2014 The Authors. Published by Elsevier Ireland Ltd.. All rights reserved.

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.

4D-CT Lung registration using anatomy-based multi-level multi-resolution optical flow analysis and thin-plate splines.

PubMed

Min, Yugang; Neylon, John; Shah, Amish; Meeks, Sanford; Lee, Percy; Kupelian, Patrick; Santhanam, Anand P

2014-09-01

The accuracy of 4D-CT registration is limited by inconsistent Hounsfield unit (HU) values in the 4D-CT data from one respiratory phase to another and lower image contrast for lung substructures. This paper presents an optical flow and thin-plate spline (TPS)-based 4D-CT registration method to account for these limitations. The use of unified HU values on multiple anatomy levels (e.g., the lung contour, blood vessels, and parenchyma) accounts for registration errors by inconsistent landmark HU value. While 3D multi-resolution optical flow analysis registers each anatomical level, TPS is employed for propagating the results from one anatomical level to another ultimately leading to the 4D-CT registration. 4D-CT registration was validated using target registration error (TRE), inverse consistency error (ICE) metrics, and a statistical image comparison using Gamma criteria of 1 % intensity difference in 2 mm(3) window range. Validation results showed that the proposed method was able to register CT lung datasets with TRE and ICE values <3 mm. In addition, the average number of voxel that failed the Gamma criteria was <3 %, which supports the clinical applicability of the propose registration mechanism. The proposed 4D-CT registration computes the volumetric lung deformations within clinically viable accuracy.

Development of the Complex General Linear Model in the Fourier Domain: Application to fMRI Multiple Input-Output Evoked Responses for Single Subjects

PubMed Central

Rio, Daniel E.; Rawlings, Robert R.; Woltz, Lawrence A.; Gilman, Jodi; Hommer, Daniel W.

2013-01-01

A linear time-invariant model based on statistical time series analysis in the Fourier domain for single subjects is further developed and applied to functional MRI (fMRI) blood-oxygen level-dependent (BOLD) multivariate data. This methodology was originally developed to analyze multiple stimulus input evoked response BOLD data. However, to analyze clinical data generated using a repeated measures experimental design, the model has been extended to handle multivariate time series data and demonstrated on control and alcoholic subjects taken from data previously analyzed in the temporal domain. Analysis of BOLD data is typically carried out in the time domain where the data has a high temporal correlation. These analyses generally employ parametric models of the hemodynamic response function (HRF) where prewhitening of the data is attempted using autoregressive (AR) models for the noise. However, this data can be analyzed in the Fourier domain. Here, assumptions made on the noise structure are less restrictive, and hypothesis tests can be constructed based on voxel-specific nonparametric estimates of the hemodynamic transfer function (HRF in the Fourier domain). This is especially important for experimental designs involving multiple states (either stimulus or drug induced) that may alter the form of the response function. PMID:23840281

Development of the complex general linear model in the Fourier domain: application to fMRI multiple input-output evoked responses for single subjects.

PubMed

Rio, Daniel E; Rawlings, Robert R; Woltz, Lawrence A; Gilman, Jodi; Hommer, Daniel W

2013-01-01

A linear time-invariant model based on statistical time series analysis in the Fourier domain for single subjects is further developed and applied to functional MRI (fMRI) blood-oxygen level-dependent (BOLD) multivariate data. This methodology was originally developed to analyze multiple stimulus input evoked response BOLD data. However, to analyze clinical data generated using a repeated measures experimental design, the model has been extended to handle multivariate time series data and demonstrated on control and alcoholic subjects taken from data previously analyzed in the temporal domain. Analysis of BOLD data is typically carried out in the time domain where the data has a high temporal correlation. These analyses generally employ parametric models of the hemodynamic response function (HRF) where prewhitening of the data is attempted using autoregressive (AR) models for the noise. However, this data can be analyzed in the Fourier domain. Here, assumptions made on the noise structure are less restrictive, and hypothesis tests can be constructed based on voxel-specific nonparametric estimates of the hemodynamic transfer function (HRF in the Fourier domain). This is especially important for experimental designs involving multiple states (either stimulus or drug induced) that may alter the form of the response function.

Effect of MRI Acoustic Noise on Cerebral FDG Uptake in Simultaneous MR-PET Imaging

PubMed Central

Abolmaali, Nasreddin; Arabasz, Grae; Guimaraes, Alexander R.; Catana, Ciprian

2013-01-01

Integrated scanners capable of simultaneous PET and MRI data acquisition are now available for human use. Although the scanners’ manufacturers have made substantial efforts to understand and minimize the mutual electromagnetic interference between the two modalities, the potential physiological inference has not been evaluated. In this work, we have studied the influence of the acoustic noise produced by the MR gradients on brain FDG uptake in the Siemens MR-BrainPET prototype. While particular attention was paid to the primary auditory cortex (PAC), a brain-wide analysis was also performed. Methods The effects of the MR on the PET count rate and image quantification were first investigated in phantoms. Next, ten healthy volunteers underwent two simultaneous FDG-PET/MR scans in the supine position with the FDG injection occurring inside the MR-BrainPET, alternating between a “quiet” (control) environment in which no MR sequences were run during the FDG uptake phase (the first 40 minutes after radiotracer administration) and a “noisy” (test) case in which MR sequences were run for the entire time. Cortical and subcortical regions of interest (ROIs) were derived from the high-resolution morphological MR data using FreeSurfer. The changes in FDG uptake in the FreeSurfer-derived ROIs between the two conditions were analyzed from parametric and static PET images, and on a voxel-by-voxel basis using SPM8 and FreeSurfer. Results Only minimal to no electromagnetic interference was observed for most of the MR sequences tested, with a maximum drop in count rate of 1.5% and a maximum change in the measured activity of 1.1% in the corresponding images. The ROI-based analysis showed statistically significant increases in the right PAC in both the parametric (9.13±4.73%) and static (4.18±2.87%) images. SPM8 analysis showed no statistically significant clusters in any images when a p<0.05 (corrected) was used; however, a p<0.001 (uncorrected) resolved bilateral statistically significant clusters of increased FDG uptake in the area of the PAC for the parametric image (left: 8.37±1.55%, right: 8.20±1.17%), but only unilateral increase in the static image (left: 8.68±3.89%). Conclusion Although the operation of the BrainPET prototype is virtually unaffected by the MR scanner, the acoustic noise produced by the MR gradients causes a focal increase in FDG uptake in the PAC, which could affect the interpretation of pathological (or brain-activation related) changes in FDG uptake in this region, if the expected effects are of comparable amplitude. PMID:23462677

Effect of MRI acoustic noise on cerebral fludeoxyglucose uptake in simultaneous MR-PET imaging.

PubMed

Chonde, Daniel B; Abolmaali, Nasreddin; Arabasz, Grae; Guimaraes, Alexander R; Catana, Ciprian

2013-05-01

Integrated scanners capable of simultaneous positron emission tomography (PET) and magnetic resonance imaging (MRI) data acquisition are now available for human use. Although the scanners' manufacturers have made substantial efforts to understand and minimize the mutual electromagnetic interference between the 2 modalities, the potential physiological inference has not been evaluated. In this study, we have studied the influence of the acoustic noise produced by the magnetic resonance (MR) gradients on brain fludeoxyglucose (FDG) uptake in the Siemens MR-BrainPET prototype. Although particular attention was paid to the primary auditory cortex (PAC), a brain-wide analysis was also performed. The effects of the MR on the PET count rate and image quantification were first investigated in phantoms. Next, 10 healthy volunteers underwent 2 simultaneous FDG-PET/MR scans in the supine position with the FDG injection occurring inside the MR-BrainPET, alternating between a "quiet" (control) environment in which no MR sequences were run during the FDG uptake phase (the first 40 minutes after radiotracer administration) and a "noisy" (test) environment in which MR sequences were run for the entire time. Cortical and subcortical regions of interest were derived from the high-resolution morphological MR data using FreeSurfer. The changes in the FDG uptake in the FreeSurfer-derived regions of interest between the 2 conditions were analyzed from parametric and static PET images, and on a voxel-by-voxel basis using SPM8 and FreeSurfer. Only minimal to no electromagnetic interference was observed for most of the MR sequences tested, with a maximum drop in count rate of 1.5% and a maximum change in the measured activity of 1.1% in the corresponding images. The region of interest-based analysis showed statistically significant increases in the right PAC in both the parametric (9.13% [4.73%]) and static (4.18% [2.87%]) images. The SPM8 analysis showed no statistically significant clusters in any images when a P < 0.05 (corrected) was used; however, a P < 0.001 (uncorrected) resolved bilateral statistically significant clusters of increased FDG uptake in the area of the PAC for the parametric image (left, 8.37% [1.55%]; right, 8.20% [1.17%]) but only unilateral increase in the static image (left, 8.68% [3.89%]). Although the operation of the BrainPET prototype is virtually unaffected by the MR scanner, the acoustic noise produced by the MR gradients causes a focal increase in the FDG uptake in the PAC, which could affect the interpretation of pathological (or brain-activation-related) changes in the FDG uptake in this region if the expected effects are of comparable amplitude.

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

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.

