Modulation of Auditory Cortex Response to Pitch Variation Following Training with Microtonal Melodies

PubMed Central

Zatorre, Robert J.; Delhommeau, Karine; Zarate, Jean Mary

2012-01-01

We tested changes in cortical functional response to auditory patterns in a configural learning paradigm. We trained 10 human listeners to discriminate micromelodies (consisting of smaller pitch intervals than normally used in Western music) and measured covariation in blood oxygenation signal to increasing pitch interval size in order to dissociate global changes in activity from those specifically associated with the stimulus feature that was trained. A psychophysical staircase procedure with feedback was used for training over a 2-week period. Behavioral tests of discrimination ability performed before and after training showed significant learning on the trained stimuli, and generalization to other frequencies and tasks; no learning occurred in an untrained control group. Before training the functional MRI data showed the expected systematic increase in activity in auditory cortices as a function of increasing micromelody pitch interval size. This function became shallower after training, with the maximal change observed in the right posterior auditory cortex. Global decreases in activity in auditory regions, along with global increases in frontal cortices also occurred after training. Individual variation in learning rate was related to the hemodynamic slope to pitch interval size, such that those who had a higher sensitivity to pitch interval variation prior to learning achieved the fastest learning. We conclude that configural auditory learning entails modulation in the response of auditory cortex to the trained stimulus feature. Reduction in blood oxygenation response to increasing pitch interval size suggests that fewer computational resources, and hence lower neural recruitment, is associated with learning, in accord with models of auditory cortex function, and with data from other modalities. PMID:23227019

[MRI semiotics features of experimental acute intracerebral hematomas].

PubMed

Burenchev, D V; Skvortsova, V I; Tvorogova, T V; Guseva, O I; Gubskiĭ, L V; Kupriianov, D A; Pirogov, Iu A

2009-01-01

The aim of this study was to assess the possibility of revealing intracerebral hematomas (ICH), using MRI, within the first hours after onset and to determine their MRI semiotics features. Thirty animals with experimental ICH were studied. A method of two-stage introduction of autologous blood was used to develop ICH as human spontaneous intracranial hematomas. Within 3-5h after blood introduction to the rat brain. The control MRI was performed in the 3rd and 7th days after blood injections. ICH were definitely identified in the first MRI scans. The MRI semiotics features of acute ICH and their transformations were assessed. The high sensitivity of MRI to ICH as well as the uniform manifestations in all animals were shown. In conclusion, the method has high specificity for acute ICH detection.

Towards in vivo focal cortical dysplasia phenotyping using quantitative MRI.

PubMed

Adler, Sophie; Lorio, Sara; Jacques, Thomas S; Benova, Barbora; Gunny, Roxana; Cross, J Helen; Baldeweg, Torsten; Carmichael, David W

2017-01-01

Focal cortical dysplasias (FCDs) are a range of malformations of cortical development each with specific histopathological features. Conventional radiological assessment of standard structural MRI is useful for the localization of lesions but is unable to accurately predict the histopathological features. Quantitative MRI offers the possibility to probe tissue biophysical properties in vivo and may bridge the gap between radiological assessment and ex-vivo histology. This review will cover histological, genetic and radiological features of FCD following the ILAE classification and will explain how quantitative voxel- and surface-based techniques can characterise these features. We will provide an overview of the quantitative MRI measures available, their link with biophysical properties and finally the potential application of quantitative MRI to the problem of FCD subtyping. Future research linking quantitative MRI to FCD histological properties should improve clinical protocols, allow better characterisation of lesions in vivo and tailored surgical planning to the individual.

Learning Computational Models of Video Memorability from fMRI Brain Imaging.

PubMed

Han, Junwei; Chen, Changyuan; Shao, Ling; Hu, Xintao; Han, Jungong; Liu, Tianming

2015-08-01

Generally, various visual media are unequally memorable by the human brain. This paper looks into a new direction of modeling the memorability of video clips and automatically predicting how memorable they are by learning from brain functional magnetic resonance imaging (fMRI). We propose a novel computational framework by integrating the power of low-level audiovisual features and brain activity decoding via fMRI. Initially, a user study experiment is performed to create a ground truth database for measuring video memorability and a set of effective low-level audiovisual features is examined in this database. Then, human subjects' brain fMRI data are obtained when they are watching the video clips. The fMRI-derived features that convey the brain activity of memorizing videos are extracted using a universal brain reference system. Finally, due to the fact that fMRI scanning is expensive and time-consuming, a computational model is learned on our benchmark dataset with the objective of maximizing the correlation between the low-level audiovisual features and the fMRI-derived features using joint subspace learning. The learned model can then automatically predict the memorability of videos without fMRI scans. Evaluations on publically available image and video databases demonstrate the effectiveness of the proposed framework.

Interval From Imaging to Treatment Delivery in the Radiation Surgery Age: How Long Is Too Long?

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

Seymour, Zachary A., E-mail: seymourz@radonc.ucsf.edu; Fogh, Shannon E.; Westcott, Sarah K.

Purpose: The purpose of this study was to evaluate workflow and patient outcomes related to frameless stereotactic radiation surgery (SRS) for brain metastases. Methods and Materials: We reviewed all treatment demographics, clinical outcomes, and workflow timing, including time from magnetic resonance imaging (MRI), computed tomography (CT) simulation, insurance authorization, and consultation to the start of SRS for brain metastases. Results: A total of 82 patients with 151 brain metastases treated with SRS were evaluated. The median times from consultation, insurance authorization, CT simulation, and MRI for treatment planning were 15, 7, 6, and 11 days to SRS. Local freedom from progressionmore » (LFFP) was lower in metastases with MRI ≥14 days before treatment (P=.0003, log rank). The 6- and 12-month LFFP rate were 95% and 75% for metastasis with interval of <14 days from MRI to treatment compared to 56% and 34% for metastases with MRI ≥14 days before treatment. On multivariate analysis, LFFP remained significantly lower for lesions with MRI ≥14 days at SRS (P=.002, Cox proportional hazards; hazard ratio: 3.4, 95% confidence interval: 1.6-7.3). Conclusions: Delay from MRI to SRS treatment delivery for brain metastases appears to reduce local control. Future studies should monitor the timing from imaging acquisition to treatment delivery. Our experience suggests that the time from MRI to treatment should be <14 days.« less

Genetic Programming and Frequent Itemset Mining to Identify Feature Selection Patterns of iEEG and fMRI Epilepsy Data

PubMed Central

Smart, Otis; Burrell, Lauren

2014-01-01

Pattern classification for intracranial electroencephalogram (iEEG) and functional magnetic resonance imaging (fMRI) signals has furthered epilepsy research toward understanding the origin of epileptic seizures and localizing dysfunctional brain tissue for treatment. Prior research has demonstrated that implicitly selecting features with a genetic programming (GP) algorithm more effectively determined the proper features to discern biomarker and non-biomarker interictal iEEG and fMRI activity than conventional feature selection approaches. However for each the iEEG and fMRI modalities, it is still uncertain whether the stochastic properties of indirect feature selection with a GP yield (a) consistent results within a patient data set and (b) features that are specific or universal across multiple patient data sets. We examined the reproducibility of implicitly selecting features to classify interictal activity using a GP algorithm by performing several selection trials and subsequent frequent itemset mining (FIM) for separate iEEG and fMRI epilepsy patient data. We observed within-subject consistency and across-subject variability with some small similarity for selected features, indicating a clear need for patient-specific features and possible need for patient-specific feature selection or/and classification. For the fMRI, using nearest-neighbor classification and 30 GP generations, we obtained over 60% median sensitivity and over 60% median selectivity. For the iEEG, using nearest-neighbor classification and 30 GP generations, we obtained over 65% median sensitivity and over 65% median selectivity except one patient. PMID:25580059

Neural Signatures of Stimulus Features in Visual Working Memory—A Spatiotemporal Approach

PubMed Central

Jackson, Margaret C.; Klein, Christoph; Mohr, Harald; Shapiro, Kimron L.; Linden, David E. J.

2010-01-01

We examined the neural signatures of stimulus features in visual working memory (WM) by integrating functional magnetic resonance imaging (fMRI) and event-related potential data recorded during mental manipulation of colors, rotation angles, and color–angle conjunctions. The N200, negative slow wave, and P3b were modulated by the information content of WM, and an fMRI-constrained source model revealed a progression in neural activity from posterior visual areas to higher order areas in the ventral and dorsal processing streams. Color processing was associated with activity in inferior frontal gyrus during encoding and retrieval, whereas angle processing involved right parietal regions during the delay interval. WM for color–angle conjunctions did not involve any additional neural processes. The finding that different patterns of brain activity underlie WM for color and spatial information is consistent with ideas that the ventral/dorsal “what/where” segregation of perceptual processing influences WM organization. The absence of characteristic signatures of conjunction-related brain activity, which was generally intermediate between the 2 single conditions, suggests that conjunction judgments are based on the coordinated activity of these 2 streams. PMID:19429863

The clinico-radiological paradox of cognitive function and MRI burden of white matter lesions in people with multiple sclerosis: A systematic review and meta-analysis.

PubMed

Mollison, Daisy; Sellar, Robin; Bastin, Mark; Mollison, Denis; Chandran, Siddharthan; Wardlaw, Joanna; Connick, Peter

2017-01-01

Moderate correlation exists between the imaging quantification of brain white matter lesions and cognitive performance in people with multiple sclerosis (MS). This may reflect the greater importance of other features, including subvisible pathology, or methodological limitations of the primary literature. To summarise the cognitive clinico-radiological paradox and explore the potential methodological factors that could influence the assessment of this relationship. Systematic review and meta-analysis of primary research relating cognitive function to white matter lesion burden. Fifty papers met eligibility criteria for review, and meta-analysis of overall results was possible in thirty-two (2050 participants). Aggregate correlation between cognition and T2 lesion burden was r = -0.30 (95% confidence interval: -0.34, -0.26). Wide methodological variability was seen, particularly related to key factors in the cognitive data capture and image analysis techniques. Resolving the persistent clinico-radiological paradox will likely require simultaneous evaluation of multiple components of the complex pathology using optimum measurement techniques for both cognitive and MRI feature quantification. We recommend a consensus initiative to support common standards for image analysis in MS, enabling benchmarking while also supporting ongoing innovation.

Association between background parenchymal enhancement of breast MRI and BIRADS rating change in the subsequent screening

NASA Astrophysics Data System (ADS)

Aghaei, Faranak; Mirniaharikandehei, Seyedehnafiseh; Hollingsworth, Alan B.; Stoug, Rebecca G.; Pearce, Melanie; Liu, Hong; Zheng, Bin

2018-03-01

Although breast magnetic resonance imaging (MRI) has been used as a breast cancer screening modality for high-risk women, its cancer detection yield remains low (i.e., <= 3%). Thus, increasing breast MRI screening efficacy and cancer detection yield is an important clinical issue in breast cancer screening. In this study, we investigated association between the background parenchymal enhancement (BPE) of breast MRI and the change of diagnostic (BIRADS) status in the next subsequent breast MRI screening. A dataset with 65 breast MRI screening cases was retrospectively assembled. All cases were rated BIRADS-2 (benign findings). In the subsequent screening, 4 cases were malignant (BIRADS-6), 48 remained BIRADS-2 and 13 were downgraded to negative (BIRADS-1). A computer-aided detection scheme was applied to process images of the first set of breast MRI screening. Total of 33 features were computed including texture feature and global BPE features. Texture features were computed from either a gray-level co-occurrence matrix or a gray level run length matrix. Ten global BPE features were also initially computed from two breast regions and bilateral difference between the left and right breasts. Box-plot based analysis shows positive association between texture features and BIRADS rating levels in the second screening. Furthermore, a logistic regression model was built using optimal features selected by a CFS based feature selection method. Using a leave-one-case-out based cross-validation method, classification yielded an overall 75% accuracy in predicting the improvement (or downgrade) of diagnostic status (to BIRAD-1) in the subsequent breast MRI screening. This study demonstrated potential of developing a new quantitative imaging marker to predict diagnostic status change in the short-term, which may help eliminate a high fraction of unnecessary repeated breast MRI screenings and increase the cancer detection yield.

High- and low-risk profiles for the development of multiple sclerosis within 10 years after optic neuritis: experience of the optic neuritis treatment trial.

PubMed

Beck, Roy W; Trobe, Jonathan D; Moke, Pamela S; Gal, Robin L; Xing, Dongyuan; Bhatti, M Tariq; Brodsky, Michael C; Buckley, Edward G; Chrousos, Georgia A; Corbett, James; Eggenberger, Eric; Goodwin, James A; Katz, Barrett; Kaufman, David I; Keltner, John L; Kupersmith, Mark J; Miller, Neil R; Nazarian, Sarkis; Orengo-Nania, Silvia; Savino, Peter J; Shults, William T; Smith, Craig H; Wall, Michael

2003-07-01

To identify factors associated with a high and low risk of developing multiple sclerosis after an initial episode of optic neuritis. Three hundred eighty-eight patients who experienced acute optic neuritis between July 1, 1988, and June 30, 1991, were followed up prospectively for the development of multiple sclerosis. Consenting patients were reassessed after 10 to 13 years. The 10-year risk of multiple sclerosis was 38% (95% confidence interval, 33%-43%). Patients (160) who had 1 or more typical lesions on the baseline magnetic resonance imaging (MRI) scan of the brain had a 56% risk; those with no lesions (191) had a 22% risk (P<.001, log rank test). Among the patients who had no lesions on MRI, male gender and optic disc swelling were associated with a lower risk of multiple sclerosis, as was the presence of the following atypical features for optic neuritis: no light perception vision; absence of pain; and ophthalmoscopic findings of severe optic disc edema, peripapillary hemorrhages, or retinal exudates. The 10-year risk of multiple sclerosis following an initial episode of acute optic neuritis is significantly higher if there is a single brain MRI lesion; higher numbers of lesions do not appreciably increase that risk. However, even when brain lesions are seen on MRI, more than 40% of the patients will not develop clinical multiple sclerosis after 10 years. In the absence of MRI lesions, certain demographic and clinical features seem to predict a very low likelihood of developing multiple sclerosis. This natural history information is a critical input for estimating a patient's 10-year multiple sclerosis risk and for weighing the benefit of initiating prophylactic treatment at the time of optic neuritis or other initial demyelinating events in the central nervous system.

Distinguishing prostate cancer from benign confounders via a cascaded classifier on multi-parametric MRI

NASA Astrophysics Data System (ADS)

Litjens, G. J. S.; Elliott, R.; Shih, N.; Feldman, M.; Barentsz, J. O.; Hulsbergen-van de Kaa, C. A.; Kovacs, I.; Huisman, H. J.; Madabhushi, A.

2014-03-01

Learning how to separate benign confounders from prostate cancer is important because the imaging characteristics of these confounders are poorly understood. Furthermore, the typical representations of the MRI parameters might not be enough to allow discrimination. The diagnostic uncertainty this causes leads to a lower diagnostic accuracy. In this paper a new cascaded classifier is introduced to separate prostate cancer and benign confounders on MRI in conjunction with specific computer-extracted features to distinguish each of the benign classes (benign prostatic hyperplasia (BPH), inflammation, atrophy or prostatic intra-epithelial neoplasia (PIN). In this study we tried to (1) calculate different mathematical representations of the MRI parameters which more clearly express subtle differences between different classes, (2) learn which of the MRI image features will allow to distinguish specific benign confounders from prostate cancer, and (2) find the combination of computer-extracted MRI features to best discriminate cancer from the confounding classes using a cascaded classifier. One of the most important requirements for identifying MRI signatures for adenocarcinoma, BPH, atrophy, inflammation, and PIN is accurate mapping of the location and spatial extent of the confounder and cancer categories from ex vivo histopathology to MRI. Towards this end we employed an annotated prostatectomy data set of 31 patients, all of whom underwent a multi-parametric 3 Tesla MRI prior to radical prostatectomy. The prostatectomy slides were carefully co-registered to the corresponding MRI slices using an elastic registration technique. We extracted texture features from the T2-weighted imaging, pharmacokinetic features from the dynamic contrast enhanced imaging and diffusion features from the diffusion-weighted imaging for each of the confounder classes and prostate cancer. These features were selected because they form the mainstay of clinical diagnosis. Relevant features for each of the classes were selected using maximum relevance minimum redundancy feature selection, allowing us to perform classifier independent feature selection. The selected features were then incorporated in a cascading classifier, which can focus on easier sub-tasks at each stage, leaving the more difficult classification tasks for later stages. Results show that distinct features are relevant for each of the benign classes, for example the fraction of extra-vascular, extra-cellular space in a voxel is a clear discriminator for inflammation. Furthermore, the cascaded classifier outperforms both multi-class and one-shot classifiers in overall accuracy for discriminating confounders from cancer: 0.76 versus 0.71 and 0.62.

Neuropathology of White Matter Lesions, Blood-Brain Barrier Dysfunction, and Dementia.

PubMed

Hainsworth, Atticus H; Minett, Thais; Andoh, Joycelyn; Forster, Gillian; Bhide, Ishaan; Barrick, Thomas R; Elderfield, Kay; Jeevahan, Jamuna; Markus, Hugh S; Bridges, Leslie R

2017-10-01

We tested whether blood-brain barrier dysfunction in subcortical white matter is associated with white matter abnormalities or risk of clinical dementia in older people (n=126; mean age 86.4, SD: 7.7 years) in the MRC CFAS (Medical Research Council Cognitive Function and Ageing Study). Using digital pathology, we quantified blood-brain barrier dysfunction (defined by immunohistochemical labeling for the plasma marker fibrinogen). This was assessed within subcortical white matter tissue samples harvested from postmortem T 2 magnetic resonance imaging (MRI)-detected white matter hyperintensities, from normal-appearing white matter (distant from coexistent MRI-defined hyperintensities), and from equivalent areas in MRI normal brains. Histopathologic lesions were defined using a marker for phagocytic microglia (CD68, clone PGM1). Extent of fibrinogen labeling was not significantly associated with white matter abnormalities defined either by MRI (odds ratio, 0.90; 95% confidence interval, 0.79-1.03; P =0.130) or by histopathology (odds ratio, 0.93; 95% confidence interval, 0.77-1.12; P =0.452). Among participants with normal MRI (no detectable white matter hyperintensities), increased fibrinogen was significantly related to decreased risk of clinical dementia (odds ratio, 0.74; 95% confidence interval, 0.58-0.94; P =0.013). Among participants with histological lesions, increased fibrinogen was related to increased risk of dementia (odds ratio, 2.26; 95% confidence interval, 1.25-4.08; P =0.007). Our data suggest that some degree of blood-brain barrier dysfunction is common in older people and that this may be related to clinical dementia risk, additional to standard MRI biomarkers. © 2017 American Heart Association, Inc.