Correlation between Gray/White Matter Volume and Cognition in Healthy Elderly People

ERIC Educational Resources Information Center

Taki, Yasuyuki; Kinomura, Shigeo; Sato, Kazunori; Goto, Ryoi; Wu, Kai; Kawashima, Ryuta; Fukuda, Hiroshi

2011-01-01

This study applied volumetric analysis and voxel-based morphometry (VBM) of brain magnetic resonance (MR) images to assess whether correlations exist between global and regional gray/white matter volume and the cognitive functions of semantic memory and short-term memory, which are relatively well preserved with aging, using MR image data from 109…

Abnormal functional specialization within medial prefrontal cortex in high-functioning autism: a multi-voxel similarity analysis

PubMed Central

Meuwese, Julia D.I.; Towgood, Karren J.; Frith, Christopher D.; Burgess, Paul W.

2009-01-01

Multi-voxel pattern analyses have proved successful in ‘decoding’ mental states from fMRI data, but have not been used to examine brain differences associated with atypical populations. We investigated a group of 16 (14 males) high-functioning participants with autism spectrum disorder (ASD) and 16 non-autistic control participants (12 males) performing two tasks (spatial/verbal) previously shown to activate medial rostral prefrontal cortex (mrPFC). Each task manipulated: (i) attention towards perceptual versus self-generated information and (ii) reflection on another person's mental state (‘mentalizing'versus ‘non-mentalizing’) in a 2 × 2 design. Behavioral performance and group-level fMRI results were similar between groups. However, multi-voxel similarity analyses revealed strong differences. In control participants, the spatial distribution of activity generalized significantly between task contexts (spatial/verbal) when examining the same function (attention/mentalizing) but not when comparing different functions. This pattern was disrupted in the ASD group, indicating abnormal functional specialization within mrPFC, and demonstrating the applicability of multi-voxel pattern analysis to investigations of atypical populations. PMID:19174370

Deformation Invariant Attribute Vector for Deformable Registration of Longitudinal Brain MR Images

PubMed Central

Li, Gang; Guo, Lei; Liu, Tianming

2009-01-01

This paper presents a novel approach to define deformation invariant attribute vector (DIAV) for each voxel in 3D brain image for the purpose of anatomic correspondence detection. The DIAV method is validated by using synthesized deformation in 3D brain MRI images. Both theoretic analysis and experimental studies demonstrate that the proposed DIAV is invariant to general nonlinear deformation. Moreover, our experimental results show that the DIAV is able to capture rich anatomic information around the voxels and exhibit strong discriminative ability. The DIAV has been integrated into a deformable registration algorithm for longitudinal brain MR images, and the results on both simulated and real brain images are provided to demonstrate the good performance of the proposed registration algorithm based on matching of DIAVs. PMID:19369031

Accurate B-spline-based 3-D interpolation scheme for digital volume correlation

NASA Astrophysics Data System (ADS)

Ren, Maodong; Liang, Jin; Wei, Bin

2016-12-01

An accurate and efficient 3-D interpolation scheme, based on sampling theorem and Fourier transform technique, is proposed to reduce the sub-voxel matching error caused by intensity interpolation bias in digital volume correlation. First, the influence factors of the interpolation bias are investigated theoretically using the transfer function of an interpolation filter (henceforth filter) in the Fourier domain. A law that the positional error of a filter can be expressed as a function of fractional position and wave number is found. Then, considering the above factors, an optimized B-spline-based recursive filter, combining B-spline transforms and least squares optimization method, is designed to virtually eliminate the interpolation bias in the process of sub-voxel matching. Besides, given each volumetric image containing different wave number ranges, a Gaussian weighting function is constructed to emphasize or suppress certain of wave number ranges based on the Fourier spectrum analysis. Finally, a novel software is developed and series of validation experiments were carried out to verify the proposed scheme. Experimental results show that the proposed scheme can reduce the interpolation bias to an acceptable level.

Brain 18F-FDG PET Metabolic Abnormalities in Patients with Long-Lasting Macrophagic Myofascitis.

PubMed

Van Der Gucht, Axel; Aoun Sebaiti, Mehdi; Guedj, Eric; Aouizerate, Jessie; Yara, Sabrina; Gherardi, Romain K; Evangelista, Eva; Chalaye, Julia; Cottereau, Anne-Ségolène; Verger, Antoine; Bachoud-Levi, Anne-Catherine; Abulizi, Mukedaisi; Itti, Emmanuel; Authier, François-Jérôme

2017-03-01

The aim of this study was to characterize brain metabolic abnormalities in patients with macrophagic myofascitis (MMF) and the relationship with cognitive dysfunction through the use of PET with 18 F-FDG. Methods: 18 F-FDG PET brain imaging and a comprehensive battery of neuropsychological tests were performed in 100 consecutive MMF patients (age [mean ± SD], 45.9 ± 12 y; 74% women). Images were analyzed with statistical parametric mapping (SPM12). Through the use of analysis of covariance, all 18 F-FDG PET brain images of MMF patients were compared with those of a reference population of 44 healthy subjects similar in age (45.4 ± 16 y; P = 0.87) and sex (73% women; P = 0.88). The neuropsychological assessment identified 4 categories of patients: those with no significant cognitive impairment ( n = 42), those with frontal subcortical (FSC) dysfunction ( n = 29), those with Papez circuit dysfunction ( n = 22), and those with callosal disconnection ( n = 7). Results: In comparison with healthy subjects, the whole population of patients with MMF exhibited a spatial pattern of cerebral glucose hypometabolism ( P < 0.001) involving the occipital lobes, temporal lobes, limbic system, cerebellum, and frontoparietal cortices, as shown by analysis of covariance. The subgroup of patients with FSC dysfunction exhibited a larger extent of involved areas (35,223 voxels vs. 13,680 voxels in the subgroup with Papez circuit dysfunction and 5,453 voxels in patients without cognitive impairment). Nonsignificant results were obtained for the last subgroup because of its small population size. Conclusion: Our study identified a peculiar spatial pattern of cerebral glucose hypometabolism that was most marked in MMF patients with FSC dysfunction. Further studies are needed to determine whether this pattern could represent a diagnostic biomarker of MMF in patients with chronic fatigue syndrome and cognitive dysfunction. © 2017 by the Society of Nuclear Medicine and Molecular Imaging.

Radiotherapy volume delineation using dynamic [18F]-FDG PET/CT imaging in patients with oropharyngeal cancer: a pilot study.

PubMed

Silvoniemi, Antti; Din, Mueez U; Suilamo, Sami; Shepherd, Tony; Minn, Heikki

2016-11-01

Delineation of gross tumour volume in 3D is a critical step in the radiotherapy (RT) treatment planning for oropharyngeal cancer (OPC). Static [ 18 F]-FDG PET/CT imaging has been suggested as a method to improve the reproducibility of tumour delineation, but it suffers from low specificity. We undertook this pilot study in which dynamic features in time-activity curves (TACs) of [ 18 F]-FDG PET/CT images were applied to help the discrimination of tumour from inflammation and adjacent normal tissue. Five patients with OPC underwent dynamic [ 18 F]-FDG PET/CT imaging in treatment position. Voxel-by-voxel analysis was performed to evaluate seven dynamic features developed with the knowledge of differences in glucose metabolism in different tissue types and visual inspection of TACs. The Gaussian mixture model and K-means algorithms were used to evaluate the performance of the dynamic features in discriminating tumour voxels compared to the performance of standardized uptake values obtained from static imaging. Some dynamic features showed a trend towards discrimination of different metabolic areas but lack of consistency means that clinical application is not recommended based on these results alone. Impact of inflammatory tissue remains a problem for volume delineation in RT of OPC, but a simple dynamic imaging protocol proved practicable and enabled simple data analysis techniques that show promise for complementing the information in static uptake values.

Quantitative characterization of brain β-amyloid using a joint PiB/FDG PET image histogram

NASA Astrophysics Data System (ADS)

Camp, Jon J.; Hanson, Dennis P.; Holmes, David R.; Kemp, Bradley J.; Senjem, Matthew L.; Murray, Melissa E.; Dickson, Dennis W.; Parisi, Joseph; Petersen, Ronald C.; Lowe, Val J.; Robb, Richard A.

2014-03-01

A complex analysis performed by spatial registration of PiB and MRI patient images in order to localize the PiB signal to specific cortical brain regions has been proven effective in identifying imaging characteristics associated with underlying Alzheimer's Disease (AD) and Lewy Body Disease (LBD) pathology. This paper presents an original method of image analysis and stratification of amyloid-related brain disease based on the global spatial correlation of PiB PET images with 18F-FDG PET images (without MR images) to categorize the PiB signal arising from the cortex. Rigid registration of PiB and 18F-FDG images is relatively straightforward, and in registration the 18F-FDG signal serves to identify the cortical region in which the PiB signal is relevant. Cortical grey matter demonstrates the highest levels of amyloid accumulation and therefore the greatest PiB signal related to amyloid pathology. The highest intensity voxels in the 18F-FDG image are attributed to the cortical grey matter. The correlation of the highest intensity PiB voxels with the highest 18F-FDG values indicates the presence of β-amyloid protein in the cortex in disease states, while correlation of the highest intensity PiB voxels with mid-range 18F-FDG values indicates only nonspecific binding in the white matter.

Voxel-based population analysis for correlating local dose and rectal toxicity in prostate cancer radiotherapy

NASA Astrophysics Data System (ADS)

Acosta, Oscar; Drean, Gael; Ospina, Juan D.; Simon, Antoine; Haigron, Pascal; Lafond, Caroline; de Crevoisier, Renaud

2013-04-01

The majority of current models utilized for predicting toxicity in prostate cancer radiotherapy are based on dose-volume histograms. One of their main drawbacks is the lack of spatial accuracy, since they consider the organs as a whole volume and thus ignore the heterogeneous intra-organ radio-sensitivity. In this paper, we propose a dose-image-based framework to reveal the relationships between local dose and toxicity. In this approach, the three-dimensional (3D) planned dose distributions across a population are non-rigidly registered into a common coordinate system and compared at a voxel level, therefore enabling the identification of 3D anatomical patterns, which may be responsible for toxicity, at least to some extent. Additionally, different metrics were employed in order to assess the quality of the dose mapping. The value of this approach was demonstrated by prospectively analyzing rectal bleeding (⩾Grade 1 at 2 years) according to the CTCAE v3.0 classification in a series of 105 patients receiving 80 Gy to the prostate by intensity modulated radiation therapy (IMRT). Within the patients presenting bleeding, a significant dose excess (6 Gy on average, p < 0.01) was found in a region of the anterior rectal wall. This region, close to the prostate (1 cm), represented less than 10% of the rectum. This promising voxel-wise approach allowed subregions to be defined within the organ that may be involved in toxicity and, as such, must be considered during the inverse IMRT planning step.

Quantitative Rapid Assessment of Leukoaraiosis in CT : Comparison to Gold Standard MRI.

PubMed

Hanning, Uta; Sporns, Peter Bernhard; Schmidt, Rene; Niederstadt, Thomas; Minnerup, Jens; Bier, Georg; Knecht, Stefan; Kemmling, André

2017-10-20

The severity of white matter lesions (WML) is a risk factor of hemorrhage and predictor of clinical outcome after ischemic stroke; however, in contrast to magnetic resonance imaging (MRI) reliable quantification for this surrogate marker is limited for computed tomography (CT), the leading stroke imaging technique. We aimed to present and evaluate a CT-based automated rater-independent method for quantification of microangiopathic white matter changes. Patients with suspected minor stroke (National Institutes of Health Stroke scale, NIHSS < 4) were screened for the analysis of non-contrast computerized tomography (NCCT) at admission and compared to follow-up MRI. The MRI-based WML volume and visual Fazekas scores were assessed as the gold standard reference. We employed a recently published probabilistic brain segmentation algorithm for CT images to determine the tissue-specific density of WM space. All voxel-wise densities were quantified in WM space and weighted according to partial probabilistic WM content. The resulting mean weighted density of WM space in NCCT, the surrogate of WML, was correlated with reference to MRI-based WML parameters. The process of CT-based tissue-specific segmentation was reliable in 79 cases with varying severity of microangiopathy. Voxel-wise weighted density within WM spaces showed a noticeable correlation (r = -0.65) with MRI-based WML volume. Particularly in patients with moderate or severe lesion load according to the visual Fazekas score the algorithm provided reliable prediction of MRI-based WML volume. Automated observer-independent quantification of voxel-wise WM density in CT significantly correlates with microangiopathic WM disease in gold standard MRI. This rapid surrogate of white matter lesion load in CT may support objective WML assessment and therapeutic decision-making during acute stroke triage.

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

Automatic pole-like object modeling via 3D part-based analysis of point cloud

NASA Astrophysics Data System (ADS)

He, Liu; Yang, Haoxiang; Huang, Yuchun

2016-10-01

Pole-like objects, including trees, lampposts and traffic signs, are indispensable part of urban infrastructure. With the advance of vehicle-based laser scanning (VLS), massive point cloud of roadside urban areas becomes applied in 3D digital city modeling. Based on the property that different pole-like objects have various canopy parts and similar trunk parts, this paper proposed the 3D part-based shape analysis to robustly extract, identify and model the pole-like objects. The proposed method includes: 3D clustering and recognition of trunks, voxel growing and part-based 3D modeling. After preprocessing, the trunk center is identified as the point that has local density peak and the largest minimum inter-cluster distance. Starting from the trunk centers, the remaining points are iteratively clustered to the same centers of their nearest point with higher density. To eliminate the noisy points, cluster border is refined by trimming boundary outliers. Then, candidate trunks are extracted based on the clustering results in three orthogonal planes by shape analysis. Voxel growing obtains the completed pole-like objects regardless of overlaying. Finally, entire trunk, branch and crown part are analyzed to obtain seven feature parameters. These parameters are utilized to model three parts respectively and get signal part-assembled 3D model. The proposed method is tested using the VLS-based point cloud of Wuhan University, China. The point cloud includes many kinds of trees, lampposts and other pole-like posters under different occlusions and overlaying. Experimental results show that the proposed method can extract the exact attributes and model the roadside pole-like objects efficiently.

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.

WE-FG-202-12: Investigation of Longitudinal Salivary Gland DCE-MRI Changes

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

Ger, R; Howell, R; Li, H

Purpose: To determine the correlation between dose and changes through treatment in dynamic contrast enhanced (DCE) MRI voxel parameters (Ktrans, kep, Ve, and Vp) within salivary glands of head and neck oropharyngeal squamous cell carcinoma (HNSCC) patients. Methods: 17 HNSCC patients treated with definitive radiation therapy completed DCE-MRI scans on a 3T scanner at pre-treatment, mid-treatment, and post-treatment time points. Mid-treatment and post-treatment DCE images were deformably registered to pre-treatment DCE images (Velocity software package). Pharmacokinetic analysis of the DCE images used a modified Tofts model to produce parameter maps with an arterial input function selected from each patient’s perivertebralmore » space on the image (NordicICE software package). In-house software was developed for voxel-by-voxel longitudinal analysis of the salivary glands within the registered images. The planning CT was rigidly registered to the pre-treatment DCE image to obtain dose values in each voxel. Voxels within the lower and upper dose quartiles for each gland were averaged for each patient, then an average of the patients’ means for the two quartiles were compared. Dose-relationships were also assessed by Spearman correlations between dose and voxel parameter changes for each patient’s gland. Results: Changes in parameters’ means between time points were observed, but inter-patient variability was high. Ve of the parotid was the only parameter that had a consistently significant longitudinal difference between dose quartiles. The highest Spearman correlation was Vp of the sublingual gland for the change in the pre-treatment to mid-treatment values with only a ρ=0.29. Conclusion: In this preliminary study, there was large inter-patient variability in the changes of DCE voxel parameters with no clear relationship with dose. Additional patients may reduce the uncertainties and allow for the determination of the existence of parameter and dose relationships.« less

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.

An exploratory study of volumetric analysis for assessing tumor response with (18)F-FAZA PET/CT in patients with advanced non-small-cell lung cancer (NSCLC).

PubMed

Kerner, Gerald S M A; Bollineni, Vikram R; Hiltermann, Thijo J N; Sijtsema, Nanna M; Fischer, Alexander; Bongaerts, Alphons H H; Pruim, Jan; Groen, Harry J M

2016-12-01

Hypoxia is associated with resistance to chemotherapy and radiotherapy and is randomly distributed within malignancies. Characterization of changes in intratumoral hypoxic regions is possible with specially developed PET tracers such as (18)F-fluoroazomycin arabinoside ((18)F-FAZA) while tumor metabolism can be measured with 2-deoxy-2-[(18)F]fluoro-D-glucose ((18)F-FDG). The purpose of this study was to study the effects of chemotherapy on (18)F-FAZA and (18)F-FDG uptake simultaneously in non-small-cell lung cancer (NSCLC) patients At baseline and after the second chemotherapy cycle, both PET/CT with (18)F-FDG and (18)F-FAZA was performed in seven patients with metastasized NSCLC. (18)F-FAZA and (18)F-FDG scans were aligned with deformable image registration using Mirada DBx. The primary tumors were contoured, and on the (18)F-FDG scan, volumes of interest (VOI) were drawn using a 41 % adaptive threshold technique. Subsequently, the resulting VOI was transferred to the (18)F-FAZA scan. (18)F-FAZA maximum tumor-to-background (T/Bgmax) ratio and the fractional hypoxic volume (FHV) were assessed. Measurements were corrected for partial volume effects. Finally, a voxel-by-voxel analysis of the primary tumor was performed to assess regional uptake differences. In the primary tumor of all seven patients, median (18)F-FDG standard uptake value (SUVmax) decreased significantly (p = 0.03). There was no significant decrease in (18)F-FAZA uptake as measured with T/Bgmax (p = 0.24) or the FHV (p = 0.35). Additionally, volumetric voxel-by-voxel analysis showed that low hypoxic tumors did not significantly change in hypoxic status between baseline and two cycles of chemotherapy, whereas highly hypoxic tumors did. Individualized volumetric voxel-by-voxel analysis revealed that hypoxia and metabolism were not associated before and after 2 cycles of chemotherapy. Tumor hypoxia and metabolism are independent dynamic events as measured by (18)F-FAZA PET and (18)F-FDG PET, both prior to and after treatment with chemotherapy in NSCLC patients.

[Diffusion tensor imaging findings in first-episode and chronic schizophrenics].