Abnormal brain magnetic resonance imaging in two patients with Smith-Magenis syndrome.

PubMed

Maya, Idit; Vinkler, Chana; Konen, Osnat; Kornreich, Liora; Steinberg, Tamar; Yeshaya, Josepha; Latarowski, Victoria; Shohat, Mordechai; Lev, Dorit; Baris, Hagit N

2014-08-01

Smith-Magenis syndrome (SMS) is a clinically recognizable contiguous gene syndrome ascribed to an interstitial deletion in chromosome 17p11.2. Seventy percent of SMS patients have a common deletion interval spanning 3.5 megabases (Mb). Clinical features of SMS include characteristic mild dysmorphic features, ocular anomalies, short stature, brachydactyly, and hypotonia. SMS patients have a unique neurobehavioral phenotype that includes intellectual disability, self-injurious behavior and severe sleep disturbance. Little has been reported in the medical literature about anatomical brain anomalies in patients with SMS. Here we describe two patients with SMS caused by the common deletion in 17p11.2 diagnosed using chromosomal microarray (CMA). Both patients had a typical clinical presentation and abnormal brain magnetic resonance imaging (MRI) findings. One patient had subependymal periventricular gray matter heterotopia, and the second had a thin corpus callosum, a thin brain stem and hypoplasia of the cerebellar vermis. This report discusses the possible abnormal MRI images in SMS and reviews the literature on brain malformations in SMS. Finally, although structural brain malformations in SMS patients are not a common feature, we suggest baseline routine brain imaging in patients with SMS in particular, and in patients with chromosomal microdeletion/microduplication syndromes in general. Structural brain malformations in these patients may affect the decision-making process regarding their management. © 2014 Wiley Periodicals, Inc.

Cortex-based inter-subject analysis of iEEG and fMRI data sets: application to sustained task-related BOLD and gamma responses.

PubMed

Esposito, Fabrizio; Singer, Neomi; Podlipsky, Ilana; Fried, Itzhak; Hendler, Talma; Goebel, Rainer

2013-02-01

Linking regional metabolic changes with fluctuations in the local electromagnetic fields directly on the surface of the human cerebral cortex is of tremendous importance for a better understanding of detailed brain processes. Functional magnetic resonance imaging (fMRI) and intra-cranial electro-encephalography (iEEG) measure two technically unrelated but spatially and temporally complementary sets of functional descriptions of human brain activity. In order to allow fine-grained spatio-temporal human brain mapping at the population-level, an effective comparative framework for the cortex-based inter-subject analysis of iEEG and fMRI data sets is needed. We combined fMRI and iEEG recordings of the same patients with epilepsy during alternated intervals of passive movie viewing and music listening to explore the degree of local spatial correspondence and temporal coupling between blood oxygen level dependent (BOLD) fMRI changes and iEEG spectral power modulations across the cortical surface after cortex-based inter-subject alignment. To this purpose, we applied a simple model of the iEEG activity spread around each electrode location and the cortex-based inter-subject alignment procedure to transform discrete iEEG measurements into cortically distributed group patterns by establishing a fine anatomic correspondence of many iEEG cortical sites across multiple subjects. Our results demonstrate the feasibility of a multi-modal inter-subject cortex-based distributed analysis for combining iEEG and fMRI data sets acquired from multiple subjects with the same experimental paradigm but with different iEEG electrode coverage. The proposed iEEG-fMRI framework allows for improved group statistics in a common anatomical space and preserves the dynamic link between the temporal features of the two modalities. Copyright © 2012 Elsevier Inc. All rights reserved.

Grading of Gliomas by Using Radiomic Features on Multiple Magnetic Resonance Imaging (MRI) Sequences.

PubMed

Qin, Jiang-Bo; Liu, Zhenyu; Zhang, Hui; Shen, Chen; Wang, Xiao-Chun; Tan, Yan; Wang, Shuo; Wu, Xiao-Feng; Tian, Jie

2017-05-07

BACKGROUND Gliomas are the most common primary brain neoplasms. Misdiagnosis occurs in glioma grading due to an overlap in conventional MRI manifestations. The aim of the present study was to evaluate the power of radiomic features based on multiple MRI sequences - T2-Weighted-Imaging-FLAIR (FLAIR), T1-Weighted-Imaging-Contrast-Enhanced (T1-CE), and Apparent Diffusion Coefficient (ADC) map - in glioma grading, and to improve the power of glioma grading by combining features. MATERIAL AND METHODS Sixty-six patients with histopathologically proven gliomas underwent T2-FLAIR and T1WI-CE sequence scanning with some patients (n=63) also undergoing DWI scanning. A total of 114 radiomic features were derived with radiomic methods by using in-house software. All radiomic features were compared between high-grade gliomas (HGGs) and low-grade gliomas (LGGs). Features with significant statistical differences were selected for receiver operating characteristic (ROC) curve analysis. The relationships between significantly different radiomic features and glial fibrillary acidic protein (GFAP) expression were evaluated. RESULTS A total of 8 radiomic features from 3 MRI sequences displayed significant differences between LGGs and HGGs. FLAIR GLCM Cluster Shade, T1-CE GLCM Entropy, and ADC GLCM Homogeneity were the best features to use in differentiating LGGs and HGGs in each MRI sequence. The combined feature was best able to differentiate LGGs and HGGs, which improved the accuracy of glioma grading compared to the above features in each MRI sequence. A significant correlation was found between GFAP and T1-CE GLCM Entropy, as well as between GFAP and ADC GLCM Homogeneity. CONCLUSIONS The combined radiomic feature had the highest efficacy in distinguishing LGGs from HGGs.

Supervised learning based multimodal MRI brain tumour segmentation using texture features from supervoxels.

PubMed

Soltaninejad, Mohammadreza; Yang, Guang; Lambrou, Tryphon; Allinson, Nigel; Jones, Timothy L; Barrick, Thomas R; Howe, Franklyn A; Ye, Xujiong

2018-04-01

Accurate segmentation of brain tumour in magnetic resonance images (MRI) is a difficult task due to various tumour types. Using information and features from multimodal MRI including structural MRI and isotropic (p) and anisotropic (q) components derived from the diffusion tensor imaging (DTI) may result in a more accurate analysis of brain images. We propose a novel 3D supervoxel based learning method for segmentation of tumour in multimodal MRI brain images (conventional MRI and DTI). Supervoxels are generated using the information across the multimodal MRI dataset. For each supervoxel, a variety of features including histograms of texton descriptor, calculated using a set of Gabor filters with different sizes and orientations, and first order intensity statistical features are extracted. Those features are fed into a random forests (RF) classifier to classify each supervoxel into tumour core, oedema or healthy brain tissue. The method is evaluated on two datasets: 1) Our clinical dataset: 11 multimodal images of patients and 2) BRATS 2013 clinical dataset: 30 multimodal images. For our clinical dataset, the average detection sensitivity of tumour (including tumour core and oedema) using multimodal MRI is 86% with balanced error rate (BER) 7%; while the Dice score for automatic tumour segmentation against ground truth is 0.84. The corresponding results of the BRATS 2013 dataset are 96%, 2% and 0.89, respectively. The method demonstrates promising results in the segmentation of brain tumour. Adding features from multimodal MRI images can largely increase the segmentation accuracy. The method provides a close match to expert delineation across all tumour grades, leading to a faster and more reproducible method of brain tumour detection and delineation to aid patient management. Copyright © 2018 Elsevier B.V. All rights reserved.

Identifying in vivo DCE MRI markers associated with microvessel architecture and gleason grades of prostate cancer.

PubMed

Singanamalli, Asha; Rusu, Mirabela; Sparks, Rachel E; Shih, Natalie N C; Ziober, Amy; Wang, Li-Ping; Tomaszewski, John; Rosen, Mark; Feldman, Michael; Madabhushi, Anant

2016-01-01

To identify computer extracted in vivo dynamic contrast enhanced (DCE) MRI markers associated with quantitative histomorphometric (QH) characteristics of microvessels and Gleason scores (GS) in prostate cancer. This study considered retrospective data from 23 biopsy confirmed prostate cancer patients who underwent 3 Tesla multiparametric MRI before radical prostatectomy (RP). Representative slices from RP specimens were stained with vascular marker CD31. Tumor extent was mapped from RP sections onto DCE MRI using nonlinear registration methods. Seventy-seven microvessel QH features and 18 DCE MRI kinetic features were extracted and evaluated for their ability to distinguish low from intermediate and high GS. The effect of temporal sampling on kinetic features was assessed and correlations between those robust to temporal resolution and microvessel features discriminative of GS were examined. A total of 12 microvessel architectural features were discriminative of low and intermediate/high grade tumors with area under the receiver operating characteristic curve (AUC) > 0.7. These features were most highly correlated with mean washout gradient (WG) (max rho = -0.62). Independent analysis revealed WG to be moderately robust to temporal resolution (intraclass correlation coefficient [ICC] = 0.63) and WG variance, which was poorly correlated with microvessel features, to be predictive of low grade tumors (AUC = 0.77). Enhancement ratio was the most robust (ICC = 0.96) and discriminative (AUC = 0.78) kinetic feature but was moderately correlated with microvessel features (max rho = -0.52). Computer extracted features of prostate DCE MRI appear to be correlated with microvessel architecture and may be discriminative of low versus intermediate and high GS. © 2015 Wiley Periodicals, Inc.

Multiresolution texture models for brain tumor segmentation in MRI.

PubMed

Iftekharuddin, Khan M; Ahmed, Shaheen; Hossen, Jakir

2011-01-01

In this study we discuss different types of texture features such as Fractal Dimension (FD) and Multifractional Brownian Motion (mBm) for estimating random structures and varying appearance of brain tissues and tumors in magnetic resonance images (MRI). We use different selection techniques including KullBack - Leibler Divergence (KLD) for ranking different texture and intensity features. We then exploit graph cut, self organizing maps (SOM) and expectation maximization (EM) techniques to fuse selected features for brain tumors segmentation in multimodality T1, T2, and FLAIR MRI. We use different similarity metrics to evaluate quality and robustness of these selected features for tumor segmentation in MRI for real pediatric patients. We also demonstrate a non-patient-specific automated tumor prediction scheme by using improved AdaBoost classification based on these image features.

Natural image classification driven by human brain activity

NASA Astrophysics Data System (ADS)

Zhang, Dai; Peng, Hanyang; Wang, Jinqiao; Tang, Ming; Xue, Rong; Zuo, Zhentao

2016-03-01

Natural image classification has been a hot topic in computer vision and pattern recognition research field. Since the performance of an image classification system can be improved by feature selection, many image feature selection methods have been developed. However, the existing supervised feature selection methods are typically driven by the class label information that are identical for different samples from the same class, ignoring with-in class image variability and therefore degrading the feature selection performance. In this study, we propose a novel feature selection method, driven by human brain activity signals collected using fMRI technique when human subjects were viewing natural images of different categories. The fMRI signals associated with subjects viewing different images encode the human perception of natural images, and therefore may capture image variability within- and cross- categories. We then select image features with the guidance of fMRI signals from brain regions with active response to image viewing. Particularly, bag of words features based on GIST descriptor are extracted from natural images for classification, and a sparse regression base feature selection method is adapted to select image features that can best predict fMRI signals. Finally, a classification model is built on the select image features to classify images without fMRI signals. The validation experiments for classifying images from 4 categories of two subjects have demonstrated that our method could achieve much better classification performance than the classifiers built on image feature selected by traditional feature selection methods.

Combined 18F-Fluciclovine PET/MRI Shows Potential for Detection and Characterization of High-Risk Prostate Cancer.

PubMed

Elschot, Mattijs; Selnæs, Kirsten M; Sandsmark, Elise; Krüger-Stokke, Brage; Størkersen, Øystein; Giskeødegård, Guro F; Tessem, May-Britt; Moestue, Siver A; Bertilsson, Helena; Bathen, Tone F

2018-05-01

The objective of this study was to investigate whether quantitative imaging features derived from combined 18 F-fluciclovine PET/multiparametric MRI show potential for detection and characterization of primary prostate cancer. Methods: Twenty-eight patients diagnosed with high-risk prostate cancer underwent simultaneous 18 F-fluciclovine PET/MRI before radical prostatectomy. Volumes of interest (VOIs) for prostate tumors, benign prostatic hyperplasia (BPH) nodules, prostatitis, and healthy tissue were delineated on T2-weighted images, using histology as a reference. Tumor VOIs were marked as high-grade (≥Gleason grade group 3) or not. MRI and PET features were extracted on the voxel and VOI levels. Partial least-squared discriminant analysis (PLS-DA) with double leave-one-patient-out cross-validation was performed to distinguish tumors from benign tissue (BPH, prostatitis, or healthy tissue) and high-grade tumors from other tissue (low-grade tumors or benign tissue). The performance levels of PET, MRI, and combined PET/MRI features were compared using the area under the receiver-operating-characteristic curve (AUC). Results: Voxel and VOI features were extracted from 40 tumor VOIs (26 high-grade), 36 BPH VOIs, 6 prostatitis VOIs, and 37 healthy-tissue VOIs. PET/MRI performed better than MRI and PET alone for distinguishing tumors from benign tissue (AUCs of 87%, 81%, and 83%, respectively, at the voxel level and 96%, 93%, and 93%, respectively, at the VOI level) and high-grade tumors from other tissue (AUCs of 85%, 79%, and 81%, respectively, at the voxel level and 93%, 93%, and 91%, respectively, at the VOI level). T2-weighted MRI, diffusion-weighted MRI, and PET features were the most important for classification. Conclusion: Combined 18 F-fluciclovine PET/multiparametric MRI shows potential for improving detection and characterization of high-risk prostate cancer, in comparison to MRI and PET alone. © 2018 by the Society of Nuclear Medicine and Molecular Imaging.

Textural features of dynamic contrast-enhanced MRI derived model-free and model-based parameter maps in glioma grading.

PubMed

Xie, Tian; Chen, Xiao; Fang, Jingqin; Kang, Houyi; Xue, Wei; Tong, Haipeng; Cao, Peng; Wang, Sumei; Yang, Yizeng; Zhang, Weiguo

2018-04-01

Presurgical glioma grading by dynamic contrast-enhanced MRI (DCE-MRI) has unresolved issues. The aim of this study was to investigate the ability of textural features derived from pharmacokinetic model-based or model-free parameter maps of DCE-MRI in discriminating between different grades of gliomas, and their correlation with pathological index. Retrospective. Forty-two adults with brain gliomas. 3.0T, including conventional anatomic sequences and DCE-MRI sequences (variable flip angle T1-weighted imaging and three-dimensional gradient echo volumetric imaging). Regions of interest on the cross-sectional images with maximal tumor lesion. Five commonly used textural features, including Energy, Entropy, Inertia, Correlation, and Inverse Difference Moment (IDM), were generated. All textural features of model-free parameters (initial area under curve [IAUC], maximal signal intensity [Max SI], maximal up-slope [Max Slope]) could effectively differentiate between grade II (n = 15), grade III (n = 13), and grade IV (n = 14) gliomas (P < 0.05). Two textural features, Entropy and IDM, of four DCE-MRI parameters, including Max SI, Max Slope (model-free parameters), vp (Extended Tofts), and vp (Patlak) could differentiate grade III and IV gliomas (P < 0.01) in four measurements. Both Entropy and IDM of Patlak-based K trans and vp could differentiate grade II (n = 15) from III (n = 13) gliomas (P < 0.01) in four measurements. No textural features of any DCE-MRI parameter maps could discriminate between subtypes of grade II and III gliomas (P < 0.05). Both Entropy and IDM of Extended Tofts- and Patlak-based vp showed highest area under curve in discriminating between grade III and IV gliomas. However, intraclass correlation coefficient (ICC) of these features revealed relatively lower inter-observer agreement. No significant correlation was found between microvascular density and textural features, compared with a moderate correlation found between cellular proliferation index and those features. Textural features of DCE-MRI parameter maps displayed a good ability in glioma grading. 3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018;47:1099-1111. © 2017 International Society for Magnetic Resonance in Medicine.

Feature-space-based FMRI analysis using the optimal linear transformation.

PubMed

Sun, Fengrong; Morris, Drew; Lee, Wayne; Taylor, Margot J; Mills, Travis; Babyn, Paul S

2010-09-01

The optimal linear transformation (OLT), an image analysis technique of feature space, was first presented in the field of MRI. This paper proposes a method of extending OLT from MRI to functional MRI (fMRI) to improve the activation-detection performance over conventional approaches of fMRI analysis. In this method, first, ideal hemodynamic response time series for different stimuli were generated by convolving the theoretical hemodynamic response model with the stimulus timing. Second, constructing hypothetical signature vectors for different activity patterns of interest by virtue of the ideal hemodynamic responses, OLT was used to extract features of fMRI data. The resultant feature space had particular geometric clustering properties. It was then classified into different groups, each pertaining to an activity pattern of interest; the applied signature vector for each group was obtained by averaging. Third, using the applied signature vectors, OLT was applied again to generate fMRI composite images with high SNRs for the desired activity patterns. Simulations and a blocked fMRI experiment were employed for the method to be verified and compared with the general linear model (GLM)-based analysis. The simulation studies and the experimental results indicated the superiority of the proposed method over the GLM-based analysis in detecting brain activities.

Magnetic resonance imaging classification of haemodialysis-related amyloidosis of the shoulder: risk factors and arthroscopic treatment.

PubMed

Ando, Akira; Hagiwara, Yoshihiro; Sekiguchi, Takuya; Koide, Masashi; Kanazawa, Kenji; Watanabe, Takashi; Itoi, Eiji

2017-07-01

This study proposed new magnetic resonance imaging (MRI) of haemodialysis shoulders (HDS) focusing on the changes of the rotator cuff, and rotator interval and risk factors for the development of HDS were examined. Eighty-five shoulders in 72 patients with a chief complaint of shoulder pain during haemodialysis and at least 10 years of haemodialysis were included. They were classified into 5 groups based on the thickness of the rotator cuff and conditions of rotator interval. Clinical and radiological findings in each grade were examined, and risk factors for the development of HDS were evaluated. Arthroscopic surgeries were performed on 22 shoulders in 20 patients, and arthroscopic findings were also evaluated. Positive correlations for the development of HDS were observed in duration of haemodialysis, positive hepatitis C virus (HCV) infection, and previous haemodialysis-related orthopaedic surgery (P < 0.001, respectively). Strong correlations were observed between positive HCV and the progression of HDS (odds ratio 24.8, 95 % confidence interval 5.7-107.6). Arthroscopically, progression of the surrounding soft tissue degeneration was observed, and operative times were lengthened depending on the progression of MRI grading. A new MRI classification of HDS which may be helpful when considering arthroscopic surgeries has been proposed. Positive HCV infection was strongly associated with the progression of HDS on MRI. Conditions of the rotator interval and the rotator cuff based on the MRI classification should be examined when treating HDS patients. III.