PubMed

Wei, Qin-Ling; Kang, Zhuang; Wu, Xiao-Li; Zhang, Jin-Bei; Li, Lei-Jun; Zheng, Liang-Rong; Guo, Xiao-Feng; Zhao, Jing-Ping

2011-08-23

To investigate the integrity of white matters in first-episode and chronic schizophrenics. For this study, 39 first-episode and 38 chronic schizophrenics, 69 healthy controls (age, gender and years of received education no significantly different from those of the patients) underwent diffusion weighted images with a single-shot echo planar imaging (EPI) sequence aligned to the straight axial plane. The fractional anisotropy (FA) images of three groups underwent one-way ANOVA with the methods of voxel-based morphometric (VBM) analysis. (1) There were three brain regions where the FA values of white matter were different among three groups: right caudate nucleus (MNI: 20, 12, 14; cluster = 432 voxels; FA value: 0.36 ± 0.18 vs 0.35 ± 0.24 vs 0.38 ± 0.17), left insula (MNI: -32, 18, 2; cluster = 204 voxels; FA value: 0.35 ± 0.31 vs 0.33 ± 0.24 vs 0.36 ± 0.21) and right anterior cingulate (MNI: 16, 36, 12; cluster = 132 voxels; FA value: 0.35 ± 0.29 vs 0.34 ± 0.31 vs 0.37 ± 0.25). (2) The mean FA values of the three brain regions of two patients groups decreased versus those of healthy controls (P < 0.05). (3) The mean FA values of left insular region in chronic patients decreased versus those of the first-episode patients (P < 0.05). The reduced integrity of white matter may play an etiological role in schizophrenia and the changes are probably progressive.

Altered voxel-wise gray matter structural brain networks in schizophrenia: Association with brain genetic expression pattern.

PubMed

Liu, Feng; Tian, Hongjun; Li, Jie; Li, Shen; Zhuo, Chuanjun

2018-05-04

Previous seed- and atlas-based structural covariance/connectivity analyses have demonstrated that patients with schizophrenia is accompanied by aberrant structural connection and abnormal topological organization. However, it remains unclear whether this disruption is present in unbiased whole-brain voxel-wise structural covariance networks (SCNs) and whether brain genetic expression variations are linked with network alterations. In this study, ninety-five patients with schizophrenia and 95 matched healthy controls were recruited and gray matter volumes were extracted from high-resolution structural magnetic resonance imaging scans. Whole-brain voxel-wise gray matter SCNs were constructed at the group level and were further analyzed by using graph theory method. Nonparametric permutation tests were employed for group comparisons. In addition, regression modes along with random effect analysis were utilized to explore the associations between structural network changes and gene expression from the Allen Human Brain Atlas. Compared with healthy controls, the patients with schizophrenia showed significantly increased structural covariance strength (SCS) in the right orbital part of superior frontal gyrus and bilateral middle frontal gyrus, while decreased SCS in the bilateral superior temporal gyrus and precuneus. The altered SCS showed reproducible correlations with the expression profiles of the gene classes involved in therapeutic targets and neurodevelopment. Overall, our findings not only demonstrate that the topological architecture of whole-brain voxel-wise SCNs is impaired in schizophrenia, but also provide evidence for the possible role of therapeutic targets and neurodevelopment-related genes in gray matter structural brain networks in schizophrenia.

Fractal Analysis of Brain Blood Oxygenation Level Dependent (BOLD) Signals from Children with Mild Traumatic Brain Injury (mTBI).

PubMed

Dona, Olga; Noseworthy, Michael D; DeMatteo, Carol; Connolly, John F

2017-01-01

Conventional imaging techniques are unable to detect abnormalities in the brain following mild traumatic brain injury (mTBI). Yet patients with mTBI typically show delayed response on neuropsychological evaluation. Because fractal geometry represents complexity, we explored its utility in measuring temporal fluctuations of brain resting state blood oxygen level dependent (rs-BOLD) signal. We hypothesized that there could be a detectable difference in rs-BOLD signal complexity between healthy subjects and mTBI patients based on previous studies that associated reduction in signal complexity with disease. Fifteen subjects (13.4 ± 2.3 y/o) and 56 age-matched (13.5 ± 2.34 y/o) healthy controls were scanned using a GE Discovery MR750 3T MRI and 32-channel RF-coil. Axial FSPGR-3D images were used to prescribe rs-BOLD (TE/TR = 35/2000ms), acquired over 6 minutes. Motion correction was performed and anatomical and functional images were aligned and spatially warped to the N27 standard atlas. Fractal analysis, performed on grey matter, was done by estimating the Hurst exponent using de-trended fluctuation analysis and signal summation conversion methods. Voxel-wise fractal dimension (FD) was calculated for every subject in the control group to generate mean and standard deviation maps for regional Z-score analysis. Voxel-wise validation of FD normality across controls was confirmed, and non-Gaussian voxels (3.05% over the brain) were eliminated from subsequent analysis. For each mTBI patient, regions where Z-score values were at least 2 standard deviations away from the mean (i.e. where |Z| > 2.0) were identified. In individual patients the frequently affected regions were amygdala (p = 0.02), vermis(p = 0.03), caudate head (p = 0.04), hippocampus(p = 0.03), and hypothalamus(p = 0.04), all previously reported as dysfunctional after mTBI, but based on group analysis. It is well known that the brain is best modeled as a complex system. Therefore a measure of complexity using rs-BOLD signal FD could provide an additional method to grade and monitor mTBI. Furthermore, this approach can be personalized thus providing unique patient specific assessment.

Improved volumetric measurement of brain structure with a distortion correction procedure using an ADNI phantom.

PubMed

Maikusa, Norihide; Yamashita, Fumio; Tanaka, Kenichiro; Abe, Osamu; Kawaguchi, Atsushi; Kabasawa, Hiroyuki; Chiba, Shoma; Kasahara, Akihiro; Kobayashi, Nobuhisa; Yuasa, Tetsuya; Sato, Noriko; Matsuda, Hiroshi; Iwatsubo, Takeshi

2013-06-01

Serial magnetic resonance imaging (MRI) images acquired from multisite and multivendor MRI scanners are widely used in measuring longitudinal structural changes in the brain. Precise and accurate measurements are important in understanding the natural progression of neurodegenerative disorders such as Alzheimer's disease. However, geometric distortions in MRI images decrease the accuracy and precision of volumetric or morphometric measurements. To solve this problem, the authors suggest a commercially available phantom-based distortion correction method that accommodates the variation in geometric distortion within MRI images obtained with multivendor MRI scanners. The authors' method is based on image warping using a polynomial function. The method detects fiducial points within a phantom image using phantom analysis software developed by the Mayo Clinic and calculates warping functions for distortion correction. To quantify the effectiveness of the authors' method, the authors corrected phantom images obtained from multivendor MRI scanners and calculated the root-mean-square (RMS) of fiducial errors and the circularity ratio as evaluation values. The authors also compared the performance of the authors' method with that of a distortion correction method based on a spherical harmonics description of the generic gradient design parameters. Moreover, the authors evaluated whether this correction improves the test-retest reproducibility of voxel-based morphometry in human studies. A Wilcoxon signed-rank test with uncorrected and corrected images was performed. The root-mean-square errors and circularity ratios for all slices significantly improved (p < 0.0001) after the authors' distortion correction. Additionally, the authors' method was significantly better than a distortion correction method based on a description of spherical harmonics in improving the distortion of root-mean-square errors (p < 0.001 and 0.0337, respectively). Moreover, the authors' method reduced the RMS error arising from gradient nonlinearity more than gradwarp methods. In human studies, the coefficient of variation of voxel-based morphometry analysis of the whole brain improved significantly from 3.46% to 2.70% after distortion correction of the whole gray matter using the authors' method (Wilcoxon signed-rank test, p < 0.05). The authors proposed a phantom-based distortion correction method to improve reproducibility in longitudinal structural brain analysis using multivendor MRI. The authors evaluated the authors' method for phantom images in terms of two geometrical values and for human images in terms of test-retest reproducibility. The results showed that distortion was corrected significantly using the authors' method. In human studies, the reproducibility of voxel-based morphometry analysis for the whole gray matter significantly improved after distortion correction using the authors' method.

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.

White matter and schizophrenia: A meta-analysis of voxel-based morphometry and diffusion tensor imaging studies.

PubMed

Vitolo, Enrico; Tatu, Mona Karina; Pignolo, Claudia; Cauda, Franco; Costa, Tommaso; Ando', Agata; Zennaro, Alessandro

2017-12-30

Voxel-based morphometry (VBM) and diffusion tensor imaging (DTI) are the most implemented methodologies to detect alterations of both gray and white matter (WM). However, the role of WM in mental disorders is still not well defined. We aimed at clarifying the role of WM disruption in schizophrenia and at identifying the most frequently involved brain networks. A systematic literature search was conducted to identify VBM and DTI studies focusing on WM alterations in patients with schizophrenia compared to control subjects. We selected studies reporting the coordinates of WM reductions and we performed the anatomical likelihood estimation (ALE). Moreover, we labeled the WM bundles with an anatomical atlas and compared VBM and DTI ALE-scores of each significant WM tract. A total of 59 studies were eligible for the meta-analysis. WM alterations were reported in 31 and 34 foci with VBM and DTI methods, respectively. The most occurred WM bundles in both VBM and DTI studies and largely involved in schizophrenia were long projection fibers, callosal and commissural fibers, part of motor descending fibers, and fronto-temporal-limbic pathways. The meta-analysis showed a widespread WM disruption in schizophrenia involving specific cerebral circuits instead of well-defined regions. Copyright © 2017 Elsevier B.V. All rights reserved.

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

Decoding Information in the Human Hippocampus: A User's Guide

ERIC Educational Resources Information Center

Chadwick, Martin J.; Bonnici, Heidi M.; Maguire, Eleanor A.

2012-01-01

Multi-voxel pattern analysis (MVPA), or "decoding", of fMRI activity has gained popularity in the neuroimaging community in recent years. MVPA differs from standard fMRI analyses by focusing on whether information relating to specific stimuli is encoded in patterns of activity across multiple voxels. If a stimulus can be predicted, or decoded,…

Accuracy and the Effect of Possible Subject-Based Confounders of Magnitude-Based MRI for Estimating Hepatic Proton Density Fat Fraction in Adults, Using MR Spectroscopy as Reference

PubMed Central

Heba, Elhamy R.; Desai, Ajinkya; Zand, Kevin A.; Hamilton, Gavin; Wolfson, Tanya; Schlein, Alexandra N.; Gamst, Anthony; Loomba, Rohit; Sirlin, Claude B.; Middleton, Michael S.

2016-01-01

Purpose To determine the accuracy and the effect of possible subject-based confounders of magnitude-based magnetic resonance imaging (MRI) for estimating hepatic proton density fat fraction (PDFF) for different numbers of echoes in adults with known or suspected nonalcoholic fatty liver disease, using MR spectroscopy (MRS) as a reference. Materials and Methods In this retrospective analysis of 506 adults, hepatic PDFF was estimated by unenhanced 3.0T MRI, using right-lobe MRS as reference. Regions of interest placed on source images and on six-echo parametric PDFF maps were colocalized to MRS voxel location. Accuracy using different numbers of echoes was assessed by regression and Bland–Altman analysis; slope, intercept, average bias, and R2 were calculated. The effect of age, sex, and body mass index (BMI) on hepatic PDFF accuracy was investigated using multivariate linear regression analyses. Results MRI closely agreed with MRS for all tested methods. For three- to six-echo methods, slope, regression intercept, average bias, and R2 were 1.01–0.99, 0.11–0.62%, 0.24–0.56%, and 0.981–0.982, respectively. Slope was closest to unity for the five-echo method. The two-echo method was least accurate, underestimating PDFF by an average of 2.93%, compared to an average of 0.23–0.69% for the other methods. Statistically significant but clinically nonmeaningful effects on PDFF error were found for subject BMI (P range: 0.0016 to 0.0783), male sex (P range: 0.015 to 0.037), and no statistically significant effect was found for subject age (P range: 0.18–0.24). Conclusion Hepatic magnitude-based MRI PDFF estimates using three, four, five, and six echoes, and six-echo parametric maps are accurate compared to reference MRS values, and that accuracy is not meaningfully confounded by age, sex, or BMI. PMID:26201284

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

Accurate discrimination of Alzheimer's disease from other dementia and/or normal subjects using SPECT specific volume analysis

NASA Astrophysics Data System (ADS)

Iyatomi, Hitoshi; Hashimoto, Jun; Yoshii, Fumuhito; Kazama, Toshiki; Kawada, Shuichi; Imai, Yutaka

2014-03-01

Discrimination between Alzheimer's disease and other dementia is clinically significant, however it is often difficult. In this study, we developed classification models among Alzheimer's disease (AD), other dementia (OD) and/or normal subjects (NC) using patient factors and indices obtained by brain perfusion SPECT. SPECT is commonly used to assess cerebral blood flow (CBF) and allows the evaluation of the severity of hypoperfusion by introducing statistical parametric mapping (SPM). We investigated a total of 150 cases (50 cases each for AD, OD, and NC) from Tokai University Hospital, Japan. In each case, we obtained a total of 127 candidate parameters from: (A) 2 patient factors (age and sex), (B) 12 CBF parameters and 113 SPM parameters including (C) 3 from specific volume analysis (SVA), and (D) 110 from voxel-based analysis stereotactic extraction estimation (vbSEE). We built linear classifiers with a statistical stepwise feature selection and evaluated the performance with the leave-one-out cross validation strategy. Our classifiers achieved very high classification performances with reasonable number of selected parameters. In the most significant discrimination in clinical, namely those of AD from OD, our classifier achieved both sensitivity (SE) and specificity (SP) of 96%. In a similar way, our classifiers achieved a SE of 90% and a SP of 98% in AD from NC, as well as a SE of 88% and a SP of 86% in AD from OD and NC cases. Introducing SPM indices such as SVA and vbSEE, classification performances improved around 7-15%. We confirmed that these SPM factors are quite important for diagnosing Alzheimer's disease.

Left dorsolateral prefrontal cortex atrophy is associated with frontal lobe function in Alzheimer's disease and contributes to caregiver burden.

PubMed

Matsuoka, Kiwamu; Yasuno, Fumihiko; Hashimoto, Akiko; Miyasaka, Toshiteru; Takahashi, Masato; Kiuchi, Kuniaki; Iida, Junzo; Kichikawa, Kimihiko; Kishimoto, Toshifumi

2018-05-01

Caregivers of patients with dementia experience physical and mental deterioration. We have previously reported a correlation between caregiver burden and the Frontal Assessment Battery (FAB) total scores of patients with Alzheimer's disease (AD), especially regarding the dependency factor from the Zarit Burden Interview. The present study aimed to identify an objective biomarker for predicting caregiver burden. The participants were 26 pairs of caregivers and patients with AD and mild-to-moderate dementia. Correlations between regional gray matter volumes in the patients with AD and the FAB total scores were explored by using whole-brain voxel-based morphometric analysis. Path analysis was used to estimate the relationships between regional gray matter volumes, FAB total scores, and caregiver burden based on the Zarit Burden Interview. The voxel-based morphometric revealed a significant positive correlation between the FAB total scores and the volume of the left dorsolateral prefrontal cortex. This positive correlation persisted after controlling for the effect of general cognitive dysfunction, which was assessed by using the Mini-Mental State Examination. Path analysis revealed that decreases in FAB scores, caused by reduced frontal lobe volumes, negatively affected caregiver burden. The present study revealed that frontal lobe function, based on FAB scores, was affected by the volume of the left dorsolateral prefrontal cortex. Decreased scores were associated with greater caregiver burden, especially for the dependency factor. These findings may facilitate the development of an objective biomarker for predicting caregiver burden. Copyright © 2017 John Wiley & Sons, Ltd.

Non-local means denoising of dynamic PET images.

PubMed

Dutta, Joyita; Leahy, Richard M; Li, Quanzheng

2013-01-01

Dynamic positron emission tomography (PET), which reveals information about both the spatial distribution and temporal kinetics of a radiotracer, enables quantitative interpretation of PET data. Model-based interpretation of dynamic PET images by means of parametric fitting, however, is often a challenging task due to high levels of noise, thus necessitating a denoising step. The objective of this paper is to develop and characterize a denoising framework for dynamic PET based on non-local means (NLM). NLM denoising computes weighted averages of voxel intensities assigning larger weights to voxels that are similar to a given voxel in terms of their local neighborhoods or patches. We introduce three key modifications to tailor the original NLM framework to dynamic PET. Firstly, we derive similarities from less noisy later time points in a typical PET acquisition to denoise the entire time series. Secondly, we use spatiotemporal patches for robust similarity computation. Finally, we use a spatially varying smoothing parameter based on a local variance approximation over each spatiotemporal patch. To assess the performance of our denoising technique, we performed a realistic simulation on a dynamic digital phantom based on the Digimouse atlas. For experimental validation, we denoised [Formula: see text] PET images from a mouse study and a hepatocellular carcinoma patient study. We compared the performance of NLM denoising with four other denoising approaches - Gaussian filtering, PCA, HYPR, and conventional NLM based on spatial patches. The simulation study revealed significant improvement in bias-variance performance achieved using our NLM technique relative to all the other methods. The experimental data analysis revealed that our technique leads to clear improvement in contrast-to-noise ratio in Patlak parametric images generated from denoised preclinical and clinical dynamic images, indicating its ability to preserve image contrast and high intensity details while lowering the background noise variance.

Partial volume segmentation in 3D of lesions and tissues in magnetic resonance images

NASA Astrophysics Data System (ADS)

Johnston, Brian; Atkins, M. Stella; Booth, Kellogg S.

1994-05-01

An important first step in diagnosis and treatment planning using tomographic imaging is differentiating and quantifying diseased as well as healthy tissue. One of the difficulties encountered in solving this problem to date has been distinguishing the partial volume constituents of each voxel in the image volume. Most proposed solutions to this problem involve analysis of planar images, in sequence, in two dimensions only. We have extended a model-based method of image segmentation which applies the technique of iterated conditional modes in three dimensions. A minimum of user intervention is required to train the algorithm. Partial volume estimates for each voxel in the image are obtained yielding fractional compositions of multiple tissue types for individual voxels. A multispectral approach is applied, where spatially registered data sets are available. The algorithm is simple and has been parallelized using a dataflow programming environment to reduce the computational burden. The algorithm has been used to segment dual echo MRI data sets of multiple sclerosis patients using lesions, gray matter, white matter, and cerebrospinal fluid as the partial volume constituents. The results of the application of the algorithm to these datasets is presented and compared to the manual lesion segmentation of the same data.

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.