Non-negative matrix factorization in texture feature for classification of dementia with MRI data

NASA Astrophysics Data System (ADS)

Sarwinda, D.; Bustamam, A.; Ardaneswari, G.

2017-07-01

This paper investigates applications of non-negative matrix factorization as feature selection method to select the features from gray level co-occurrence matrix. The proposed approach is used to classify dementia using MRI data. In this study, texture analysis using gray level co-occurrence matrix is done to feature extraction. In the feature extraction process of MRI data, we found seven features from gray level co-occurrence matrix. Non-negative matrix factorization selected three features that influence of all features produced by feature extractions. A Naïve Bayes classifier is adapted to classify dementia, i.e. Alzheimer's disease, Mild Cognitive Impairment (MCI) and normal control. The experimental results show that non-negative factorization as feature selection method able to achieve an accuracy of 96.4% for classification of Alzheimer's and normal control. The proposed method also compared with other features selection methods i.e. Principal Component Analysis (PCA).

A general prediction model for the detection of ADHD and Autism using structural and functional MRI.

PubMed

Sen, Bhaskar; Borle, Neil C; Greiner, Russell; Brown, Matthew R G

2018-01-01

This work presents a novel method for learning a model that can diagnose Attention Deficit Hyperactivity Disorder (ADHD), as well as Autism, using structural texture and functional connectivity features obtained from 3-dimensional structural magnetic resonance imaging (MRI) and 4-dimensional resting-state functional magnetic resonance imaging (fMRI) scans of subjects. We explore a series of three learners: (1) The LeFMS learner first extracts features from the structural MRI images using the texture-based filters produced by a sparse autoencoder. These filters are then convolved with the original MRI image using an unsupervised convolutional network. The resulting features are used as input to a linear support vector machine (SVM) classifier. (2) The LeFMF learner produces a diagnostic model by first computing spatial non-stationary independent components of the fMRI scans, which it uses to decompose each subject's fMRI scan into the time courses of these common spatial components. These features can then be used with a learner by themselves or in combination with other features to produce the model. Regardless of which approach is used, the final set of features are input to a linear support vector machine (SVM) classifier. (3) Finally, the overall LeFMSF learner uses the combined features obtained from the two feature extraction processes in (1) and (2) above as input to an SVM classifier, achieving an accuracy of 0.673 on the ADHD-200 holdout data and 0.643 on the ABIDE holdout data. Both of these results, obtained with the same LeFMSF framework, are the best known, over all hold-out accuracies on these datasets when only using imaging data-exceeding previously-published results by 0.012 for ADHD and 0.042 for Autism. Our results show that combining multi-modal features can yield good classification accuracy for diagnosis of ADHD and Autism, which is an important step towards computer-aided diagnosis of these psychiatric diseases and perhaps others as well.

Coregistered whole body magnetic resonance imaging-positron emission tomography (MRI-PET) versus PET-computed tomography plus brain MRI in staging resectable lung cancer: comparisons of clinical effectiveness in a randomized trial.

PubMed

Yi, Chin A; Lee, Kyung Soo; Lee, Ho Yun; Kim, Seonwoo; Kwon, O Jung; Kim, Hojoong; Choi, Joon Young; Kim, Byung-Tae; Hwang, Hye Sun; Shim, Young Mog

2013-05-15

The objective of this study was to assess whether coregistered whole brain (WB) magnetic resonance imaging-positron emission tomography (MRI-PET) would increase the number of correctly upstaged patients compared with WB PET-computed tomography (PET-CT) plus dedicated brain MRI in patients with nonsmall cell lung cancer (NSCLC). From January 2010 through November 2011, patients with NSCLC who had resectable disease based on conventional staging were assigned randomly either to coregistered MRI-PET or WB PET-CT plus brain MRI (ClinicalTrials.gov trial NCT01065415). The primary endpoint was correct upstaging (the identification of lesions with higher tumor, lymph node, or metastasis classification, verified with biopsy or other diagnostic test) to have the advantage of avoiding unnecessary thoracotomy, to determine appropriate treatment, and to accurately predict patient prognosis. The secondary endpoints were over staging and under staging compared with pathologic staging. Lung cancer was correctly upstaged in 37 of 143 patients (25.9%) in the MRI-PET group and in 26 of 120 patients (21.7%) in the PET-CT plus brain MRI group (4.2% difference; 95% confidence interval, -6.1% to 14.5%; P = .426). Lung cancer was over staged in 26 of 143 patients (18.2%) in the MRI-PET group and in 7 of 120 patients (5.8%) in the PET-CT plus brain MRI group (12.4% difference; 95% confidence interval, 4.8%-20%; P = .003), whereas lung cancer was under staged in 18 of 143 patients (12.6%) and in 28 of 120 patients (23.3%), respectively (-10.7% difference; 95% confidence interval, -20.1% to -1.4%; P = .022). Although both staging tools allowed greater than 20% correct upstaging compared with conventional staging methods, coregistered MRI-PET did not appear to help identify significantly more correctly upstaged patients than PET-CT plus brain MRI in patients with NSCLC. Copyright © 2013 American Cancer Society.

Functional feature embedded space mapping of fMRI data.

PubMed

Hu, Jin; Tian, Jie; Yang, Lei

2006-01-01

We have proposed a new method for fMRI data analysis which is called Functional Feature Embedded Space Mapping (FFESM). Our work mainly focuses on the experimental design with periodic stimuli which can be described by a number of Fourier coefficients in the frequency domain. A nonlinear dimension reduction technique Isomap is applied to the high dimensional features obtained from frequency domain of the fMRI data for the first time. Finally, the presence of activated time series is identified by the clustering method in which the information theoretic criterion of minimum description length (MDL) is used to estimate the number of clusters. The feasibility of our algorithm is demonstrated by real human experiments. Although we focus on analyzing periodic fMRI data, the approach can be extended to analyze non-periodic fMRI data (event-related fMRI) by replacing the Fourier analysis with a wavelet analysis.

Magnetization-prepared rapid acquisition with gradient echo magnetic resonance imaging signal and texture features for the prediction of mild cognitive impairment to Alzheimer's disease progression.

PubMed

Martinez-Torteya, Antonio; Rodriguez-Rojas, Juan; Celaya-Padilla, José M; Galván-Tejada, Jorge I; Treviño, Victor; Tamez-Peña, Jose

2014-10-01

Early diagnoses of Alzheimer's disease (AD) would confer many benefits. Several biomarkers have been proposed to achieve such a task, where features extracted from magnetic resonance imaging (MRI) have played an important role. However, studies have focused exclusively on morphological characteristics. This study aims to determine whether features relating to the signal and texture of the image could predict mild cognitive impairment (MCI) to AD progression. Clinical, biological, and positron emission tomography information and MRI images of 62 subjects from the AD neuroimaging initiative were used in this study, extracting 4150 features from each MRI. Within this multimodal database, a feature selection algorithm was used to obtain an accurate and small logistic regression model, generated by a methodology that yielded a mean blind test accuracy of 0.79. This model included six features, five of them obtained from the MRI images, and one obtained from genotyping. A risk analysis divided the subjects into low-risk and high-risk groups according to a prognostic index. The groups were statistically different ([Formula: see text]). These results demonstrated that MRI features related to both signal and texture add MCI to AD predictive power, and supported the ongoing notion that multimodal biomarkers outperform single-modality ones.

TU-CD-BRB-12: Radiogenomics of MRI-Guided Prostate Cancer Biopsy Habitats

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

Stoyanova, R; Lynne, C; Abraham, S

2015-06-15

Purpose: Diagnostic prostate biopsies are subject to sampling bias. We hypothesize that quantitative imaging with multiparametric (MP)-MRI can more accurately direct targeted biopsies to index lesions associated with highest risk clinical and genomic features. Methods: Regionally distinct prostate habitats were delineated on MP-MRI (T2-weighted, perfusion and diffusion imaging). Directed biopsies were performed on 17 habitats from 6 patients using MRI-ultrasound fusion. Biopsy location was characterized with 52 radiographic features. Transcriptome-wide analysis of 1.4 million RNA probes was performed on RNA from each habitat. Genomics features with insignificant expression values (<0.25) and interquartile range <0.5 were filtered, leaving total of 212more » genes. Correlation between imaging features, genes and a 22 feature genomic classifier (GC), developed as a prognostic assay for metastasis after radical prostatectomy was investigated. Results: High quality genomic data was derived from 17 (100%) biopsies. Using the 212 ‘unbiased’ genes, the samples clustered by patient origin in unsupervised analysis. When only prostate cancer related genomic features were used, hierarchical clustering revealed samples clustered by needle-biopsy Gleason score (GS). Similarly, principal component analysis of the imaging features, found the primary source of variance segregated the samples into high (≥7) and low (6) GS. Pearson’s correlation analysis of genes with significant expression showed two main patterns of gene expression clustering prostate peripheral and transitional zone MRI features. Two-way hierarchical clustering of GC with radiomics features resulted in the expected groupings of high and low expressed genes in this metastasis signature. Conclusions: MP-MRI-targeted diagnostic biopsies can potentially improve risk stratification by directing pathological and genomic analysis to clinically significant index lesions. As determinant lesions are more reliably identified, targeting with radiotherapy should improve outcome. This is the first demonstration of a link between quantitative imaging features (radiomics) with genomic features in MRI-directed prostate biopsies. The research was supported by NIH- NCI R01 CA 189295 and R01 CA 189295; E Davicioni is partial owner of GenomeDx Biosciences, Inc. M Takhar, N Erho, L Lam, C Buerki and E Davicioni are current employees at GenomeDx Biosciences, Inc.« less

Haralick textural features on T2 -weighted MRI are associated with biochemical recurrence following radiotherapy for peripheral zone prostate cancer.

PubMed

Gnep, Khémara; Fargeas, Auréline; Gutiérrez-Carvajal, Ricardo E; Commandeur, Frédéric; Mathieu, Romain; Ospina, Juan D; Rolland, Yan; Rohou, Tanguy; Vincendeau, Sébastien; Hatt, Mathieu; Acosta, Oscar; de Crevoisier, Renaud

2017-01-01

To explore the association between magnetic resonance imaging (MRI), including Haralick textural features, and biochemical recurrence following prostate cancer radiotherapy. In all, 74 patients with peripheral zone localized prostate adenocarcinoma underwent pretreatment 3.0T MRI before external beam radiotherapy. Median follow-up of 47 months revealed 11 patients with biochemical recurrence. Prostate tumors were segmented on T 2 -weighted sequences (T 2 -w) and contours were propagated onto the coregistered apparent diffusion coefficient (ADC) images. We extracted 140 image features from normalized T 2 -w and ADC images corresponding to first-order (n = 6), gradient-based (n = 4), and second-order Haralick textural features (n = 130). Four geometrical features (tumor diameter, perimeter, area, and volume) were also computed. Correlations between Gleason score and MRI features were assessed. Cox regression analysis and random survival forests (RSF) were performed to assess the association between MRI features and biochemical recurrence. Three T 2 -w and one ADC Haralick textural features were significantly correlated with Gleason score (P < 0.05). Twenty-eight T 2 -w Haralick features and all four geometrical features were significantly associated with biochemical recurrence (P < 0.05). The most relevant features were Haralick features T 2 -w contrast, T 2 -w difference variance, ADC median, along with tumor volume and tumor area (C-index from 0.76 to 0.82; P < 0.05). By combining these most powerful features in an RSF model, the obtained C-index was 0.90. T 2 -w Haralick features appear to be strongly associated with biochemical recurrence following prostate cancer radiotherapy. 3 J. Magn. Reson. Imaging 2017;45:103-117. © 2016 International Society for Magnetic Resonance in Medicine.

Diagnosing pyogenic, brucella and tuberculous spondylitis using histopathology and MRI: A retrospective study.

PubMed

Li, Tao; Liu, Tao; Jiang, Zhensong; Cui, Xingang; Sun, Jianmin

2016-10-01

The present study examined the histopathological and magnetic resonance imaging (MRI) features of pyogenic, brucella and tuberculous spondylitis (PS, BS and TS, respectively). A total of 22 PS, 20 BS and 20 TS patients were included in the study. Histopathological examination was used to assess the lesion structure and composition, and the MRI observation identified the lesion location and signal features. The following histopathological and MRI features were identified significantly more in patients with PS than in patients with BS and TS: Predominant neutrophil infiltration, abnormal intervertebral disk signal, lesions on the ventral and lateral sides of the vertebral bodies, and thick and irregular abscess walls. The following histopathological and MRI features were identified significantly more in patients with BS than in patients with PS and TS: Predominant lymphocyte infiltration, new bone formation, epithelioid granuloma, lesions on the ventral sides of the vertebral bodies, no, or very mild, vertebral body deformation, no abnormal paraspinal soft tissue signal, no intraosseous or paraspinal abscesses, and thin and irregular abscess walls. The following histopathological and MRI features were identified significantly more in patients with TS than in patients with BS and PS: Sequestrum, Langerhans giant cells, caseous necrosis, lesions primarily in the thoracic region and on the lateral sides of the vertebral bodies, no obvious intervertebral disk damage, obvious vertebral body deformation, abnormal paraspinal soft tissue signal, intraosseous or paraspinal abscesses, and thin and smooth abscess walls. In conclusion, it can be suggested that these significant differences in histopathological and MRI features between the three different types of spondylitis may contribute towards the differential diagnosis of the diseases.

Longitudinal MRI assessment: the identification of relevant features in the development of Posterior Fossa Syndrome in children

NASA Astrophysics Data System (ADS)

Spiteri, M.; Lewis, E.; Windridge, D.; Avula, S.

2015-03-01

Up to 25% of children who undergo brain tumour resection surgery in the posterior fossa develop posterior fossa syndrome (PFS). This syndrome is characterised by mutism and disturbance in speech. Our hypothesis is that there is a correlation between PFS and the occurrence of hypertrophic olivary degeneration (HOD) in lobes within the posterior fossa, known as the inferior olivary nuclei (ION). HOD is exhibited as an increase in size and intensity of the ION on an MR image. Intra-operative MRI (IoMRI) is used during surgical procedures at the Alder Hey Children's Hospital, Liver- pool, England, in the treatment of Posterior Fossa tumours and allows visualisation of the brain during surgery. The final MR scan on the IoMRI allows early assessment of the ION immediately after the surgical procedure. The longitudinal MRI data of 28 patients was analysed in a collaborative study with Alder Hey Children's Hospital, in order to identify the most relevant imaging features that relate to the development of PFS, specifically related to HOD. A semi-automated segmentation process was carried out to delineate the ION on each MRI. Feature selection techniques were used to identify the most relevant features amongst the MRI data, demographics and clinical data provided by the hospital. A support vector machine (SVM) was used to analyse the discriminative ability of the selected features. The results indicate the presence of HOD as the most efficient feature that correlates with the development of PFS, followed by the change in intensity and size of the ION and whether HOD occurred bilaterally or unilaterally.

Skeletal muscle metastases on magnetic resonance imaging: analysis of 31 cases.

PubMed

Li, Qi; Wang, Lei; Pan, Shinong; Shu, Hong; Ma, Ying; Lu, Zaiming; Fu, Xihu; Jiang, Bo; Guo, Qiyong

2016-01-01

To investigate the magnetic resonance imaging (MRI) features of skeletal muscle metastases (SMM). The records of 31 patients with proven SMM were retrospectively reviewed. Clinical history, type of primary malignancy, location of metastases, and MRI features of SMM were evaluated. Based on MRI findings, SMM were divided into three MRI types. The correlation between MRI types with ages and pathology category, between MRI types of SMM and ages, as well as MRI types of SMM and pathology category were analysed with Spearman's rho. The most common primary tumour was genital tumour (25.8%) and bronchial carcinoma (19.4%), and the most common cell type was adenocarcinoma (58.1%). SMM were located in the iliopsoas muscle (26.3%), paravertebral muscles (21.1%), and upper extremity muscles (18.4%). MRI features: (1) Type-I localised lesions (12.90%), round-like mass limited to local regions with heterogeneous iso-signal intensity in T1WI and heterogeneous hyper-intensity in T2WI; (2) Type-II diffuse lesions without bone destruction (35.48%), abnormal diffuse swelling of the muscle with irregular boundaries and slightly hypo- to iso-intensity in T1WI and hyper-intensity in T2WI; and (3) Type-III diffuse lesions with bone destruction (51.61%), distinct irregular lump with iso-intensity in T1WI and heterogeneous hyper-intensity in T2WI with adjacent bone invasion. There was positive correlation between MRI types and ages (r = 0.431, p < 0.05). There were no significant differences of MRI types with pathology category (p > 0.05). SMM features on MRI can be broadly used to classify lesions, which is beneficial for SMM diagnosis.