TH-E-BRF-02: 4D-CT Ventilation Image-Based IMRT Plans Are Dosimetrically Comparable to SPECT Ventilation Image-Based Plans

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

Kida, S; University of Tokyo Hospital, Bunkyo, Tokyo; Bal, M

Purpose: An emerging lung ventilation imaging method based on 4D-CT can be used in radiotherapy to selectively avoid irradiating highly-functional lung regions, which may reduce pulmonary toxicity. Efforts to validate 4DCT ventilation imaging have been focused on comparison with other imaging modalities including SPECT and xenon CT. The purpose of this study was to compare 4D-CT ventilation image-based functional IMRT plans with SPECT ventilation image-based plans as reference. Methods: 4D-CT and SPECT ventilation scans were acquired for five thoracic cancer patients in an IRB-approved prospective clinical trial. The ventilation images were created by quantitative analysis of regional volume changes (amore » surrogate for ventilation) using deformable image registration of the 4D-CT images. A pair of 4D-CT ventilation and SPECT ventilation image-based IMRT plans was created for each patient. Regional ventilation information was incorporated into lung dose-volume objectives for IMRT optimization by assigning different weights on a voxel-by-voxel basis. The objectives and constraints of the other structures in the plan were kept identical. The differences in the dose-volume metrics have been evaluated and tested by a paired t-test. SPECT ventilation was used to calculate the lung functional dose-volume metrics (i.e., mean dose, V20 and effective dose) for both 4D-CT ventilation image-based and SPECT ventilation image-based plans. Results: Overall there were no statistically significant differences in any dose-volume metrics between the 4D-CT and SPECT ventilation imagebased plans. For example, the average functional mean lung dose of the 4D-CT plans was 26.1±9.15 (Gy), which was comparable to 25.2±8.60 (Gy) of the SPECT plans (p = 0.89). For other critical organs and PTV, nonsignificant differences were found as well. Conclusion: This study has demonstrated that 4D-CT ventilation image-based functional IMRT plans are dosimetrically comparable to SPECT ventilation image-based plans, providing evidence to use 4D-CT ventilation imaging for clinical applications. Supported in part by Free to Breathe Young Investigator Research Grant and NIH/NCI R01 CA 093626. The authors thank Philips Radiation Oncology Systems for the Pinnacle3 treatment planning systems.« less

Regional vulnerability of longitudinal cortical association connectivity: Associated with structural network topology alterations in preterm children with cerebral palsy.

PubMed

Ceschin, Rafael; Lee, Vince K; Schmithorst, Vince; Panigrahy, Ashok

2015-01-01

Preterm born children with spastic diplegia type of cerebral palsy and white matter injury or periventricular leukomalacia (PVL), are known to have motor, visual and cognitive impairments. Most diffusion tensor imaging (DTI) studies performed in this group have demonstrated widespread abnormalities using averaged deterministic tractography and voxel-based DTI measurements. Little is known about structural network correlates of white matter topography and reorganization in preterm cerebral palsy, despite the availability of new therapies and the need for brain imaging biomarkers. Here, we combined novel post-processing methodology of probabilistic tractography data in this preterm cohort to improve spatial and regional delineation of longitudinal cortical association tract abnormalities using an along-tract approach, and compared these data to structural DTI cortical network topology analysis. DTI images were acquired on 16 preterm children with cerebral palsy (mean age 5.6 ± 4) and 75 healthy controls (mean age 5.7 ± 3.4). Despite mean tract analysis, Tract-Based Spatial Statistics (TBSS) and voxel-based morphometry (VBM) demonstrating diffusely reduced fractional anisotropy (FA) reduction in all white matter tracts, the along-tract analysis improved the detection of regional tract vulnerability. The along-tract map-structural network topology correlates revealed two associations: (1) reduced regional posterior-anterior gradient in FA of the longitudinal visual cortical association tracts (inferior fronto-occipital fasciculus, inferior longitudinal fasciculus, optic radiation, posterior thalamic radiation) correlated with reduced posterior-anterior gradient of intra-regional (nodal efficiency) metrics with relative sparing of frontal and temporal regions; and (2) reduced regional FA within frontal-thalamic-striatal white matter pathways (anterior limb/anterior thalamic radiation, superior longitudinal fasciculus and cortical spinal tract) correlated with alteration in eigenvector centrality, clustering coefficient (inter-regional) and participation co-efficient (inter-modular) alterations of frontal-striatal and fronto-limbic nodes suggesting re-organization of these pathways. Both along tract and structural topology network measurements correlated strongly with motor and visual clinical outcome scores. This study shows the value of combining along-tract analysis and structural network topology in depicting not only selective parietal occipital regional vulnerability but also reorganization of frontal-striatal and frontal-limbic pathways in preterm children with cerebral palsy. These finding also support the concept that widespread, but selective posterior-anterior neural network connectivity alterations in preterm children with cerebral palsy likely contribute to the pathogenesis of neurosensory and cognitive impairment in this group.

Pain and temperature processing in dementia: a clinical and neuroanatomical analysis

PubMed Central

Fletcher, Phillip D.; Downey, Laura E.; Golden, Hannah L.; Clark, Camilla N.; Slattery, Catherine F.; Paterson, Ross W.; Rohrer, Jonathan D.; Schott, Jonathan M.; Rossor, Martin N.

2015-01-01

Symptoms suggesting altered processing of pain and temperature have been described in dementia diseases and may contribute importantly to clinical phenotypes, particularly in the frontotemporal lobar degeneration spectrum, but the basis for these symptoms has not been characterized in detail. Here we analysed pain and temperature symptoms using a semi-structured caregiver questionnaire recording altered behavioural responsiveness to pain or temperature for a cohort of patients with frontotemporal lobar degeneration (n = 58, 25 female, aged 52–84 years, representing the major clinical syndromes and representative pathogenic mutations in the C9orf72 and MAPT genes) and a comparison cohort of patients with amnestic Alzheimer’s disease (n = 20, eight female, aged 53–74 years). Neuroanatomical associations were assessed using blinded visual rating and voxel-based morphometry of patients’ brain magnetic resonance images. Certain syndromic signatures were identified: pain and temperature symptoms were particularly prevalent in behavioural variant frontotemporal dementia (71% of cases) and semantic dementia (65% of cases) and in association with C9orf72 mutations (6/6 cases), but also developed in Alzheimer’s disease (45% of cases) and progressive non-fluent aphasia (25% of cases). While altered temperature responsiveness was more common than altered pain responsiveness across syndromes, blunted responsiveness to pain and temperature was particularly associated with behavioural variant frontotemporal dementia (40% of symptomatic cases) and heightened responsiveness with semantic dementia (73% of symptomatic cases) and Alzheimer’s disease (78% of symptomatic cases). In the voxel-based morphometry analysis of the frontotemporal lobar degeneration cohort, pain and temperature symptoms were associated with grey matter loss in a right-lateralized network including insula (P < 0.05 corrected for multiple voxel-wise comparisons within the prespecified anatomical region of interest) and anterior temporal cortex (P < 0.001 uncorrected over whole brain) previously implicated in processing homeostatic signals. Pain and temperature symptoms accompanying C9orf72 mutations were specifically associated with posterior thalamic atrophy (P < 0.05 corrected for multiple voxel-wise comparisons within the prespecified anatomical region of interest). Together the findings suggest candidate cognitive and neuroanatomical bases for these salient but under-appreciated phenotypic features of the dementias, with wider implications for the homeostatic pathophysiology and clinical management of neurodegenerative diseases. PMID:26463677

Imaging the cortical effect of lamotrigine in patients with idiopathic generalized epilepsy: a low-resolution electromagnetic tomography (LORETA) study.

PubMed

Clemens, Béla; Piros, Pálma; Bessenyei, Mónika; Tóth, Márton; Hollódy, Katalin; Kondákor, István

2008-10-01

Anatomical localization of the cortical effect of lamotrigine (LTG) in patients with idiopathic generalized epilepsy (IGE). 19 patients with untreated IGE were investigated. EEG was recorded in the untreated condition and 3 months later when LTG treatment abolished the seizures. 19-channel EEG was recorded, and a total of 2min artifact-free, waking EEG was processed to low-resolution electromagnetic tomography (LORETA) analysis. Activity (that is, current source density, A/m(2)) was computed in four frequency bands (delta, theta, alpha, and beta), for 2394 voxels that represented the cortical gray matter and the hippocampi. Group differences between the untreated and treated conditions were computed for the four bands and all voxels by multiple t-tests for interdependent datasets. The results were presented in terms of anatomical distribution and statistical significance. p<0.01 (uncorrected) changes (decrease of activity) emerged in the theta and the alpha bands. Theta activity decreased in a large cluster of voxels including parts of the temporal, parietal, occipital cortex bilaterally, and in the transverse temporal gyri, insula, hippocampus, and uncus on the right side. Alpha activity decreased in a relatively smaller cortical area involving the right temporo-parietal junction and surrounding parts of the cortex, and part of the insula on the right side. LTG decreased theta activity in several cortical areas where abnormally increased theta activity had been found in a prior study in another cohort of untreated IGE patients [Clemens, B., Bessenyei, M., Piros, P., Tóth, M., Seress, L., Kondákor, I., 2007b. Characteristic distribution of interictal brain electrical activity in idiopathic generalized epilepsy. Epilepsia 48, 941-949]. These LTG-related changes might be related to the decrease of seizure propensity in IGE.

MRI-negative refractory partial epilepsy: role for diffusion tensor imaging in high field MRI.

PubMed

Chen, Qin; Lui, Su; Li, Chun-Xiao; Jiang, Li-Jun; Ou-Yang, Luo; Tang, He-Han; Shang, Hui-Fang; Huang, Xiao-Qi; Gong, Qi-Yong; Zhou, Dong

2008-07-01

Our aim is to use the high field MR scanner (3T) to verify whether diffusion tensor imaging (DTI) could help in locating the epileptogenic zone in patients with MRI-negative refractory partial epilepsy. Fifteen patients with refractory partial epilepsy who had normal conventional MRI, and 40 healthy volunteers were recruited for the study. DTI was performed on a 3T MR scanner, individual maps of mean diffusivity (MD) and fractional anisotropy (FA) were calculated, and Voxel-Based Analysis (VBA) was performed for individual comparison between patients and controls. Voxel-based analysis revealed significant MD increase in variant regions in 13 patients. The electroclinical seizure localization was concurred to seven patients. No patient exhibited regions of significant decreased MD. Regions of significant reduced FA were observed in five patients, with two of these concurring with electroclinical seizure localization. Two patients had regions of significant increase in FA, which were distinct from electroclinical seizure localization. Our study's results revealed that DTI is a responsive neuroradiologic technique that provides information about the epileptogenic areas in patients with MRI-negative refractory partial epilepsy. This technique may also helpful in pre-surgical evaluation.