Skeletal muscle metastases on magnetic resonance imaging: analysis of 31 cases

PubMed Central

Li, Qi; Wang, Lei; Shu, Hong; Ma, Ying; Lu, Zaiming; Fu, Xihu; Jiang, Bo; Guo, Qiyong

2016-01-01

Aim of the study To investigate the magnetic resonance imaging (MRI) features of skeletal muscle metastases (SMM). Material and methods The records of 31 patients with proven SMM were retrospectively reviewed. Clinical history, type of primary malignancy, location of metastases, and MRI features of SMM were evaluated. Based on MRI findings, SMM were divided into three MRI types. The correlation between MRI types with ages and pathology category, between MRI types of SMM and ages, as well as MRI types of SMM and pathology category were analysed with Spearman's rho. Results The most common primary tumour was genital tumour (25.8%) and bronchial carcinoma (19.4%), and the most common cell type was adenocarcinoma (58.1%). SMM were located in the iliopsoas muscle (26.3%), paravertebral muscles (21.1%), and upper extremity muscles (18.4%). MRI features: (1) Type-I localised lesions (12.90%), round-like mass limited to local regions with heterogeneous iso-signal intensity in T1WI and heterogeneous hyper-intensity in T2WI; (2) Type-II diffuse lesions without bone destruction (35.48%), abnormal diffuse swelling of the muscle with irregular boundaries and slightly hypo- to iso-intensity in T1WI and hyper-intensity in T2WI; and (3) Type-III diffuse lesions with bone destruction (51.61%), distinct irregular lump with iso-intensity in T1WI and heterogeneous hyper-intensity in T2WI with adjacent bone invasion. There was positive correlation between MRI types and ages (r = 0.431, p < 0.05). There were no significant differences of MRI types with pathology category (p > 0.05). Conclusions SMM features on MRI can be broadly used to classify lesions, which is beneficial for SMM diagnosis. PMID:27647989

TU-F-CAMPUS-J-02: Evaluation of Textural Feature Extraction for Radiotherapy Response Assessment of Early Stage Breast Cancer Patients Using Diffusion Weighted MRI and Dynamic Contrast Enhanced MRI

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

Xie, Y; Wang, C; Horton, J

Purpose: To investigate the feasibility of using classic textural feature extraction in radiotherapy response assessment, we studied a unique cohort of early stage breast cancer patients with paired pre - and post-radiation Diffusion Weighted MRI (DWI-MRI) and Dynamic Contrast Enhanced MRI (DCE-MRI). Methods: 15 female patients from our prospective phase I trial evaluating preoperative radiotherapy were included in this retrospective study. Each patient received a single-fraction radiation treatment, and DWI and DCE scans were conducted before and after the radiotherapy. DWI scans were acquired using a spin-echo EPI sequence with diffusion weighting factors of b = 0 and b =more » 500 mm{sup 2} /s, and the apparent diffusion coefficient (ADC) maps were calculated. DCE-MRI scans were acquired using a T{sub 1}-weighted 3D SPGR sequence with a temporal resolution of about 1 minute. The contrast agent (CA) was intravenously injected with a 0.1 mmol/kg bodyweight dose at 2 ml/s. Two parameters, volume transfer constant (K{sup trans} ) and k{sub ep} were analyzed using the two-compartment Tofts kinetic model. For DCE parametric maps and ADC maps, 33 textural features were generated from the clinical target volume (CTV) in a 3D fashion using the classic gray level co-occurrence matrix (GLCOM) and gray level run length matrix (GLRLM). Wilcoxon signed-rank test was used to determine the significance of each texture feature’s change after the radiotherapy. The significance was set to 0.05 with Bonferroni correction. Results: For ADC maps calculated from DWI-MRI, 24 out of 33 CTV features changed significantly after the radiotherapy. For DCE-MRI pharmacokinetic parameters, all 33 CTV features of K{sup trans} and 33 features of k{sub ep} changed significantly. Conclusion: Initial results indicate that those significantly changed classic texture features are sensitive to radiation-induced changes and can be used for assessment of radiotherapy response in breast cancer.« less

Predicting epidermal growth factor receptor gene amplification status in glioblastoma multiforme by quantitative enhancement and necrosis features deriving from conventional magnetic resonance imaging.

PubMed

Dong, Fei; Zeng, Qiang; Jiang, Biao; Yu, Xinfeng; Wang, Weiwei; Xu, Jingjing; Yu, Jinna; Li, Qian; Zhang, Minming

2018-05-01

To study whether some of the quantitative enhancement and necrosis features in preoperative conventional MRI (cMRI) had a predictive value for epidermal growth factor receptor (EGFR) gene amplification status in glioblastoma multiforme (GBM).Fifty-five patients with pathologically determined GBMs who underwent cMRI were retrospectively reviewed. The following cMRI features were quantitatively measured and recorded: long and short diameters of the enhanced portion (LDE and SDE), maximum and minimum thickness of the enhanced portion (MaxTE and MinTE), and long and short diameters of the necrotic portion (LDN and SDN). Univariate analysis of each feature and a decision tree model fed with all the features were performed. Area under the receiver operating characteristic (ROC) curve (AUC) was used to assess the performance of features, and predictive accuracy was used to assess the performance of the model.For single feature, MinTE showed the best performance in differentiating EGFR gene amplification negative (wild-type) (nEGFR) GBM from EGFR gene amplification positive (pEGFR) GBM, and it got an AUC of 0.68 with a cut-off value of 2.6 mm. The decision tree model included 2 features MinTE and SDN, and got an accuracy of 0.83 in validation dataset.Our results suggest that quantitative measurement of the features MinTE and SDN in preoperative cMRI had a high accuracy for predicting EGFR gene amplification status in GBM.

Uterine Fibroid Embolization for Symptomatic Fibroids: Study at a Teaching Hospital in Kenya

PubMed Central

Mutai, John Kiprop; Vinayak, Sudhir; Stones, William; Hacking, Nigel; Mariara, Charles

2015-01-01

Objective: Characterization of magnetic (MRI) features in women undergoing uterine fibroid embolization (UFE) and identification of clinical correlates in an African population. Materials and Methods: Patients with symptomatic fibroids who are selected to undergo UFE at the hospital formed the study population. The baseline MRI features, baseline symptom score, short-term imaging outcome, and mid-term symptom scores were analyzed for interval changes. Assessment of potential associations between short-term imaging features and mid-term symptom scores was also done. Results: UFE resulted in statistically significant reduction (P < 0.001) of dominant fibroid, uterine volumes, and reduction of symptom severity scores, which were 43.7%, 40.1%, and 37.8%, respectively. Also, 59% of respondents had more than 10 fibroids. The predominant location of the dominant fibroid was intramural. No statistically significant association was found between clinical and radiological outcome. Conclusion: The response of uterine fibroids to embolization in the African population is not different from the findings reported in other studies from the west. The presence of multiple and large fibroids in this study is consistent with the case mix described in other studies of African-American populations. Patient counseling should emphasize the independence of volume reduction and symptom improvement. Though volume changes are of relevance for the radiologist in understanding the evolution of the condition and identifying potential technical treatment failures, it should not be the main basis of evaluation of treatment success. PMID:25883858

Relationship between heart rate and quiescent interval of the cardiac cycle in children using MRI.

PubMed

Zhang, Wei; Bogale, Saivivek; Golriz, Farahnaz; Krishnamurthy, Rajesh

2017-11-01

Imaging the heart in children comes with the challenge of constant cardiac motion. A prospective electrocardiography-triggered CT scan allows for scanning during a predetermined phase of the cardiac cycle with least motion. This technique requires knowing the optimal quiescent intervals of cardiac cycles in a pediatric population. To evaluate high-temporal-resolution cine MRI of the heart in children to determine the relationship of heart rate to the optimal quiescent interval within the cardiac cycle. We included a total of 225 consecutive patients ages 0-18 years who had high-temporal-resolution cine steady-state free-precession sequence performed as part of a magnetic resonance imaging (MRI) or magnetic resonance angiography study of the heart. We determined the location and duration of the quiescent interval in systole and diastole for heart rates ranging 40-178 beats per minute (bpm). We performed the Wilcoxon signed rank test to compare the duration of quiescent interval in systole and diastole for each heart rate group. The duration of the quiescent interval at heart rates <80 bpm and >90 bpm was significantly longer in diastole and systole, respectively (P<.0001 for all ranges, except for 90-99 bpm [P=.02]). For heart rates 80-89 bpm, diastolic interval was longer than systolic interval, but the difference was not statistically significant (P=.06). We created a chart depicting optimal quiescent intervals across a range of heart rates that could be applied for prospective electrocardiography-triggered CT imaging of the heart. The optimal quiescent interval at heart rates <80 bpm is in diastole and at heart rates ≥90 bpm is in systole. The period of quiescence at heart rates 80-89 bpm is uniformly short in systole and diastole.

Magnetization-prepared rapid acquisition with gradient echo magnetic resonance imaging signal and texture features for the prediction of mild cognitive impairment to Alzheimer’s disease progression

PubMed Central

Martinez-Torteya, Antonio; Rodriguez-Rojas, Juan; Celaya-Padilla, José M.; Galván-Tejada, Jorge I.; Treviño, Victor; Tamez-Peña, Jose

2014-01-01

Abstract. Early diagnoses of Alzheimer’s disease (AD) would confer many benefits. Several biomarkers have been proposed to achieve such a task, where features extracted from magnetic resonance imaging (MRI) have played an important role. However, studies have focused exclusively on morphological characteristics. This study aims to determine whether features relating to the signal and texture of the image could predict mild cognitive impairment (MCI) to AD progression. Clinical, biological, and positron emission tomography information and MRI images of 62 subjects from the AD neuroimaging initiative were used in this study, extracting 4150 features from each MRI. Within this multimodal database, a feature selection algorithm was used to obtain an accurate and small logistic regression model, generated by a methodology that yielded a mean blind test accuracy of 0.79. This model included six features, five of them obtained from the MRI images, and one obtained from genotyping. A risk analysis divided the subjects into low-risk and high-risk groups according to a prognostic index. The groups were statistically different (p-value=2.04e−11). These results demonstrated that MRI features related to both signal and texture add MCI to AD predictive power, and supported the ongoing notion that multimodal biomarkers outperform single-modality ones. PMID:26158047

Noninvasive Assessment of Advanced Fibrosis Based on Hepatic Volume in Patients with Nonalcoholic Fatty Liver Disease.

PubMed

Hayashi, Tatsuya; Saitoh, Satoshi; Fukuzawa, Kei; Tsuji, Yoshinori; Takahashi, Junji; Kawamura, Yusuke; Akuta, Norio; Kobayashi, Masahiro; Ikeda, Kenji; Fujii, Takeshi; Miyati, Tosiaki; Kumada, Hiromitsu

2017-09-15

Noninvasive liver fibrosis evaluation was performed in patients with nonalcoholic fatty liver disease (NAFLD). We used a quantitative method based on the hepatic volume acquired from gadoxetate disodium-enhanced (Gd-EOB-DTPA-enhanced) magnetic resonance imaging (MRI) for diagnosing advanced fibrosis in patients with NAFLD. A total of 130 patients who were diagnosed with NAFLD and underwent Gd-EOB-DTPA-enhanced MRI were retrospectively included. Histological data were available for 118 patients. Hepatic volumetric parameters, including the left hepatic lobe to right hepatic lobe volume ratio (L/R ratio), were measured. The usefulness of the L/R ratio for diagnosing fibrosis ≥F3-4 and F4 was assessed using the area under the receiver operating characteristic (AUROC) curve. Multiple regression analysis was performed to identify variables (age, body mass index, serum fibrosis markers, and histological features) that were associated with the L/R ratio. The L/R ratio demonstrated good performance in differentiating advanced fibrosis (AUROC, 0.80; 95% confidence interval, 0.72 to 0.88) from cirrhosis (AUROC, 0.87; 95% confidence interval, 0.75 to 0.99). Multiple regression analysis showed that only fibrosis was significantly associated with the L/R ratio (coefficient, 0.121; p<0.0001). The L/R ratio, which is not influenced by pathological parameters other than fibrosis, is useful for diagnosing cirrhosis in patients with NAFLD.

A Space Affine Matching Approach to fMRI Time Series Analysis.

PubMed

Chen, Liang; Zhang, Weishi; Liu, Hongbo; Feng, Shigang; Chen, C L Philip; Wang, Huili

2016-07-01

For fMRI time series analysis, an important challenge is to overcome the potential delay between hemodynamic response signal and cognitive stimuli signal, namely the same frequency but different phase (SFDP) problem. In this paper, a novel space affine matching feature is presented by introducing the time domain and frequency domain features. The time domain feature is used to discern different stimuli, while the frequency domain feature to eliminate the delay. And then we propose a space affine matching (SAM) algorithm to match fMRI time series by our affine feature, in which a normal vector is estimated using gradient descent to explore the time series matching optimally. The experimental results illustrate that the SAM algorithm is insensitive to the delay between the hemodynamic response signal and the cognitive stimuli signal. Our approach significantly outperforms GLM method while there exists the delay. The approach can help us solve the SFDP problem in fMRI time series matching and thus of great promise to reveal brain dynamics.

Functional magnetic resonance imaging activation detection: fuzzy cluster analysis in wavelet and multiwavelet domains.

PubMed

Jahanian, Hesamoddin; Soltanian-Zadeh, Hamid; Hossein-Zadeh, Gholam-Ali

2005-09-01

To present novel feature spaces, based on multiscale decompositions obtained by scalar wavelet and multiwavelet transforms, to remedy problems associated with high dimension of functional magnetic resonance imaging (fMRI) time series (when they are used directly in clustering algorithms) and their poor signal-to-noise ratio (SNR) that limits accurate classification of fMRI time series according to their activation contents. Using randomization, the proposed method finds wavelet/multiwavelet coefficients that represent the activation content of fMRI time series and combines them to define new feature spaces. Using simulated and experimental fMRI data sets, the proposed feature spaces are compared to the cross-correlation (CC) feature space and their performances are evaluated. In these studies, the false positive detection rate is controlled using randomization. To compare different methods, several points of the receiver operating characteristics (ROC) curves, using simulated data, are estimated and compared. The proposed features suppress the effects of confounding signals and improve activation detection sensitivity. Experimental results show improved sensitivity and robustness of the proposed method compared to the conventional CC analysis. More accurate and sensitive activation detection can be achieved using the proposed feature spaces compared to CC feature space. Multiwavelet features show superior detection sensitivity compared to the scalar wavelet features. (c) 2005 Wiley-Liss, Inc.

Ovarian torsion: diagnostic features on CT and MRI with pathologic correlation.

PubMed

Duigenan, Shauna; Oliva, Esther; Lee, Susanna I

2012-02-01

The CT and MRI features of ovarian torsion are illustrated with gross pathologic correlation. Ovarian enlargement with or without an underlying mass is the finding most frequently associated with torsion, but it is nonspecific. A twisted pedicle, although not often detected on imaging, is pathognomonic when seen. Subacute ovarian hemorrhage and abnormal enhancement is usually seen, and both features show characteristic patterns on CT and MRI. Ipsilateral uterine deviation can also be seen. Diagnostic pitfalls that may mimic ovarian torsion and observations for discriminating them are discussed.

Neuroimaging basis in the conversion of aMCI patients with APOE-ε4 to AD: study protocol of a prospective diagnostic trial.

PubMed

Chen, Guan-Qun; Sheng, Can; Li, Yu-Xia; Yu, Yang; Wang, Xiao-Ni; Sun, Yu; Li, Hong-Yan; Li, Xuan-Yu; Xie, Yun-Yan; Han, Ying

2016-05-12

The ε4 allele of the Apolipoprotein E gene (APOE-ε4) is a potent genetic risk factor for sporadic Alzheimer's disease (AD). Amnestic mild cognitive impairment (aMCI) is an intermediate state between normal cognitive aging and dementia, which is easy to convert to AD dementia. It is an urgent problem in the field of cognitive neuroscience to reveal the conversion of aMCI-ε4 to AD. Based on our preliminary work, we will study the neuroimaging features in the special group of aMCI-ε4 with multi-modality magnetic resonance imaging (structural MRI, resting state-fMRI and diffusion tensor imaging) longitudinally. In this study, 200 right-handed subjects who are diagnosed as aMCI with APOE-ε4 will be recruited at the memory clinic of the Neurology Department, XuanWu Hospital, Capital Medical University, Beijing, China. All subjects will undergo the neuroimaging and neuropsychological evaluation at a 1 year-interval for 3 years. The primary outcome measures are 1) Microstructural alterations revealed with multimodal MRI scans including structure MRI (sMRI), resting state functional MRI (rs-fMRI), diffusion tensor imaging (DTI); 2) neuropsychological evaluation, including the World Health Organization-University of California-LosAngeles Auditory Verbal Learning Test (WHO-UCLA AVLT), Addenbrook's cognitive examination-revised (ACE-R), mini-mental state examination (MMSE), Montreal Cognitive Assessment (MoCA), Clinical Dementia Rating scale (CDR). This study is to find out the neuroimaging biomarker and the changing laws of the marker during the progress of aMCI-ε4 to AD, and the final purpose is to provide scientific evidence for new prevention, diagnosis and treatment of AD. This study has been registered to ClinicalTrials.gov (NCT02225964, https://www.clinicaltrials.gov/ ) in August 24, 2014.

Computer extracted texture features on T2w MRI to predict biochemical recurrence following radiation therapy for prostate cancer

NASA Astrophysics Data System (ADS)

Ginsburg, Shoshana B.; Rusu, Mirabela; Kurhanewicz, John; Madabhushi, Anant

2014-03-01

In this study we explore the ability of a novel machine learning approach, in conjunction with computer-extracted features describing prostate cancer morphology on pre-treatment MRI, to predict whether a patient will develop biochemical recurrence within ten years of radiation therapy. Biochemical recurrence, which is characterized by a rise in serum prostate-specific antigen (PSA) of at least 2 ng/mL above the nadir PSA, is associated with increased risk of metastasis and prostate cancer-related mortality. Currently, risk of biochemical recurrence is predicted by the Kattan nomogram, which incorporates several clinical factors to predict the probability of recurrence-free survival following radiation therapy (but has limited prediction accuracy). Semantic attributes on T2w MRI, such as the presence of extracapsular extension and seminal vesicle invasion and surrogate measure- ments of tumor size, have also been shown to be predictive of biochemical recurrence risk. While the correlation between biochemical recurrence and factors like tumor stage, Gleason grade, and extracapsular spread are well- documented, it is less clear how to predict biochemical recurrence in the absence of extracapsular spread and for small tumors fully contained in the capsule. Computer{extracted texture features, which quantitatively de- scribe tumor micro-architecture and morphology on MRI, have been shown to provide clues about a tumor's aggressiveness. However, while computer{extracted features have been employed for predicting cancer presence and grade, they have not been evaluated in the context of predicting risk of biochemical recurrence. This work seeks to evaluate the role of computer-extracted texture features in predicting risk of biochemical recurrence on a cohort of sixteen patients who underwent pre{treatment 1.5 Tesla (T) T2w MRI. We extract a combination of first-order statistical, gradient, co-occurrence, and Gabor wavelet features from T2w MRI. To identify which of these T2w MRI texture features are potential independent prognostic markers of PSA failure, we implement a partial least squares (PLS) method to embed the data in a low{dimensional space and then use the variable importance in projections (VIP) method to quantify the contributions of individual features to classification on the PLS embedding. In spite of the poor resolution of the 1.5 T MRI data, we are able to identify three Gabor wavelet features that, in conjunction with a logistic regression classifier, yield an area under the receiver operating characteristic curve of 0.83 for predicting the probability of biochemical recurrence following radiation therapy. In comparison to both the Kattan nomogram and semantic MRI attributes, the ability of these three computer-extracted features to predict biochemical recurrence risk is demonstrated.

MR image features predicting hemorrhagic transformation in acute cerebral infarction: a multimodal study.

PubMed

Liu, Chunming; Dong, Zhengchao; Xu, Liang; Khursheed, Aiman; Dong, Longchun; Liu, Zhenxing; Yang, Jun; Liu, Jun

2015-11-01

The aims of this study were to observe magnetic resonance imaging (MRI) features and the frequency of hemorrhagic transformation (HT) in patients with acute cerebral infarction and to identify the risk factors of HT. We first performed multimodal MRI (anatomical, diffusion weighted, and susceptibility weighted) scans on 87 patients with acute cerebral infarction within 24 hours after symptom onset and documented the image findings. We then performed follow-up examinations 3 days to 2 weeks after the onset or whenever the conditions of the patients worsened within 3 days. We utilized univariate statistics to identify the correlations between HT and image features and used multivariate logistical regression to correct for confounding factors to determine relevant independent image features of HT. HT was observed in 17 out of total 87 patients (19.5 %). The infarct size (p = 0.021), cerebral microbleeds (CMBs) (p = 0.004), relative apparent diffusion (rADC) (p = 0.023), and venous anomalies (p = 0.000) were significantly related with HT in the univariate statistics. Multivariate analysis demonstrated that CMBs (odd ratio (OR) = 0.082; 95 % confidence interval (CI) = 0.011-0.597; p = 0.014), rADC (OR = 0.000; 95 % CI = 0.000-0.692; p = 0.041), and venous anomalies (OR = 0.066; 95 % CI = 0.011-0.403; p = 0.003) were independent risk factors for HT. The frequency of HT is 19.5 % in this study. CMBs, rADC, and venous anomalies are independent risk factors for HT of acute cerebral infarction.

Individual MRI and radiographic features of knee OA in subjects with unilateral knee pain: Health ABC study

PubMed Central

Javaid, MK; Kiran, A; Guermazi, A; Kwoh, K; Zaim, S; Carbone, L; Harris, T.; McCulloch, C.E.; Arden, NK; Lane, NE; Felson, D; Nevitt, M

2012-01-01

Strong associations between radiographic features of knee OA and pain have been demonstrated in persons with unilateral knee symptoms. Our objectives were to compare radiographic with MRI features of knee OA and assess the discrimination between painful and non-painful knees in persons with unilateral symptoms. 283 individuals with unilateral knee pain aged 71 to 80 years from Health ABC, a study of weight-related diseases and mobility, had bilateral knee radiographs, read for KL grade and individual radiographic features, and 1.5T MRIs, read using WORMS. The association of structural features with pain was assessed using a within-person case/control design and conditional logistic regression. Receiver operator characteristics (ROC) were then used to test the discriminatory performance of structural features. In conditional logistic analyses, knee pain was significantly associated with both radiographic (any JSN grade >=1: OR 3.20 (1.79 – 5.71) and MRI (any cartilage defect:>=2: OR 3.67 (1.49 – 9.04)) features. However, most subjects had MR detected osteophytes, cartilage and bone marrow lesions in both knees and no individual structural feature discriminated well between painful and non-painful knees using ROC. The best performing MRI feature (synovitis/effusion) was not significantly more informative than KL grade >=2 (p=0.42). In persons with unilateral knee pain, MR and radiographic features were associated with knee pain confirming an important role in the etiology of pain. However, no single MRI or radiographic finding performed well in discriminating painful from non-painful knees. Further work is needed to examine how structural and non-structural factors influence knee pain. PMID:22736267

Ultrafast dynamic contrast-enhanced mri of the breast using compressed sensing: breast cancer diagnosis based on separate visualization of breast arteries and veins.

PubMed

Onishi, Natsuko; Kataoka, Masako; Kanao, Shotaro; Sagawa, Hajime; Iima, Mami; Nickel, Marcel Dominik; Toi, Masakazu; Togashi, Kaori

2018-01-01

To evaluate the feasibility of ultrafast dynamic contrast-enhanced (UF-DCE) magnetic resonance imaging (MRI) with compressed sensing (CS) for the separate identification of breast arteries/veins and perform temporal evaluations of breast arteries and veins with a focus on the association with ipsilateral cancers. Our Institutional Review Board approved this study with retrospective design. Twenty-five female patients who underwent UF-DCE MRI at 3T were included. UF-DCE MRI consisting of 20 continuous frames was acquired using a prototype 3D gradient-echo volumetric interpolated breath-hold sequence including a CS reconstruction: temporal resolution, 3.65 sec/frame; spatial resolution, 0.9 × 1.3 × 2.5 mm. Two readers analyzed 19 maximum intensity projection images reconstructed from subtracted images, separately identified breast arteries/veins and the earliest frame in which they were respectively visualized, and calculated the time interval between arterial and venous visualization (A-V interval) for each breast. In total, 49 breasts including 31 lesions (breast cancer, 16; benign lesion, 15) were identified. In 39 of the 49 breasts (breasts with cancers, 16; breasts with benign lesions, 10; breasts with no lesions, 13), both breast arteries and veins were separately identified. The A-V intervals for breasts with cancers were significantly shorter than those for breasts with benign lesions (P = 0.043) and no lesions (P = 0.007). UF-DCE MRI using CS enables the separate identification of breast arteries/veins. Temporal evaluations calculating the time interval between arterial and venous visualization might be helpful in the differentiation of ipsilateral breast cancers from benign lesions. 3 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2018;47:97-104. © 2017 International Society for Magnetic Resonance in Medicine.

Dynamic MRI for distinguishing high-flow from low-flow peripheral vascular malformations.

PubMed

Ohgiya, Yoshimitsu; Hashimoto, Toshi; Gokan, Takehiko; Watanabe, Shouji; Kuroda, Masayoshi; Hirose, Masanori; Matsui, Seishi; Nobusawa, Hiroshi; Kitanosono, Takashi; Munechika, Hirotsugu

2005-11-01

The purpose of our study was to assess the usefulness of dynamic MRI in distinguishing high-flow vascular malformations from low-flow vascular malformations, which do not need angiography for treatment. Between September 2001 and January 2003, 16 patients who underwent conventional and dynamic MRI had peripheral vascular malformations (six high- and 10 low-flow). The temporal resolution of dynamic MRI was 5 sec. Time intervals between beginning of enhancement of an arterial branch in the vicinity of a lesion in the same slice and the onset of enhancement in the lesion were calculated. We defined these time intervals as "artery-lesion enhancement time." Time intervals between the onset of enhancement in the lesion and the time of the maximal percentage of enhancement above baseline of the lesion within 120 sec were measured. We defined these time intervals as "contrast rise time" of the lesion. Diagnosis of the peripheral vascular malformations was based on angiographic or venographic findings. The mean artery-lesion enhancement time of the high-flow vascular malformations (3.3 sec [range, 0-5 sec]) was significantly shorter than that of the low-flow vascular malformations (8.8 sec [range, 0-20 sec]) (Mann-Whitney test, p < 0.05). The mean maximal lesion enhancement time of the high-flow vascular malformations (5.8 sec [range, 5-10 sec]) was significantly shorter than that of the low-flow vascular malformations (88.4 sec [range, 50-100 sec]) (Mann-Whitney test, p < 0.01). Dynamic MRI is useful for distinguishing high-flow from low-flow vascular malformations, especially when the contrast rise time of the lesion is measured.

An empirical comparison of SPM preprocessing parameters to the analysis of fMRI data.

PubMed

Della-Maggiore, Valeria; Chau, Wilkin; Peres-Neto, Pedro R; McIntosh, Anthony R

2002-09-01

We present the results from two sets of Monte Carlo simulations aimed at evaluating the robustness of some preprocessing parameters of SPM99 for the analysis of functional magnetic resonance imaging (fMRI). Statistical robustness was estimated by implementing parametric and nonparametric simulation approaches based on the images obtained from an event-related fMRI experiment. Simulated datasets were tested for combinations of the following parameters: basis function, global scaling, low-pass filter, high-pass filter and autoregressive modeling of serial autocorrelation. Based on single-subject SPM analysis, we derived the following conclusions that may serve as a guide for initial analysis of fMRI data using SPM99: (1) The canonical hemodynamic response function is a more reliable basis function to model the fMRI time series than HRF with time derivative. (2) Global scaling should be avoided since it may significantly decrease the power depending on the experimental design. (3) The use of a high-pass filter may be beneficial for event-related designs with fixed interstimulus intervals. (4) When dealing with fMRI time series with short interstimulus intervals (<8 s), the use of first-order autoregressive model is recommended over a low-pass filter (HRF) because it reduces the risk of inferential bias while providing a relatively good power. For datasets with interstimulus intervals longer than 8 seconds, temporal smoothing is not recommended since it decreases power. While the generalizability of our results may be limited, the methods we employed can be easily implemented by other scientists to determine the best parameter combination to analyze their data.

SU-E-J-264: Using Magnetic Resonance Imaging-Derived Features to Quantify Radiotherapy-Induced Normal Tissue Morbidity

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

Thor, M; Tyagi, N; Deasy, J

2015-06-15

Purpose: The aim of this study was to explore the use of Magnetic Resonance Imaging (MRI)-derived features as indicators of Radiotherapy (RT)-induced normal tissue morbidity. We also investigate the relationship between these features and RT dose in four critical structures. Methods: We demonstrate our approach for four patients treated with RT for base of tongue cancer in 2005–2007. For each patient, two MRI scans (T1-weighted pre (T1pre) and post (T1post) gadolinium contrast-enhancement) were acquired within the first six months after RT. The assessed morbidity endpoint observed in 2/4 patients was Grade 2+ CTCAEv.3 trismus. Four ipsilateral masticatory-related structures (masseter, lateralmore » and medial pterygoid, and the temporal muscles) were delineated on both T1pre and T1post and these scans were co-registered to the treatment planning CT using a deformable demons algorithm. For each structure, the maximum and mean RT dose, and six MRI-derived features (the second order texture features entropy and homogeneity, and the first order mean, median, kurtosis, and skewness) were extracted and compared structure-wise between patients with and without trismus. All MRI-derived features were calculated as the difference between T1pre and T1post, ΔS. Results: For 5/6 features and all structures, ΔS diverged between trismus and non-trismus patients particularly for the masseter, lateral pterygoid, and temporal muscles using the kurtosis feature (−0.2 vs. 6.4 for lateral pterygoid). Both the maximum and mean RT dose in all four muscles were higher amongst the trismus patients (with the maximum dose being up to 25 Gy higher). Conclusion: Using MRI-derived features to quantify RT-induced normal tissue complications is feasible. We showed that several features are different between patients with and without morbidity and that the RT dose in all investigated structures are higher amongst patients with morbidity. MRI-derived features, therefore, has the potential to improve predictions of normal tissue morbidity.« less

CT versus MR Techniques in the Detection of Cervical Artery Dissection.

PubMed

Hanning, Uta; Sporns, Peter B; Schmiedel, Meilin; Ringelstein, Erich B; Heindel, Walter; Wiendl, Heinz; Niederstadt, Thomas; Dittrich, Ralf

2017-11-01

Spontaneous cervical artery dissection (sCAD) is an important etiology of juvenile stroke. The gold standard for the diagnosis of sCAD is convential angiography. However, magnetic resonance imaging (MRI)/MR angiography (MRA) and computed tomography (CT)/CT angiography (CTA) are frequently used alternatives. New developments such as multislice CT/CTA have enabled routine acquisition of thinner sections with rapid imaging times. The goal of this study was to compare the capability of recent developed 128-slice CT/CTA to MRI/MRA to detect radiologic features of sCAD. Retrospective review of patients with suspected sCAD (n = 188) in a database of our Stroke center (2008-2014), who underwent CT/CTA and MRI/MRA on initial clinical work-up. A control group of 26 patients was added. All Images were evaluated concerning specific and sensitive radiological features for dissection by two experienced neuroradiologists. Imaging features were compared between the two modalities. Forty patients with 43 dissected arteries received both modalities (29 internal carotid arteries [ICAs] and 14 vertebral arteries [VAs]). All CADs were identified in CT/CTA and MRI/MRA. The features intimal flap, stenosis, and lumen irregularity appeared in both modalities. One high-grade stenosis was identified by CT/CTA that was expected occluded on MRI/MRA. Two MRI/MRA-confirmed pseudoaneurysms were missed by CT/CTA. None of the controls evidenced specific imaging signs for dissection. CT/CTA is a reliable and better available alternative to MRI/MRA for diagnosis of sCAD. CT/CTA should be used to complement MRI/MRA in cases where MRI/MRA suggests occlusion. Copyright © 2017 by the American Society of Neuroimaging.

Association between mammogram density and background parenchymal enhancement of breast MRI

NASA Astrophysics Data System (ADS)

Aghaei, Faranak; Danala, Gopichandh; Wang, Yunzhi; Zarafshani, Ali; Qian, Wei; Liu, Hong; Zheng, Bin

2018-02-01

Breast density has been widely considered as an important risk factor for breast cancer. The purpose of this study is to examine the association between mammogram density results and background parenchymal enhancement (BPE) of breast MRI. A dataset involving breast MR images was acquired from 65 high-risk women. Based on mammography density (BIRADS) results, the dataset was divided into two groups of low and high breast density cases. The Low-Density group has 15 cases with mammographic density (BIRADS 1 and 2), while the High-density group includes 50 cases, which were rated by radiologists as mammographic density BIRADS 3 and 4. A computer-aided detection (CAD) scheme was applied to segment and register breast regions depicted on sequential images of breast MRI scans. CAD scheme computed 20 global BPE features from the entire two breast regions, separately from the left and right breast region, as well as from the bilateral difference between left and right breast regions. An image feature selection method namely, CFS method, was applied to remove the most redundant features and select optimal features from the initial feature pool. Then, a logistic regression classifier was built using the optimal features to predict the mammogram density from the BPE features. Using a leave-one-case-out validation method, the classifier yields the accuracy of 82% and area under ROC curve, AUC=0.81+/-0.09. Also, the box-plot based analysis shows a negative association between mammogram density results and BPE features in the MRI images. This study demonstrated a negative association between mammogram density and BPE of breast MRI images.

Paediatric acquired demyelinating syndromes: incidence, clinical and magnetic resonance imaging features.

PubMed

Absoud, Michael; Lim, Ming J; Chong, Wui K; De Goede, Christian G; Foster, Katharine; Gunny, Roxana; Hemingway, Cheryl; Jardine, Philip E; Kneen, Rachel; Likeman, Marcus; Nischal, Ken K; Pike, Michael G; Sibtain, Naomi A; Whitehouse, William P; Cummins, Carole; Wassmer, Evangeline

2013-01-01

Changing trends in multiple sclerosis (MS) epidemiology may first be apparent in the childhood population affected with first onset acquired demyelinating syndromes (ADSs). We aimed to determine the incidence, clinical, investigative and magnetic resonance imaging (MRI) features of childhood central nervous system ADSs in the British Isles for the first time. We conducted a population active surveillance study. All paediatricians, and ophthalmologists (n = 4095) were sent monthly reporting cards (September 2009-September 2010). International Paediatric MS Study Group 2007 definitions and McDonald 2010 MS imaging criteria were used for acute disseminated encephalomyelitis (ADEM), clinically isolated syndrome (CIS) and neuromyelitis optica (NMO). Clinicians completed a standard questionnaire and provided an MRI copy for review. Card return rates were 90%, with information available for 200/222 positive notifications (90%). After exclusion of cases, 125 remained (age range 1.3-15.9), with CIS in 66.4%, ADEM in 32.0% and NMO in 1.6%. The female-to-male ratio in children older than 10 years (n = 63) was 1.52:1 (p = 0.045). The incidence of first onset ADS in children aged 1-15 years old was 9.83 per million children per year (95% confidence interval [CI] 8.18-11.71). A trend towards higher incidence rates of ADS in children of South Asian and Black ethnicity was observed compared with White children. Importantly, a number of MRI characteristics distinguished ADEM from CIS cases. Of CIS cases with contrast imaging, 26% fulfilled McDonald 2010 MS diagnostic criteria. We report the highest surveillance incidence rates of childhood ADS. Paediatric MS diagnosis at first ADS presentation has implications for clinical practice and clinical trial design.

Custom fit 3D-printed brain holders for comparison of histology with MRI in marmosets.

PubMed

Guy, Joseph R; Sati, Pascal; Leibovitch, Emily; Jacobson, Steven; Silva, Afonso C; Reich, Daniel S

2016-01-15

MRI has the advantage of sampling large areas of tissue and locating areas of interest in 3D space in both living and ex vivo systems, whereas histology has the ability to examine thin slices of ex vivo tissue with high detail and specificity. Although both are valuable tools, it is currently difficult to make high-precision comparisons between MRI and histology due to large differences inherent to the techniques. A method combining the advantages would be an asset to understanding the pathological correlates of MRI. 3D-printed brain holders were used to maintain marmoset brains in the same orientation during acquisition of ex vivo MRI and pathologic cutting of the tissue. The results of maintaining this same orientation show that sub-millimeter, discrete neuropathological features in marmoset brain consistently share size, shape, and location between histology and ex vivo MRI, which facilitates comparison with serial imaging acquired in vivo. Existing methods use computational approaches sensitive to data input in order to warp histologic images to match large-scale features on MRI, but the new method requires no warping of images, due to a preregistration accomplished in the technique, and is insensitive to data formatting and artifacts in both MRI and histology. The simple method of using 3D-printed brain holders to match brain orientation during pathologic sectioning and MRI acquisition enables rapid and precise comparison of small features seen on MRI to their underlying histology. Published by Elsevier B.V.

Juvenile Osteochondritis Dissecans: Correlation Between Histopathology and MRI.

PubMed

Zbojniewicz, Andrew M; Stringer, Keith F; Laor, Tal; Wall, Eric J

2015-07-01

The objective of our study was to correlate specimens of juvenile osteochondritis dissecans (OCD) lesions of the knee to MRI examinations to elucidate the histopathologic basis of characteristic imaging features. Five children (three boys and two girls; age range, 12-13 years old) who underwent transarticular biopsy of juvenile OCD lesions of the knee were retrospectively included in this study. Two radiologists reviewed the MRI examinations and a pathologist reviewed the histopathologic specimens and recorded characteristic features. Digital specimen photographs were calibrated to the size of the respective MR image with the use of a reference scale. Photographs were rendered semitransparent and over-laid onto the MR image with the location chosen on the basis of the site of the prior biopsy. A total of seven biopsy specimens were included. On MRI, all lesions showed cystlike foci in the subchondral bone, bone marrow edema pattern on proton density-or T2-weighted images, and relatively thick unossified epiphyseal cartilage. In four patients, a laminar signal intensity pattern was seen, and two patients had multiple breaks in the subchondral bone plate. Fibrovascular tissue was found at histopathology in all patients. Cleft spaces near the cartilage-bone interface and were seen in all patients while chondrocyte cloning was present in most cases. Focal bone necrosis and inflammation were infrequent MRI findings. Precise correlation of the MRI appearance to the histopathologic overlays consistently was found. A direct correlation exists between the histopathologic findings and the MRI features in patients with juvenile OCD. Additional studies are needed to correlate these MRI features with juvenile OCD healing success rates.