Abnormal degree centrality in Alzheimer's disease patients with depression: A resting-state functional magnetic resonance imaging study.

PubMed

Guo, Zhongwei; Liu, Xiaozheng; Hou, Hongtao; Wei, Fuquan; Liu, Jian; Chen, Xingli

2016-06-15

Depression is common in Alzheimer's disease (AD) and occurs in AD patients with a prevalence of up to 40%. It reduces cognitive function and increases the burden on caregivers. Currently, there are very few medications that are useful for treating depression in AD patients. Therefore, understanding the brain abnormalities in AD patients with depression (D-AD) is crucial for developing effective interventions. The aim of this study was to investigate the intrinsic dysconnectivity pattern of whole-brain functional networks at the voxel level in D-AD patients based on degree centrality (DC) as measured by resting-state functional magnetic resonance imaging (R-fMRI). Our study included 32 AD patients. All patients were evaluated using the Neuropsychiatric Inventory and Hamilton Depression Rating Scale and further divided into two groups: 15 D-AD patients and 17 non-depressed AD (nD-AD) patients. R-fMRI datasets were acquired from these D-AD and nD-AD patients. First, we performed a DC analysis to identify voxels that showed altered whole brain functional connectivity (FC) with other voxels. We then further investigated FC using the abnormal DC regions to examine in more detail the connectivity patterns of the identified DC changes. D-AD patients had lower DC values in the right middle frontal, precentral, and postcentral gyrus than nD-AD patients. Seed-based analysis revealed decreased connectivity between the precentral and postcentral gyrus to the supplementary motor area and middle cingulum. FC also decreased in the right middle frontal, precentral, and postcentral gyrus. Thus, AD patients with depression fit a 'network dysfunction model' distinct from major depressive disorder and AD. Copyright © 2016. Published by Elsevier Inc.

Functional Neuroanatomical Correlates of The Frontal Assessment Battery Performance in Alzheimer Disease: A FDG-PET Study.

PubMed

Lee, Jun Ho; Byun, Min Soo; Sohn, Bo Kyung; Choe, Young Min; Yi, Dahyun; Han, Ji Young; Choi, Hyo Jung; Baek, Hyewon; Woo, Jong Inn; Lee, Dong Young

2015-09-01

We aimed to elucidate the functional neuroanatomical correlates of Frontal Assessment Battery (FAB) performances by applying [(18)F]fluorodeoxyglucose positron emission tomography (FDG-PET) to a large population of patients with Alzheimer disease (AD). The FAB was administered to 177 patients with AD, and regional cerebral glucose metabolism (rCMglc) was measured by FDG-PET scan. Correlations between FAB scores and rCMglc were explored using both region-of-interest-based (ROI-based) and voxel-based approaches. The ROI-based analysis showed that FAB scores correlated with the rCMglc of the dorsolateral prefrontal cortices. Voxel-based approach revealed significant positive correlations between FAB scores and rCMglc which were in various cortical regions including the temporal and parietal cortices as well as frontal regions, independent of age, gender, and education. After controlling the effect of global disease severity with Mini-Mental State Examination score, significant positive correlation was found only in the bilateral prefrontal regions. Although FAB scores are influenced by temporoparietal dysfunction due to the overall progression of AD, it likely reflects prefrontal dysfunction specifically regardless of global cognitive state or disease severity in patients with AD. © The Author(s) 2015.

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.

White-matter microstructure and hearing acuity in older adults: a population-based cross-sectional DTI study.

PubMed

Rigters, Stephanie C; Cremers, Lotte G M; Ikram, M Arfan; van der Schroeff, Marc P; de Groot, Marius; Roshchupkin, Gennady V; Niessen, Wiro J N; Baatenburg de Jong, Robert J; Goedegebure, André; Vernooij, Meike W

2018-01-01

To study the relation between the microstructure of white matter in the brain and hearing function in older adults we carried out a population-based, cross-sectional study. In 2562 participants of the Rotterdam Study, we conducted diffusion tensor imaging to determine the microstructure of the white-matter tracts. We performed pure-tone audiogram and digit-in-noise tests to quantify hearing acuity. Poorer white-matter microstructure, especially in the association tracts, was related to poorer hearing acuity. After differentiating the separate white-matter tracts in the left and right hemisphere, poorer white-matter microstructure in the right superior longitudinal fasciculus and the right uncinate fasciculus remained significantly associated with worse hearing. These associations did not significantly differ between middle-aged (51-69 years old) and older (70-100 years old) participants. Progressing age was thus not found to be an effect modifier. In a voxel-based analysis no voxels in the white matter were significantly associated with hearing impairment. Copyright © 2017 Elsevier Inc. All rights reserved.

Histogram analysis of apparent diffusion coefficient maps for differentiating primary CNS lymphomas from tumefactive demyelinating lesions.

PubMed

Lu, Shan Shan; Kim, Sang Joon; Kim, Namkug; Kim, Ho Sung; Choi, Choong Gon; Lim, Young Min

2015-04-01

This study intended to investigate the usefulness of histogram analysis of apparent diffusion coefficient (ADC) maps for discriminating primary CNS lymphomas (PCNSLs), especially atypical PCNSLs, from tumefactive demyelinating lesions (TDLs). Forty-seven patients with PCNSLs and 18 with TDLs were enrolled in our study. Hyperintense lesions seen on T2-weighted images were defined as ROIs after ADC maps were registered to the corresponding T2-weighted image. ADC histograms were calculated from the ROIs containing the entire lesion on every section and on a voxel-by-voxel basis. The ADC histogram parameters were compared among all PCNSLs and TDLs as well as between the subgroup of atypical PCNSLs and TDLs. ROC curves were constructed to evaluate the diagnostic performance of the histogram parameters and to determine the optimum thresholds. The differences between the PCNSLs and TDLs were found in the minimum ADC values (ADCmin) and in the 5th and 10th percentiles (ADC5% and ADC10%) of the cumulative ADC histograms. However, no statistical significance was found in the mean ADC value or in the ADC value concerning the mode, kurtosis, and skewness. The ADCmin, ADC5%, and ADC10% were also lower in atypical PCNSLs than in TDLs. ADCmin was the best indicator for discriminating atypical PCNSLs from TDLs, with a threshold of 556×10(-6) mm2/s (sensitivity, 81.3 %; specificity, 88.9%). Histogram analysis of ADC maps may help to discriminate PCNSLs from TDLs and may be particularly useful in differentiating atypical PCNSLs from TDLs.

Cortical Thinning in Patients with Recent Onset Post-Traumatic Stress Disorder after a Single Prolonged Trauma Exposure

PubMed Central

Liu, Yang; Li, Yi-Jun; Luo, Er-Ping; Lu, Hong-Bing; Yin, Hong

2012-01-01

Most of magnetic resonance imaging (MRI) studies about post-traumatic stress disorder (PTSD) focused primarily on measuring of small brain structure volume or regional brain volume changes. There were rare reports investigating cortical thickness alterations in recent onset PTSD. Recent advances in computational analysis made it possible to measure cortical thickness in a fully automatic way, along with voxel-based morphometry (VBM) that enables an exploration of global structural changes throughout the brain by applying statistical parametric mapping (SPM) to high-resolution MRI. In this paper, Laplacian method was utilized to estimate cortical thickness after automatic segmentation of gray matter from MR images under SPM. Then thickness maps were analyzed by SPM8. Comparison between 10 survivors from a mining disaster with recent onset PTSD and 10 survivors without PTSD from the same trauma indicates cortical thinning in the left parietal lobe, right inferior frontal gyrus, and right parahippocampal gyrus. The regional cortical thickness of the right inferior frontal gyrus showed a significant negative correlation with the CAPS score in the patients with PTSD. Our study suggests that shape-related cortical thickness analysis may be more sensitive than volumetric analysis to subtle alteration at early stage of PTSD. PMID:22720021

Vitality Forms Processing in the Insula during Action Observation: A Multivoxel Pattern Analysis

PubMed Central

Di Cesare, Giuseppe; Valente, Giancarlo; Di Dio, Cinzia; Ruffaldi, Emanuele; Bergamasco, Massimo; Goebel, Rainer; Rizzolatti, Giacomo

2016-01-01

Observing the style of an action done by others allows the observer to understand the cognitive state of the agent. This information has been defined by Stern “vitality forms”. Previous experiments showed that the dorso-central insula is selectively active both during vitality form observation and execution. In the present study, we presented participants with videos showing hand actions performed with different velocities and asked them to judge either their vitality form (gentle, neutral, rude) or their velocity (slow, medium, fast). The aim of the present study was to assess, using multi-voxel pattern analysis, whether vitality forms and velocities of observed goal-directed actions are differentially processed in the insula, and more specifically whether action velocity is encoded per se or it is an element that triggers neural populations of the insula encoding the vitality form. The results showed that, consistently across subjects, in the dorso-central sector of the insula there were voxels selectively tuned to vitality forms, while voxel tuned to velocity were rare. These results indicate that the dorso-central insula, which previous data showed to be involved in the vitality form processing, contains voxels specific for the action style processing. PMID:27375461

Vitality Forms Processing in the Insula during Action Observation: A Multivoxel Pattern Analysis.