Folded concave penalized learning in identifying multimodal MRI marker for Parkinson’s disease

PubMed Central

Liu, Hongcheng; Du, Guangwei; Zhang, Lijun; Lewis, Mechelle M.; Wang, Xue; Yao, Tao; Li, Runze; Huang, Xuemei

2016-01-01

Background Brain MRI holds promise to gauge different aspects of Parkinson’s disease (PD)-related pathological changes. Its analysis, however, is hindered by the high-dimensional nature of the data. New method This study introduces folded concave penalized (FCP) sparse logistic regression to identify biomarkers for PD from a large number of potential factors. The proposed statistical procedures target the challenges of high-dimensionality with limited data samples acquired. The maximization problem associated with the sparse logistic regression model is solved by local linear approximation. The proposed procedures then are applied to the empirical analysis of multimodal MRI data. Results From 45 features, the proposed approach identified 15 MRI markers and the UPSIT, which are known to be clinically relevant to PD. By combining the MRI and clinical markers, we can enhance substantially the specificity and sensitivity of the model, as indicated by the ROC curves. Comparison to existing methods We compare the folded concave penalized learning scheme with both the Lasso penalized scheme and the principle component analysis-based feature selection (PCA) in the Parkinson’s biomarker identification problem that takes into account both the clinical features and MRI markers. The folded concave penalty method demonstrates a substantially better clinical potential than both the Lasso and PCA in terms of specificity and sensitivity. Conclusions For the first time, we applied the FCP learning method to MRI biomarker discovery in PD. The proposed approach successfully identified MRI markers that are clinically relevant. Combining these biomarkers with clinical features can substantially enhance performance. PMID:27102045

Accelerated Return to Sport After Anterior Cruciate Ligament Reconstruction and Early Knee Osteoarthritis Features at 1 Year: An Exploratory Study.

PubMed

Culvenor, Adam G; Patterson, Brooke E; Guermazi, Ali; Morris, Hayden G; Whitehead, Timothy S; Crossley, Kay M

2018-04-01

A timely return to competitive sport is a primary goal of anterior cruciate ligament reconstruction (ACLR). It is not known whether an accelerated return to sport increases the risk of early-onset knee osteoarthritis (KOA). To determine whether an accelerated return to sport post-ACLR (ie, <10 months) is associated with increased odds of early KOA features on magnetic resonance imaging (MRI) 1 year after surgery and to evaluate the relationship between an accelerated return to sport and early KOA features stratified by type of ACL injury (isolated or concurrent chondral/meniscal injury) and lower limb function (good or poor). Cross-sectional study. Private radiology clinic and university laboratory. A total of 111 participants (71 male; mean age 30 ± 8 years) 1-year post-ACLR. Participants completed a self-report questionnaire regarding postoperative return-to-sport data (specific sport, postoperative month first returned), and isotropic 3-T MRI scans were obtained. Early KOA features (bone marrow, cartilage and meniscal lesions, and osteophytes) assessed with the MRI OA Knee Score. Logistic regression analyses evaluated the odds of early KOA features with an accelerated return to sport (<10 months post-ACLR versus ≥10 months or no return to sport) in the total cohort and stratified by type of ACL injury and lower limb function. Forty-six (41%) participants returned to competitive sport <10 months post-ACLR. An early return to sport was associated with significantly increased odds of bone marrow lesions (odds ratio [OR] 2.7, 95% confidence interval [CI] 1.3-6.0) but not cartilage (OR 1.2, 95% CI 0.5-2.6) or meniscal lesions (OR 0.8, 95% CI 0.4-1.8) or osteophytes (OR 0.6, 95% CI 0.3-1.4). In those with poor lower limb function, early return to sport exacerbated the odds of bone marrow lesions (OR 4.6, 95% CI 1.6-13.5), whereas stratified analyses for type of ACL injury did not reach statistical significance. An accelerated return to sport, particularly in the presence of poor lower limb function, may be implicated in posttraumatic KOA development. IV. Copyright © 2018 American Academy of Physical Medicine and Rehabilitation. Published by Elsevier Inc. All rights reserved.

Identifying MRI markers to evaluate early treatment-related changes post-laser ablation for cancer pain management

NASA Astrophysics Data System (ADS)

Tiwari, Pallavi; Danish, Shabbar; Madabhushi, Anant

2014-03-01

Laser interstitial thermal therapy (LITT) has recently emerged as a new treatment modality for cancer pain management that targets the cingulum (pain center in the brain), and has shown promise over radio-frequency (RF) based ablation which is reported to provide temporary relief. One of the major advantages enjoyed by LITT is its compatibility with magnetic resonance imaging (MRI), allowing for high resolution in vivo imaging to be used in LITT procedures. Since laser ablation for pain management is currently exploratory and is only performed at a few centers worldwide, its short-, and long-term effects on the cingulum are currently unknown. Traditionally treatment effects are evaluated by monitoring changes in volume of the ablation zone post-treatment. However, this is sub-optimal since it involves evaluating a single global parameter (volume) to detect changes pre-, and post-MRI. Additionally, the qualitative observations of LITT-related changes on multi-parametric MRI (MPMRI) do not specifically address differentiation between the appearance of treatment related changes (edema, necrosis) from recurrence of the disease (pain recurrence). In this work, we explore the utility of computer extracted texture descriptors on MP-MRI to capture early treatment related changes on a per-voxel basis by extracting quantitative relationships that may allow for an in-depth understanding of tissue response to LITT on MRI, subtle changes that may not be appreciable on original MR intensities. The second objective of this work is to investigate the efficacy of different MRI protocols in accurately capturing treatment related changes within and outside the ablation zone post-LITT. A retrospective cohort of studies comprising pre- and 24-hour post-LITT 3 Tesla T1-weighted (T1w), T2w, T2-GRE, and T2-FLAIR acquisitions was considered. Our scheme involved (1) inter-protocol as well as inter-acquisition affine registration of pre- and post-LITT MRI, (2) quantitation of MRI parameters by correcting for intensity drift in order to examine tissue-specific response, and (3) quantification of MRI maps via texture and intensity features to evaluate changes in MR markers pre- and post-LITT. A total of 78 texture features comprising of non-steerable and steerable gradient and second order statistical features were extracted from pre- and post-LITT MP-MRI on a per-voxel basis. Quantitative, voxel-wise comparison of the changes in MRI texture features between pre-, and post-LITT MRI indicate that (a) steerable and non-steerable gradient texture features were highly sensitive as well as specific in predicting subtle micro-architectural changes within and around the ablation zone pre- and post-LITT, (b) FLAIR was identified as the most sensitive MRI protocol in identifying early treatment changes yielding a normalized percentage change of 360% within the ablation zone relative to its pre-LITT value, and (c) GRE was identified as the most sensitive MRI protocol in quantifying changes outside the ablation zone post-LITT. Our preliminary results thus indicate great potential for non-invasive computerized MRI features in determining localized micro-architectural focal treatment related changes post-LITT.

MRI differentiation of low-grade from high-grade appendicular chondrosarcoma.

PubMed

Douis, Hassan; Singh, Leanne; Saifuddin, Asif

2014-01-01

To identify magnetic resonance imaging (MRI) features which differentiate low-grade chondral lesions (atypical cartilaginous tumours/grade 1 chondrosarcoma) from high-grade chondrosarcomas (grade 2, grade 3 and dedifferentiated chondrosarcoma) of the major long bones. We identified all patients treated for central atypical cartilaginous tumours and central chondrosarcoma of major long bones (humerus, femur, tibia) over a 13-year period. The MRI studies were assessed for the following features: bone marrow oedema, soft tissue oedema, bone expansion, cortical thickening, cortical destruction, active periostitis, soft tissue mass and tumour length. The MRI-features were compared with the histopathological tumour grading using univariate, multivariate logistic regression and receiver operating characteristic curve (ROC) analyses. One hundred and seventy-nine tumours were included in this retrospective study. There were 28 atypical cartilaginous tumours, 79 grade 1 chondrosarcomas, 36 grade 2 chondrosarcomas, 13 grade 3 chondrosarcomas and 23 dedifferentiated chondrosarcomas. Multivariate analysis demonstrated that bone expansion (P = 0.001), active periostitis (P = 0.001), soft tissue mass (P < 0.001) and tumour length (P < 0.001) were statistically significant differentiating factors between low-grade and high-grade chondral lesions with an area under the ROC curve of 0.956. On MRI, bone expansion, active periostitis, soft tissue mass and tumour length can reliably differentiate high-grade chondrosarcomas from low-grade chondral lesions of the major long bones. • Accurate differentiation of low-grade from high-grade chondrosarcomas is essential before surgery • MRI can reliably differentiate high-grade from low-grade chondrosarcomas of long bone • Differentiating features are bone expansion, periostitis, soft tissue mass and tumour length • Presence of these four MRI features demonstrated a diagnostic accuracy (AUC) of 95.6 % • The findings may result in more accurate diagnosis before definitive surgery.

Groupwise registration of cardiac perfusion MRI sequences using normalized mutual information in high dimension

NASA Astrophysics Data System (ADS)

Hamrouni, Sameh; Rougon, Nicolas; Pr"teux, Françoise

2011-03-01

In perfusion MRI (p-MRI) exams, short-axis (SA) image sequences are captured at multiple slice levels along the long-axis of the heart during the transit of a vascular contrast agent (Gd-DTPA) through the cardiac chambers and muscle. Compensating cardio-thoracic motions is a requirement for enabling computer-aided quantitative assessment of myocardial ischaemia from contrast-enhanced p-MRI sequences. The classical paradigm consists of registering each sequence frame on a reference image using some intensity-based matching criterion. In this paper, we introduce a novel unsupervised method for the spatio-temporal groupwise registration of cardiac p-MRI exams based on normalized mutual information (NMI) between high-dimensional feature distributions. Here, local contrast enhancement curves are used as a dense set of spatio-temporal features, and statistically matched through variational optimization to a target feature distribution derived from a registered reference template. The hard issue of probability density estimation in high-dimensional state spaces is bypassed by using consistent geometric entropy estimators, allowing NMI to be computed directly from feature samples. Specifically, a computationally efficient kth-nearest neighbor (kNN) estimation framework is retained, leading to closed-form expressions for the gradient flow of NMI over finite- and infinite-dimensional motion spaces. This approach is applied to the groupwise alignment of cardiac p-MRI exams using a free-form Deformation (FFD) model for cardio-thoracic motions. Experiments on simulated and natural datasets suggest its accuracy and robustness for registering p-MRI exams comprising more than 30 frames.

[Clinical features and magnetic resonance imaging evaluation of encephalopathy in high-risk late preterm infants].

PubMed

Zhu, Yan; Zhang, Ke; Hu, Lan; Xiao, Mi-Li; Li, Zhi-Hua; Chen, Chao

2017-05-01

To investigate the risk factors, clinical features, and magnetic resonance imaging (MRI) changes of encephalopathy in high-risk late preterm infants. Head MRI scan was performed for late preterm infants with high-risk factors for brain injury who were hospitalized between January 2009 and December 2014. The risk factors, clinical features, and head MRI features of encephalopathy in late preterm infants were analyzed. A total of 1 007 late preterm infants underwent MRI scan, among whom 313 (31.1%) had imaging features in accordance with the features of encephalopathy of prematurity. Of all infants, 76.7% had white matter damage. There was no association between the development of encephalopathy and gestational age in late preterm infants, but the detection rate of encephalopathy gradually increased with the increasing birth weight (P<0.05). The logistic regression analysis showed that a history of resuscitation was an independent risk factor for encephalopathy of prematurity (P<0.01). Encephalopathy of prematurity is commonly seen in high-risk late preterm infants, especially white matter damage. A history of resuscitation is an independent risk factor for encephalopathy in late preterm infants.

MRI in acute disseminated encephalomyelitis following Semple antirabies vaccine.

PubMed

Murthy, J M

1998-07-01

I reviewed MRI findings in five patients with acute disseminated encephalomyelitis following vaccination with Semple antirabies vaccine. MRI in two patients with encephalitis features showed multiple white matter lesions in the cerebrum, cerebellar peduncles and brain stem. Two patients who had features of cord involvement showed signal alterations in the cord extending over a few segments. Asymptomatic lesions in the cerebrum were seen in two patients. In a patient with encephalomyelitis MRI 50 days later showed resolution of the lesions. The white matter lesions described were indistinguishable from those seen in acute disseminated encephalomyelitis following other infections.

Learning in data-limited multimodal scenarios: Scandent decision forests and tree-based features.

PubMed

Hor, Soheil; Moradi, Mehdi

2016-12-01

Incomplete and inconsistent datasets often pose difficulties in multimodal studies. We introduce the concept of scandent decision trees to tackle these difficulties. Scandent trees are decision trees that optimally mimic the partitioning of the data determined by another decision tree, and crucially, use only a subset of the feature set. We show how scandent trees can be used to enhance the performance of decision forests trained on a small number of multimodal samples when we have access to larger datasets with vastly incomplete feature sets. Additionally, we introduce the concept of tree-based feature transforms in the decision forest paradigm. When combined with scandent trees, the tree-based feature transforms enable us to train a classifier on a rich multimodal dataset, and use it to classify samples with only a subset of features of the training data. Using this methodology, we build a model trained on MRI and PET images of the ADNI dataset, and then test it on cases with only MRI data. We show that this is significantly more effective in staging of cognitive impairments compared to a similar decision forest model trained and tested on MRI only, or one that uses other kinds of feature transform applied to the MRI data. Copyright © 2016. Published by Elsevier B.V.

MRI Interscanner Agreement of the Association between the Susceptibility Vessel Sign and Histologic Composition of Thrombi.

PubMed

Bourcier, Romain; Détraz, Lili; Serfaty, Jean Michel; Delasalle, Beatrice Guyomarch; Mirza, Mahmood; Derraz, Imad; Toulgoat, Frédérique; Naggara, Olivier; Toquet, Claire; Desal, Hubert

2017-11-01

The susceptibility vessel sign (SVS) on magnetic resonance imaging (MRI) is related to thrombus location, composition, and size in acute stroke. No previous study has determined its inter-MRI scanner variability. We aimed to compare the diagnostic accuracy in-vitro of four different MRI scanners for the characterization of histologic thrombus composition. Thirty-five manufactured thrombi analogs of different composition that were histologically categorized as fibrin-dominant, mixed, or red blood cell (RBC)-dominant were scanned on four different MRI units with T2* sequence. Nine radiologists, blinded to thrombus composition and MRI scanner model, classified twice, in a 2-week interval, the SVS of each thrombus as absent, questionable, or present. We calculated the weighted kappa with 95% confidence interval (CI), sensitivity, specificity and accuracy of the SVS on each MRI scanner to detect RBC-dominant thrombi. The SVS was present in 42%, absent in 33%, and questionable in 25% of thrombi. The interscanner agreement was moderate to good, ranging from .45 (CI: .37-.52) to .67 (CI: .61-.74). The correlation between the SVS and the thrombus composition was moderate (κ: .50 [CI: .44-.55]) to good κ: .76 ([CI: .72-.80]). Sensitivity, specificity, and accuracy to identify RBC-dominant clots were significantly different between MRI scanners (P < .001). The diagnostic accuracy of SVS to determine thrombus composition varies significantly among MRI scanners. Normalization of T2*sequences between scanners may be needed to better predict thrombus composition in multicenter studies. Copyright © 2017 by the American Society of Neuroimaging.

Multiparametric MRI characterization and prediction in autism spectrum disorder using graph theory and machine learning.

PubMed

Zhou, Yongxia; Yu, Fang; Duong, Timothy

2014-01-01

This study employed graph theory and machine learning analysis of multiparametric MRI data to improve characterization and prediction in autism spectrum disorders (ASD). Data from 127 children with ASD (13.5±6.0 years) and 153 age- and gender-matched typically developing children (14.5±5.7 years) were selected from the multi-center Functional Connectome Project. Regional gray matter volume and cortical thickness increased, whereas white matter volume decreased in ASD compared to controls. Small-world network analysis of quantitative MRI data demonstrated decreased global efficiency based on gray matter cortical thickness but not with functional connectivity MRI (fcMRI) or volumetry. An integrative model of 22 quantitative imaging features was used for classification and prediction of phenotypic features that included the autism diagnostic observation schedule, the revised autism diagnostic interview, and intelligence quotient scores. Among the 22 imaging features, four (caudate volume, caudate-cortical functional connectivity and inferior frontal gyrus functional connectivity) were found to be highly informative, markedly improving classification and prediction accuracy when compared with the single imaging features. This approach could potentially serve as a biomarker in prognosis, diagnosis, and monitoring disease progression.

A Comparison of Supervised Machine Learning Algorithms and Feature Vectors for MS Lesion Segmentation Using Multimodal Structural MRI

PubMed Central

Sweeney, Elizabeth M.; Vogelstein, Joshua T.; Cuzzocreo, Jennifer L.; Calabresi, Peter A.; Reich, Daniel S.; Crainiceanu, Ciprian M.; Shinohara, Russell T.

2014-01-01

Machine learning is a popular method for mining and analyzing large collections of medical data. We focus on a particular problem from medical research, supervised multiple sclerosis (MS) lesion segmentation in structural magnetic resonance imaging (MRI). We examine the extent to which the choice of machine learning or classification algorithm and feature extraction function impacts the performance of lesion segmentation methods. As quantitative measures derived from structural MRI are important clinical tools for research into the pathophysiology and natural history of MS, the development of automated lesion segmentation methods is an active research field. Yet, little is known about what drives performance of these methods. We evaluate the performance of automated MS lesion segmentation methods, which consist of a supervised classification algorithm composed with a feature extraction function. These feature extraction functions act on the observed T1-weighted (T1-w), T2-weighted (T2-w) and fluid-attenuated inversion recovery (FLAIR) MRI voxel intensities. Each MRI study has a manual lesion segmentation that we use to train and validate the supervised classification algorithms. Our main finding is that the differences in predictive performance are due more to differences in the feature vectors, rather than the machine learning or classification algorithms. Features that incorporate information from neighboring voxels in the brain were found to increase performance substantially. For lesion segmentation, we conclude that it is better to use simple, interpretable, and fast algorithms, such as logistic regression, linear discriminant analysis, and quadratic discriminant analysis, and to develop the features to improve performance. PMID:24781953

A comparison of supervised machine learning algorithms and feature vectors for MS lesion segmentation using multimodal structural MRI.

PubMed

Sweeney, Elizabeth M; Vogelstein, Joshua T; Cuzzocreo, Jennifer L; Calabresi, Peter A; Reich, Daniel S; Crainiceanu, Ciprian M; Shinohara, Russell T

2014-01-01

Machine learning is a popular method for mining and analyzing large collections of medical data. We focus on a particular problem from medical research, supervised multiple sclerosis (MS) lesion segmentation in structural magnetic resonance imaging (MRI). We examine the extent to which the choice of machine learning or classification algorithm and feature extraction function impacts the performance of lesion segmentation methods. As quantitative measures derived from structural MRI are important clinical tools for research into the pathophysiology and natural history of MS, the development of automated lesion segmentation methods is an active research field. Yet, little is known about what drives performance of these methods. We evaluate the performance of automated MS lesion segmentation methods, which consist of a supervised classification algorithm composed with a feature extraction function. These feature extraction functions act on the observed T1-weighted (T1-w), T2-weighted (T2-w) and fluid-attenuated inversion recovery (FLAIR) MRI voxel intensities. Each MRI study has a manual lesion segmentation that we use to train and validate the supervised classification algorithms. Our main finding is that the differences in predictive performance are due more to differences in the feature vectors, rather than the machine learning or classification algorithms. Features that incorporate information from neighboring voxels in the brain were found to increase performance substantially. For lesion segmentation, we conclude that it is better to use simple, interpretable, and fast algorithms, such as logistic regression, linear discriminant analysis, and quadratic discriminant analysis, and to develop the features to improve performance.

The Rise and Fall of Priming: How Visual Exposure Shapes Cortical Representations of Objects

PubMed Central

Zago, Laure; Fenske, Mark J.; Aminoff, Elissa; Bar, Moshe

2006-01-01

How does the amount of time for which we see an object influence the nature and content of its cortical representation? To address this question, we varied the duration of initial exposure to visual objects and then measured functional magnetic resonance imaging (fMRI) signal and behavioral performance during a subsequent repeated presentation of these objects. We report a novel ‘rise-and-fall’ pattern relating exposure duration and the corresponding magnitude of fMRI cortical signal. Compared with novel objects, repeated objects elicited maximal cortical response reduction when initially presented for 250 ms. Counter-intuitively, initially seeing an object for a longer duration significantly reduced the magnitude of this effect. This ‘rise-and-fall’ pattern was also evident for the corresponding behavioral priming. To account for these findings, we propose that the earlier interval of an exposure to a visual stimulus results in a fine-tuning of the cortical response, while additional exposure promotes selection of a subset of key features for continued representation. These two independent mechanisms complement each other in shaping object representations with experience. PMID:15716471

Development of a Support Vector Machine - Based Image Analysis System for Focal Liver Lesions Classification in Magnetic Resonance Images

NASA Astrophysics Data System (ADS)

Gatos, I.; Tsantis, S.; Karamesini, M.; Skouroliakou, A.; Kagadis, G.

2015-09-01

Purpose: The design and implementation of a computer-based image analysis system employing the support vector machine (SVM) classifier system for the classification of Focal Liver Lesions (FLLs) on routine non-enhanced, T2-weighted Magnetic Resonance (MR) images. Materials and Methods: The study comprised 92 patients; each one of them has undergone MRI performed on a Magnetom Concerto (Siemens). Typical signs on dynamic contrast-enhanced MRI and biopsies were employed towards a three class categorization of the 92 cases: 40-benign FLLs, 25-Hepatocellular Carcinomas (HCC) within Cirrhotic liver parenchyma and 27-liver metastases from Non-Cirrhotic liver. Prior to FLLs classification an automated lesion segmentation algorithm based on Marcov Random Fields was employed in order to acquire each FLL Region of Interest. 42 texture features derived from the gray-level histogram, co-occurrence and run-length matrices and 12 morphological features were obtained from each lesion. Stepwise multi-linear regression analysis was utilized to avoid feature redundancy leading to a feature subset that fed the multiclass SVM classifier designed for lesion classification. SVM System evaluation was performed by means of leave-one-out method and ROC analysis. Results: Maximum accuracy for all three classes (90.0%) was obtained by means of the Radial Basis Kernel Function and three textural features (Inverse- Different-Moment, Sum-Variance and Long-Run-Emphasis) that describe lesion's contrast, variability and shape complexity. Sensitivity values for the three classes were 92.5%, 81.5% and 96.2% respectively, whereas specificity values were 94.2%, 95.3% and 95.5%. The AUC value achieved for the selected subset was 0.89 with 0.81 - 0.94 confidence interval. Conclusion: The proposed SVM system exhibit promising results that could be utilized as a second opinion tool to the radiologist in order to decrease the time/cost of diagnosis and the need for patients to undergo invasive examination.

Defining active sacroiliitis on MRI for classification of axial spondyloarthritis: update by the ASAS MRI working group.

PubMed

Lambert, Robert G W; Bakker, Pauline A C; van der Heijde, Désirée; Weber, Ulrich; Rudwaleit, Martin; Hermann, K G; Sieper, Joachim; Baraliakos, Xenofon; Bennett, Alex; Braun, Jürgen; Burgos-Vargas, Rubén; Dougados, Maxime; Pedersen, Susanne Juhl; Jurik, Anne Grethe; Maksymowych, Walter P; Marzo-Ortega, Helena; Østergaard, Mikkel; Poddubnyy, Denis; Reijnierse, Monique; van den Bosch, Filip; van der Horst-Bruinsma, Irene; Landewé, Robert

2016-11-01

To review and update the existing definition of a positive MRI for classification of axial spondyloarthritis (SpA). The Assessment in SpondyloArthritis International Society (ASAS) MRI working group conducted a consensus exercise to review the definition of a positive MRI for inclusion in the ASAS classification criteria of axial SpA. Existing definitions and new data relevant to the MRI diagnosis and classification of sacroiliitis and spondylitis in axial SpA, published since the ASAS definition first appeared in print in 2009, were reviewed and discussed. The precise wording of the existing definition was examined in detail and the data and a draft proposal were presented to and voted on by the ASAS membership. The clear presence of bone marrow oedema on MRI in subchondral bone is still considered to be the defining observation that determines the presence of active sacroiliitis. Structural damage lesions seen on MRI may contribute to a decision by the observer that inflammatory lesions are genuinely due to SpA but are not required to meet the definition. The existing definition was clarified adding guidelines and images to assist in the application of the definition. The definition of a positive MRI for classification of axial SpA should continue to primarily depend on the imaging features of 'active sacroiliitis' until more data are available regarding MRI features of structural damage in the sacroiliac joint and MRI features in the spine and their utility when used for classification purposes. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/.

Computerized Interpretation of Dynamic Breast MRI

DTIC Science & Technology

2005-05-01

The interpretation criteria in the current literature fall Breast MRI has emerged as a promising modality for the into two major categories: 5’ 14...is that theraphy , current interpretation schemes might not be sufficiently ro- Despite its well-recognized advantages, applications of bust. MRI in...postcontrast series For the manual delineation, a radiologist (U.B.), blinded were then taken with a time interval of 60 s. Each series to the histological

The value of specific MRI features in the evaluation of suspected placental invasion.

PubMed

Lax, Allison; Prince, Martin R; Mennitt, Kevin W; Schwebach, J Reid; Budorick, Nancy E

2007-01-01

The objective of this study was to determine imaging features that may help predict the presence of placenta accreta, placenta increta or placenta percreta on prenatal MRI scanning. A retrospective review of the prenatal MR scans of 10 patients with a diagnosis of placenta accreta, placenta increta or placenta percreta made by pathologic and clinical reports and of 10 patients without placental invasion was performed. Two expert MRI readers were blinded to the patients' true diagnosis and were asked to score a total of 17 MRI features of the placenta and adjacent structures. The interrater reliability was assessed using kappa statistics. The features with a moderate kappa statistic or better (kappa > .40) were then compared with the true diagnosis for each observer. Seven of the scored features had an interobserver reliability of kappa > .40: placenta previa (kappa = .83); abnormal uterine bulging (kappa = .48); intraplacental hemorrhage (kappa = .51); heterogeneity of signal intensity on T2-weighted (T2W) imaging (kappa = .61); the presence of dark intraplacental bands on T2W imaging (kappa = .53); increased placental thickness (kappa = .69); and visualization of the myometrium beneath the placenta on T2W imaging (kappa = .44). Using Fisher's two-sided exact test, there was a statistically significant difference between the proportion of patients with placental invasion and those without placental invasion for three of the features: abnormal uterine bulging (Rater 1, P = .005; Rater 2, P = .011); heterogeneity of T2W imaging signal intensity (Rater 1, P = .006; Rater 2, P = .010); and presence of dark intraplacental bands on T2W imaging (Rater 1, P = .003; Rater 2, P = .033). MRI can be a useful adjunct to ultrasound in diagnosing placenta accreta prenatally. Three features that are seen on MRI in patients with placental invasion appear to be useful for diagnosis: uterine bulging; heterogeneous signal intensity within the placenta; and the presence of dark intraplacental bands on T2W imaging.

Random forest feature selection, fusion and ensemble strategy: Combining multiple morphological MRI measures to discriminate among healhy elderly, MCI, cMCI and alzheimer's disease patients: From the alzheimer's disease neuroimaging initiative (ADNI) database.

PubMed

Dimitriadis, S I; Liparas, Dimitris; Tsolaki, Magda N

2018-05-15

In the era of computer-assisted diagnostic tools for various brain diseases, Alzheimer's disease (AD) covers a large percentage of neuroimaging research, with the main scope being its use in daily practice. However, there has been no study attempting to simultaneously discriminate among Healthy Controls (HC), early mild cognitive impairment (MCI), late MCI (cMCI) and stable AD, using features derived from a single modality, namely MRI. Based on preprocessed MRI images from the organizers of a neuroimaging challenge, 3 we attempted to quantify the prediction accuracy of multiple morphological MRI features to simultaneously discriminate among HC, MCI, cMCI and AD. We explored the efficacy of a novel scheme that includes multiple feature selections via Random Forest from subsets of the whole set of features (e.g. whole set, left/right hemisphere etc.), Random Forest classification using a fusion approach and ensemble classification via majority voting. From the ADNI database, 60 HC, 60 MCI, 60 cMCI and 60 CE were used as a training set with known labels. An extra dataset of 160 subjects (HC: 40, MCI: 40, cMCI: 40 and AD: 40) was used as an external blind validation dataset to evaluate the proposed machine learning scheme. In the second blind dataset, we succeeded in a four-class classification of 61.9% by combining MRI-based features with a Random Forest-based Ensemble Strategy. We achieved the best classification accuracy of all teams that participated in this neuroimaging competition. The results demonstrate the effectiveness of the proposed scheme to simultaneously discriminate among four groups using morphological MRI features for the very first time in the literature. Hence, the proposed machine learning scheme can be used to define single and multi-modal biomarkers for AD. Copyright © 2017 Elsevier B.V. All rights reserved.

Imaging features of colovesical fistulae on MRI.

PubMed

Tang, Y Z; Booth, T C; Swallow, D; Shahabuddin, K; Thomas, M; Hanbury, D; Chang, S; King, C

2012-10-01

MRI is routinely used in the investigation of colovesical fistulae at our institute. Several papers have alluded to its usefulness in achieving the diagnosis; however, there is a paucity of literature on its imaging findings. Our objective was to quantify the MRI characteristics of these fistulae. We selected all cases over a 4-year period with a final clinical diagnosis of colovesical fistula which had been investigated with MRI. The MRI scans were reviewed in a consensus fashion by two consultant uroradiologists. Their MRI features were quantified. There were 40 cases of colovesical fistulae. On MRI, the fistula morphology consistently fell into three patterns. The most common pattern (71%) demonstrated an intervening abscess between the bowel wall and bladder wall. The second pattern (15%) had a visible track between the affected bowel and bladder. The third pattern (13%) was a complete loss of fat plane between the affected bladder and bowel wall. MRI correctly determined the underlying aetiology in 63% of cases. MRI is a useful imaging modality in the diagnosis of colovesical fistulae. The fistulae appear to have three characteristic morphological patterns that may aid future diagnoses of colovesical fistulae. To the authors' knowledge, this is the first publication of the MRI findings in colovesical fistulae.

Identifying radiological needs of referring clinicians.

PubMed

Zhang, Li; Hefke, Antje; Figiel, Jens; Schwarz, Ulrike; Rominger, Marga; Klose, Klaus Jochen

2013-06-01

To provide prospective information about quality- and satisfaction-related product features in radiology, a customer-centered approach for acquiring clinicians' requirements and their prioritizations is essential. We introduced the Kano model for the first time in radiology to obtain such information. A Kano questionnaire, consisting of pairs of questions regarding 13 clinician requirements related to computed tomography (CT), magnetic resonance imaging (MRI) access and report turnaround time (RTT), was developed and administered. Each requirement was assigned a Kano category, and its satisfaction and dissatisfaction coefficients were calculated and presented in a Kano diagram. The data were stratified based on different clinics and on staff and resident clinicians. The time interval was evaluated between the completion of an examination and the first attempt to access the report by a clinician. Consultation for modality selection and scheduling and access to CT within 24 h and RTT within 8 to 24 h were considered as must-be requirements. Access to CT within 4 h and within 8 h, access to MRI within 8 h and within 24 h, and access to RTT within 4 h were one-dimensional requirements. The extension of operation time for CT or MRI, as well as MRI access within 4 h, was considered attractive. Eight out of nine clinics considered RTT within 8 h as a must-be requirement. There were differences in responses both among different clinics and between staff and resident clinicians. Access attempts to reports by clinicians in the first 4 h after the examination completion accounted for 65 % of CTs and 49 % of MRIs.

MRI abnormalities of peripheral nerve and muscle are common in amyotrophic lateral sclerosis and share features with multifocal motor neuropathy

PubMed Central

Staff, Nathan P.; Amrami, Kimberly K.; Howe, Benjamin M.

2015-01-01

Introduction MRI of peripheral nerve and muscle in patients with ALS may be performed to investigate alternative diagnoses including multifocal motor neuropathy (MMN). MRI findings of peripheral nerve and muscle are not well described in these conditions, making interpretation of results difficult. Methods We examined systematically the peripheral nerve and muscle MRI findings in patients with ALS (n=60) and MMN (n=8). Results In patients with ALS and MMN, abnormal MRIs were common (85% and 75%, respectively) but did not correlate with disease severity. Peripheral nerve MRI abnormalities were similar in frequency (ALS: 58% vs. MMN: 63%) with most changes being of mild-to-moderate severity. Muscle MRI changes were more common in ALS (57% vs. 33%), and no muscle atrophy was seen in patients with MMN. Discussion MRI abnormalities of peripheral nerve and muscle in ALS and MMN are common and share some features. PMID:25736373

Volumetric Assessment of Swallowing Muscles: A Comparison of CT and MRI Segmentation.

PubMed

Sporns, Kim Barbara; Hanning, Uta; Schmidt, Rene; Muhle, Paul; Wirth, Rainer; Zimmer, Sebastian; Dziewas, Rainer; Suntrup-Krueger, Sonja; Sporns, Peter Bernhard; Heindel, Walter; Schwindt, Wolfram

2018-05-01

 Recent retrospective studies have proposed a high correlation between atrophy of swallowing muscles, age, severity of dysphagia and aspiration status based on computed tomography (CT). However, ionizing radiation poses an ethical barrier to research in prospective non-patient populations. Hence, there is a need to prove the efficacy of techniques that rely on noninvasive methods and produce high-resolution soft tissue images such as magnetic resonance imaging (MRI). The objective of this study was therefore to compare the segmentation results of swallowing muscles using CT and MRI.  Retrospective study of 21 patients (median age: 46.6; gender: 11 female) who underwent CT and MRI of the head and neck region within a time frame of less than 50 days because of suspected head and neck cancer using contrast agent. CT and MR images were segmented by two blinded readers using Medical Imaging Toolkit (MITK) and both modalities were tested (with the equivalence test) regarding the segmented muscle volumes. Adjustment for multiple testing was performed using the Bonferroni test and the potential time effect of the muscle volumes and the time interval between the modalities was assessed by a spearman correlation. The study was approved by the local ethics committee.  The median volumes for each muscle belly of the digastric muscle derived from CT were 3051 mm 3 (left) and 2969 mm 3 (right), and from MRI they were 3218 mm 3 (left) and 3027 mm 3 (right). The median volume of the geniohyoid muscle was 6580 mm 3 on CT and 6648 mm 3 on MRI. The interrater reliability was high for all segmented muscles. The mean time interval between the CT and MRI examinations was 34 days (IQR 25; 41). The muscle differences of each muscle between the two modalities did not reveal significant correlation to the time interval between the examinations (digastric left r = 0.003 and digastric right r = -0.008; geniohyoid muscle r = 0.075).  CT-based segmentation and MRI-based segmentation of the digastric and geniohyoid muscle are equally feasible. The potential advantage of MRI for prospective studies is the absence of ionizing radiation.   · CT-based segmentation and MRI-based segmentation of the swallowing muscles are equally feasible.. · The advantage of MRI is the absence of ionizing radiation.. · MRI should therefore be deployed for future prospective studies.. · Sporns KB, Hanning U, Schmidt R et al. Volumetric Assessment of Swallowing Muscles: A Comparison of CT and MRI Segmentation. Fortschr Röntgenstr 2018; 190: 441 - 446. © Georg Thieme Verlag KG Stuttgart · New York.

Presence of Late Gadolinium Enhancement by Cardiac Magnetic Resonance Among Patients With Suspected Cardiac Sarcoidosis Is Associated With Adverse Cardiovascular Prognosis: A Systematic Review and Meta-Analysis.