PubMed

Di Cesare, Giuseppe; Valente, Giancarlo; Di Dio, Cinzia; Ruffaldi, Emanuele; Bergamasco, Massimo; Goebel, Rainer; Rizzolatti, Giacomo

2016-01-01

Observing the style of an action done by others allows the observer to understand the cognitive state of the agent. This information has been defined by Stern "vitality forms". Previous experiments showed that the dorso-central insula is selectively active both during vitality form observation and execution. In the present study, we presented participants with videos showing hand actions performed with different velocities and asked them to judge either their vitality form (gentle, neutral, rude) or their velocity (slow, medium, fast). The aim of the present study was to assess, using multi-voxel pattern analysis, whether vitality forms and velocities of observed goal-directed actions are differentially processed in the insula, and more specifically whether action velocity is encoded per se or it is an element that triggers neural populations of the insula encoding the vitality form. The results showed that, consistently across subjects, in the dorso-central sector of the insula there were voxels selectively tuned to vitality forms, while voxel tuned to velocity were rare. These results indicate that the dorso-central insula, which previous data showed to be involved in the vitality form processing, contains voxels specific for the action style processing.

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.

Diffusion Entropy: A Potential Neuroimaging Biomarker of Bipolar Disorder in the Temporal Pole.

PubMed

Spuhler, Karl; Bartlett, Elizabeth; Ding, Jie; DeLorenzo, Christine; Parsey, Ramin; Huang, Chuan

2018-02-01

Despite much research, bipolar depression remains poorly understood, with no clinically useful biomarkers for its diagnosis. The paralimbic system has become a target for biomarker research, with paralimbic structural connectivity commonly reported to distinguish bipolar patients from controls in tractography-based diffusion MRI studies, despite inconsistent findings in voxel-based studies. The purpose of this analysis was to validate existing findings with traditional diffusion MRI metrics and investigate the utility of a novel diffusion MRI metric, entropy of diffusion, in the search for bipolar depression biomarkers. We performed group-level analysis on 9 un-medicated (6 medication-naïve; 3 medication-free for at least 33 days) bipolar patients in a major depressive episode and 9 matched healthy controls to compare: (1) average mean diffusivity (MD) and fractional anisotropy (FA) and; (2) MD and FA histogram entropy-a statistical measure of distribution homogeneity-in the amygdala, hippocampus, orbitofrontal cortex and temporal pole. We also conducted classification analyses with leave-one-out and separate testing dataset (N = 11) approaches. We did not observe statistically significant differences in average MD or FA between the groups in any region. However, in the temporal pole, we observed significantly lower MD entropy in bipolar patients; this finding suggests a regional difference in MD distributions in the absence of an average difference. This metric allowed us to accurately characterize bipolar patients from controls in leave-one-out (accuracy = 83%) and prediction (accuracy = 73%) analyses. This novel application of diffusion MRI yielded not only an interesting separation between bipolar patients and healthy controls, but also accurately classified bipolar patients from controls. © 2017 Wiley Periodicals, Inc.

Resting-state brain networks in patients with Parkinson's disease and impulse control disorders.

PubMed

Tessitore, Alessandro; Santangelo, Gabriella; De Micco, Rosa; Giordano, Alfonso; Raimo, Simona; Amboni, Marianna; Esposito, Fabrizio; Barone, Paolo; Tedeschi, Gioacchino; Vitale, Carmine

2017-09-01

To investigate intrinsic neural networks connectivity changes in Parkinson's disease (PD) patients with and without impulse control disorders (ICD). Fifteen patients with PD with ICD (ICD+), 15 patients with PD without ICD (ICD-) and 24 age and sex-matched healthy controls (HC) were enrolled in the study. To identify patients with and without ICD and/or punding, we used the Minnesota Impulsive Disorders Interview (MIDI) and a clinical interview based on diagnostic criteria for each symptom. All patients underwent a detailed neuropsychological evaluation. Whole brain structural and functional imaging was performed on a 3T GE MR scanner. Statistical analysis of functional data was completed using BrainVoyager QX software. Voxel-based morphometry (VBM) was used to test whether between-group differences in resting-state connectivity were related to structural abnormalities. The presence of ICD symptoms was associated with an increased connectivity within the salience and default-mode networks, as well as with a decreased connectivity within the central executive network (p < .05 corrected). ICD severity was correlated with both salience and default mode networks connectivity changes only in the ICD+ group. VBM analysis did not reveal any statistically significant differences in local grey matter volume between ICD+ and ICD- patients and between all patients and HC (p < .05. FWE). The presence of a disrupted connectivity within the three core neurocognitive networks may be considered as a potential neural correlate of ICD presence in patients with PD. Our findings provide additional insights into the mechanisms underlying ICD in PD, confirming the crucial role of an abnormal prefrontal-limbic-striatal homeostasis in their development. Copyright © 2017 Elsevier Ltd. All rights reserved.

Combining Segmented Grey and White Matter Images Improves Voxel-based Morphometry for the Case of Dilated Lateral Ventricles.

PubMed

Goto, Masami; Abe, Osamu; Aoki, Shigeki; Kamagata, Koji; Hori, Masaaki; Miyati, Tosiaki; Gomi, Tsutomu; Takeda, Tohoru

2018-01-18

To evaluate the error in segmented tissue images and to show the usefulness of the brain image in voxel-based morphometry (VBM) using Statistical Parametric Mapping (SPM) 12 software and 3D T 1 -weighted magnetic resonance images (3D-T 1 WIs) processed to simulate idiopathic normal pressure hydrocephalus (iNPH). VBM analysis was performed on sagittal 3D-T 1 WIs obtained in 22 healthy volunteers using a 1.5T MR scanner. Regions of interest for the lateral ventricles of all subjects were carefully outlined on the original 3D-T 1 WIs, and two types of simulated 3D-T 1 WI were also prepared (non-dilated 3D-T 1 WI as normal control and dilated 3D-T 1 WI to simulate iNPH). All simulated 3D-T 1 WIs were segmented into gray matter, white matter, and cerebrospinal fluid images, and normalized to standard space. A brain image was made by adding the gray and white matter images. After smoothing with a 6-mm isotropic Gaussian kernel, group comparisons (dilated vs non-dilated) were made for gray and white matter, cerebrospinal fluid, and brain images using a paired t-test. In evaluation of tissue volume, estimation error was larger using gray or white matter images than using the brain image, and estimation errors in gray and white matter volume change were found for the brain surface. To our knowledge, this is the first VBM study to show the possibility that VBM of gray and white matter volume on the brain surface may be more affected by individual differences in the level of dilation of the lateral ventricles than by individual differences in gray and white matter volumes. We recommend that VBM evaluation in patients with iNPH should be performed using the brain image rather than the gray and white matter images.

Iterative Structural and Functional Synergistic Resolution Recovery (iSFS-RR) Applied to PET-MR Images in Epilepsy

NASA Astrophysics Data System (ADS)

Silva-Rodríguez, J.; Cortés, J.; Rodríguez-Osorio, X.; López-Urdaneta, J.; Pardo-Montero, J.; Aguiar, P.; Tsoumpas, C.

2016-10-01

Structural Functional Synergistic Resolution Recovery (SFS-RR) is a technique that uses supplementary structural information from MR or CT to improve the spatial resolution of PET or SPECT images. This wavelet-based method may have a potential impact on the clinical decision-making of brain focal disorders such as refractory epilepsy, since it can produce images with better quantitative accuracy and enhanced detectability. In this work, a method for the iterative application of SFS-RR (iSFS-RR) was firstly developed and optimized in terms of convergence and input voxel size, and the corrected images were used for the diagnosis of 18 patients with refractory epilepsy. To this end, PET/MR images were clinically evaluated through visual inspection, atlas-based asymmetry indices (AIs) and SPM (Statistical Parametric Mapping) analysis, using uncorrected images and images corrected with SFS-RR and iSFS-RR. Our results showed that the sensitivity can be increased from 78% for uncorrected images, to 84% for SFS-RR and 94% for the proposed iSFS-RR. Thus, the proposed methodology has demonstrated the potential to improve the management of refractory epilepsy patients in the clinical routine.

Spatio-temporal diffusion of dynamic PET images

NASA Astrophysics Data System (ADS)

Tauber, C.; Stute, S.; Chau, M.; Spiteri, P.; Chalon, S.; Guilloteau, D.; Buvat, I.

2011-10-01

Positron emission tomography (PET) images are corrupted by noise. This is especially true in dynamic PET imaging where short frames are required to capture the peak of activity concentration after the radiotracer injection. High noise results in a possible bias in quantification, as the compartmental models used to estimate the kinetic parameters are sensitive to noise. This paper describes a new post-reconstruction filter to increase the signal-to-noise ratio in dynamic PET imaging. It consists in a spatio-temporal robust diffusion of the 4D image based on the time activity curve (TAC) in each voxel. It reduces the noise in homogeneous areas while preserving the distinct kinetics in regions of interest corresponding to different underlying physiological processes. Neither anatomical priors nor the kinetic model are required. We propose an automatic selection of the scale parameter involved in the diffusion process based on a robust statistical analysis of the distances between TACs. The method is evaluated using Monte Carlo simulations of brain activity distributions. We demonstrate the usefulness of the method and its superior performance over two other post-reconstruction spatial and temporal filters. Our simulations suggest that the proposed method can be used to significantly increase the signal-to-noise ratio in dynamic PET imaging.