PubMed

Hulten, Edward; Agarwal, Vikram; Cahill, Michael; Cole, Geoff; Vita, Tomas; Parrish, Scott; Bittencourt, Marcio Sommer; Murthy, Venkatesh L; Kwong, Raymond; Di Carli, Marcelo F; Blankstein, Ron

2016-09-01

Individuals with cardiac sarcoidosis have an increased risk of ventricular arrhythmia and death. Several small cohort studies have evaluated the ability of late gadolinium enhancement (LGE) by cardiac magnetic resonance imaging (MRI) to predict adverse cardiovascular events. However, studies have yielded inconsistent results, and some analyses were underpowered. Therefore, we sought to systematically review and perform meta-analysis of the prognostic value of cardiac MRI for patients with known or suspected cardiac sarcoidosis. We systematically searched for cohort studies of patients with known sarcoidosis with suspected cardiac involvement who underwent cardiac MRI with LGE with at least 12 months of either prospective or retrospective follow-up data regarding post-MRI adverse cardiovascular outcomes. We identified 7 studies of 694 subjects (mean age 53; 42% men).One hundred and ninety-nine patients (29%) were LGE positive. All-cause mortality occurred in 19 LGE-positive versus 17 LGE-negative subjects (annualized incidence, 3.1% versus 0.6%). The pooled relative risk was 3.38 (95% confidence interval, 1.07-10.7; P=0.04). Cardiovascular mortality occurred in 10 LGE-positive versus 2 LGE-negative subjects (annualized incidence, 1.9% versus 0.3%; relative risk 10.7 [95% confidence interval, 1.34-86.3]; P=0.03). Ventricular arrhythmia occurred in 41 LGE-positive versus 0 LGE-negative subjects (annualized incidence, 5.9% versus 0%; relative risk 19.5 [95% confidence interval, 2.68-143]; P=0.003). A combined end point of death or ventricular arrhythmia occurred in 64 LGE-positive versus 18 LGE-negative subjects (annualized incidence, 8.8% versus 0.6%; relative risk 6.20 [95% confidence interval, 2.47-15.6]; P<0.001). There was no significant heterogeneity for any outcomes. LGE is associated with future cardiovascular death and ventricular arrhythmia among patients referred to MRI for known or suspected cardiac sarcoidosis. © 2016 American Heart Association, Inc.

Single-trial EEG-informed fMRI analysis of emotional decision problems in hot executive function.

PubMed

Guo, Qian; Zhou, Tiantong; Li, Wenjie; Dong, Li; Wang, Suhong; Zou, Ling

2017-07-01

Executive function refers to conscious control in psychological process which relates to thinking and action. Emotional decision is a part of hot executive function and contains emotion and logic elements. As a kind of important social adaptation ability, more and more attention has been paid in recent years. Gambling task can be well performed in the study of emotional decision. As fMRI researches focused on gambling task show not completely consistent brain activation regions, this study adopted EEG-fMRI fusion technology to reveal brain neural activity related with feedback stimuli. In this study, an EEG-informed fMRI analysis was applied to process simultaneous EEG-fMRI data. First, relative power-spectrum analysis and K-means clustering method were performed separately to extract EEG-fMRI features. Then, Generalized linear models were structured using fMRI data and using different EEG features as regressors. The results showed that in the win versus loss stimuli, the activated regions almost covered the caudate, the ventral striatum (VS), the orbital frontal cortex (OFC), and the cingulate. Wide activation areas associated with reward and punishment were revealed by the EEG-fMRI integration analysis than the conventional fMRI results, such as the posterior cingulate and the OFC. The VS and the medial prefrontal cortex (mPFC) were found when EEG power features were performed as regressors of GLM compared with results entering the amplitudes of feedback-related negativity (FRN) as regressors. Furthermore, the brain region activation intensity was the strongest when theta-band power was used as a regressor compared with the other two fusion results. The EEG-based fMRI analysis can more accurately depict the whole-brain activation map and analyze emotional decision problems.

Novel and general approach to linear filter design for contrast-to-noise ratio enhancement of magnetic resonance images with multiple interfering features in the scene

NASA Astrophysics Data System (ADS)

Soltanian-Zadeh, Hamid; Windham, Joe P.

1992-04-01

Maximizing the minimum absolute contrast-to-noise ratios (CNRs) between a desired feature and multiple interfering processes, by linear combination of images in a magnetic resonance imaging (MRI) scene sequence, is attractive for MRI analysis and interpretation. A general formulation of the problem is presented, along with a novel solution utilizing the simple and numerically stable method of Gram-Schmidt orthogonalization. We derive explicit solutions for the case of two interfering features first, then for three interfering features, and, finally, using a typical example, for an arbitrary number of interfering feature. For the case of two interfering features, we also provide simplified analytical expressions for the signal-to-noise ratios (SNRs) and CNRs of the filtered images. The technique is demonstrated through its applications to simulated and acquired MRI scene sequences of a human brain with a cerebral infarction. For these applications, a 50 to 100% improvement for the smallest absolute CNR is obtained.

Magnetic resonance characteristics and susceptibility weighted imaging of the brain in gadolinium encephalopathy.

PubMed

Samardzic, Dejan; Thamburaj, Krishnamoorthy

2015-01-01

To report the brain imaging features on magnetic resonance imaging (MRI) in inadvertent intrathecal gadolinium administration. A 67-year-old female with gadolinium encephalopathy from inadvertent high dose intrathecal gadolinium administration during an epidural steroid injection was studied with multisequence 3T MRI. T1-weighted imaging shows pseudo-T2 appearance with diffusion of gadolinium into the brain parenchyma, olivary bodies, and membranous labyrinth. Nulling of cerebrospinal fluid (CSF) signal is absent on fluid attenuation recovery (FLAIR). Susceptibility-weighted imaging (SWI) demonstrates features similar to subarachnoid hemorrhage. CT may demonstrate a pseudo-cerebral edema pattern given the high attenuation characteristics of gadolinium. Intrathecal gadolinium demonstrates characteristic imaging features on MRI of the brain and may mimic subarachnoid hemorrhage on susceptibility-weighted imaging. Identifying high dose gadolinium within the CSF spaces on MRI is essential to avoid diagnostic and therapeutic errors. Copyright © 2013 by the American Society of Neuroimaging.

The role of magnetic resonance imaging and ultrasound in patients with adnexal masses.

PubMed

Sohaib, S A; Mills, T D; Sahdev, A; Webb, J A W; Vantrappen, P O; Jacobs, I J; Reznek, R H

2005-03-01

To evaluate the accuracy of ultrasonography (US) and magnetic resonance imaging (MRI) in characterizing adnexal masses, and to determine which patients may benefit from MRI. We prospectively studied 72 women (mean age 53 years, range 19 to 86 years) with clinically suspected adnexal masses. A single experienced sonographer performed transabdominal and transvaginal greyscale spectral and colour Doppler examinations. MRI was carried out on a 1.5T system using T1, T2 and fat-suppressed T1-weighted sequences before and after intravenous injection of gadolinium. The adnexal masses were categorized as benign or malignant without knowledge of clinical details, according to the imaging features which were compared with the surgical and pathological findings. For characterizing lesions as malignant, the sensitivity, specificity and accuracy of MRI were 96.6%, 83.7% and 88.9%, respectively, and of US were 100%, 39.5% and 63.9%, respectively. MRI was more specific (p<0.05) than US. Both MRI and US correctly diagnosed 17 (24%) cases with benign and 28 (39%) cases with malignant masses. MRI correctly diagnosed 19 (26%) cases with benign lesion(s), which on US were thought to be malignant. The age, menopausal status and CA-125 levels in these women made benign disease likely, but US features were suggestive of malignancy (large masses and solid-cystic lesions with nodules). MRI is more specific and accurate than US and Doppler assessment for characterizing adnexal masses. Women who clinically have a relatively low risk of malignancy but who have complex sonographic features may benefit from MRI.

When the Brain Takes 'BOLD' Steps: Real-Time fMRI Neurofeedback Can Further Enhance the Ability to Gradually Self-regulate Regional Brain Activation.

PubMed

Sorger, Bettina; Kamp, Tabea; Weiskopf, Nikolaus; Peters, Judith Caroline; Goebel, Rainer

2018-05-15

Brain-computer interfaces (BCIs) based on real-time functional magnetic resonance imaging (rtfMRI) are currently explored in the context of developing alternative (motor-independent) communication and control means for the severely disabled. In such BCI systems, the user encodes a particular intention (e.g., an answer to a question or an intended action) by evoking specific mental activity resulting in a distinct brain state that can be decoded from fMRI activation. One goal in this context is to increase the degrees of freedom in encoding different intentions, i.e., to allow the BCI user to choose from as many options as possible. Recently, the ability to voluntarily modulate spatial and/or temporal blood oxygenation level-dependent (BOLD)-signal features has been explored implementing different mental tasks and/or different encoding time intervals, respectively. Our two-session fMRI feasibility study systematically investigated for the first time the possibility of using magnitudinal BOLD-signal features for intention encoding. Particularly, in our novel paradigm, participants (n=10) were asked to alternately self-regulate their regional brain-activation level to 30%, 60% or 90% of their maximal capacity by applying a selected activation strategy (i.e., performing a mental task, e.g., inner speech) and modulation strategies (e.g., using different speech rates) suggested by the experimenters. In a second step, we tested the hypothesis that the additional availability of feedback information on the current BOLD-signal level within a region of interest improves the gradual-self regulation performance. Therefore, participants were provided with neurofeedback in one of the two fMRI sessions. Our results show that the majority of the participants were able to gradually self-regulate regional brain activation to at least two different target levels even in the absence of neurofeedback. When provided with continuous feedback on their current BOLD-signal level, most participants further enhanced their gradual self-regulation ability. Our findings were observed across a wide variety of mental tasks and across clinical MR field strengths (i.e., at 1.5T and 3T), indicating that these findings are robust and can be generalized across mental tasks and scanner types. The suggested novel parametric activation paradigm enriches the spectrum of current rtfMRI-neurofeedback and BCI methodology and has considerable potential for fundamental and clinical neuroscience applications. Copyright © 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.

Is Ultrasound As Useful As Metal Artifact Reduction Sequence Magnetic Resonance Imaging in Longitudinal Surveillance of Metal-on-Metal Hip Arthroplasty Patients?

PubMed

Kwon, Young-Min; Dimitriou, Dimitris; Liow, Ming Han Lincoln; Tsai, Tsung-Yuan; Li, Guoan

2016-08-01

Current guidelines recommend longitudinal monitoring of at-risk metal-on-metal (MoM) arthroplasty patients with cross-sectional imaging such as metal artifact reduction sequence (MARS) magnetic resonance imaging (MRI) or ultrasound. During follow-up evaluations, the clinical focus is on the relative interval changes in symptoms, radiographs, laboratory tests, and cross-sectional imaging modalities. Although MRI has the capacity for the detection of adverse local soft tissue reactions (ALTRs), the potential disadvantages of MARS MRI include the obscuration of periprosthetic tissues by metal artifacts and the cost. The aim of this study was to evaluate the diagnostic accuracy of ultrasound in comparison with MARS MRI in detecting ALTR in MoM patients during consecutive follow-up. Thirty-five MoM patients (42 hips) were recruited prospectively to evaluate the sensitivity and specificity of the ultrasound for detecting ALTR in relation to MARS MRI during 2 longitudinal follow-up scans. The agreement between ultrasound and MARS MRI in ALTR grade, size, and size change was calculated. At the initial evaluation and at the subsequent follow-up, ultrasound had a sensitivity of 81% and 86% and a specificity of 92% and 88%, respectively. At the follow-up evaluations, ultrasound was able to detect the "change" in the lesions size with -0.3 cm(2) average bias from the MARS MRI with higher agreement (k = 0.85) with MARS MRI compared to the initial evaluation in detecting any "change" in ALTR size or grade. Ultrasound detected the interval change in the ALTR size and grade with higher accuracy and higher agreement with MARS MRI compared with the initial evaluation, suggesting ultrasound is a valid and useful. Copyright © 2016 Elsevier Inc. All rights reserved.

Accuracy of ultrasonography and magnetic resonance imaging in the diagnosis of placenta accreta.

PubMed

Riteau, Anne-Sophie; Tassin, Mikael; Chambon, Guillemette; Le Vaillant, Claudine; de Laveaucoupet, Jocelyne; Quéré, Marie-Pierre; Joubert, Madeleine; Prevot, Sophie; Philippe, Henri-Jean; Benachi, Alexandra

2014-01-01

To evaluate the accuracy of ultrasonography and magnetic resonance imaging (MRI) in the diagnosis of placenta accreta and to define the most relevant specific ultrasound and MRI features that may predict placental invasion. This study was approved by the institutional review board of the French College of Obstetricians and Gynecologists. We retrospectively reviewed the medical records of all patients referred for suspected placenta accreta to two university hospitals from 01/2001 to 05/2012. Our study population included 42 pregnant women who had been investigated by both ultrasonography and MRI. Ultrasound images and MRI were blindly reassessed for each case by 2 raters in order to score features that predict abnormal placental invasion. Sensitivity in the diagnosis of placenta accreta was 100% with ultrasound and 76.9% for MRI (P = 0.03). Specificity was 37.5% with ultrasonography and 50% for MRI (P = 0.6). The features of greatest sensitivity on ultrasonography were intraplacental lacunae and loss of the normal retroplacental clear space. Increased vascularization in the uterine serosa-bladder wall interface and vascularization perpendicular to the uterine wall had the best positive predictive value (92%). At MRI, uterine bulging had the best positive predictive value (85%) and its combination with the presence of dark intraplacental bands on T2-weighted images improved the predictive value to 90%. Ultrasound imaging is the mainstay of screening for placenta accreta. MRI appears to be complementary to ultrasonography, especially when there are few ultrasound signs.

Framework for 3D histologic reconstruction and fusion with in vivo MRI: Preliminary results of characterizing pulmonary inflammation in a mouse model.

PubMed

Rusu, Mirabela; Golden, Thea; Wang, Haibo; Gow, Andrew; Madabhushi, Anant

2015-08-01

Pulmonary inflammation is associated with a variety of diseases. Assessing pulmonary inflammation on in vivo imaging may facilitate the early detection and treatment of lung diseases. Although routinely used in thoracic imaging, computed tomography has thus far not been compellingly shown to characterize inflammation in vivo. Alternatively, magnetic resonance imaging (MRI) is a nonionizing radiation technique to better visualize and characterize pulmonary tissue. Prior to routine adoption of MRI for early characterization of inflammation in humans, a rigorous and quantitative characterization of the utility of MRI to identify inflammation is required. Such characterization may be achieved by considering ex vivo histology as the ground truth, since it enables the definitive spatial assessment of inflammation. In this study, the authors introduce a novel framework to integrate 2D histology, ex vivo and in vivo imaging to enable the mapping of the extent of disease from ex vivo histology onto in vivo imaging, with the goal of facilitating computerized feature analysis and interrogation of disease appearance on in vivo imaging. The authors' framework was evaluated in a preclinical preliminary study aimed to identify computer extracted features on in vivo MRI associated with chronic pulmonary inflammation. The authors' image analytics framework first involves reconstructing the histologic volume in 3D from individual histology slices. Second, the authors map the disease ground truth onto in vivo MRI via coregistration with 3D histology using the ex vivo lung MRI as a conduit. Finally, computerized feature analysis of the disease extent is performed to identify candidate in vivo imaging signatures of disease presence and extent. The authors evaluated the framework by assessing the quality of the 3D histology reconstruction and the histology-MRI fusion, in the context of an initial use case involving characterization of chronic inflammation in a mouse model. The authors' evaluation considered three mice, two with an inflammation phenotype and one control. The authors' iterative 3D histology reconstruction yielded a 70.1% ± 2.7% overlap with the ex vivo MRI volume. Across a total of 17 anatomic landmarks manually delineated at the division of airways, the target registration error between the ex vivo MRI and 3D histology reconstruction was 0.85 ± 0.44 mm, suggesting that a good alignment of the ex vivo 3D histology and ex vivo MRI had been achieved. The 3D histology-in vivo MRI coregistered volumes resulted in an overlap of 73.7% ± 0.9%. Preliminary computerized feature analysis was performed on an additional four control mice, for a total of seven mice considered in this study. Gabor texture filters appeared to best capture differences between the inflamed and noninflamed regions on MRI. The authors' 3D histology reconstruction and multimodal registration framework were successfully employed to reconstruct the histology volume of the lung and fuse it with in vivo MRI to create a ground truth map for inflammation on in vivo MRI. The analytic platform presented here lays the framework for a rigorous validation of the identified imaging features for chronic lung inflammation on MRI in a large prospective cohort.

Magnetic Resonance Imaging Features as Surrogate Markers of X-Linked Hypophosphatemic Rickets Activity.

PubMed

Lempicki, Marta; Rothenbuhler, Anya; Merzoug, Valérie; Franchi-Abella, Stéphanie; Chaussain, Catherine; Adamsbaum, Catherine; Linglart, Agnès

2017-01-01

X-linked hypophosphatemic rickets (XLH) is the most common form of inheritable rickets. Rickets treatment is monitored by assessing alkaline phosphatase (ALP) levels, clinical features, and radiographs. Our objectives were to describe the magnetic resonance imaging (MRI) features of XLH and to assess correlations with disease activity. Twenty-seven XLH patients (median age 9.2 years) were included in this prospective single-center observational study. XLH activity was assessed using height, leg bowing, dental abscess history, and serum ALP levels. We looked for correlations between MRI features and markers of disease activity. On MRI, the median maximum width of the physis was 5.6 mm (range 4.8-7.8; normal <1.5), being >1.5 mm in all of the patients. The appearance of the zone of provisional calcification was abnormal on 21 MRI images (78%), Harris lines were present on 24 (89%), and bone marrow signal abnormalities were present on 16 (59%). ALP levels correlated with the maximum physeal widening and with the transverse extent of the widening. MRI of the knee provides precise rickets patterns that are correlated with ALP, an established biochemical marker of the disease, avoiding X-ray exposure and providing surrogate quantitative markers of disease activity. © 2017 S. Karger AG, Basel.

A SVM-based quantitative fMRI method for resting-state functional network detection.

PubMed

Song, Xiaomu; Chen, Nan-kuei

2014-09-01

Resting-state functional magnetic resonance imaging (fMRI) aims to measure baseline neuronal connectivity independent of specific functional tasks and to capture changes in the connectivity due to neurological diseases. Most existing network detection methods rely on a fixed threshold to identify functionally connected voxels under the resting state. Due to fMRI non-stationarity, the threshold cannot adapt to variation of data characteristics across sessions and subjects, and generates unreliable mapping results. In this study, a new method is presented for resting-state fMRI data analysis. Specifically, the resting-state network mapping is formulated as an outlier detection process that is implemented using one-class support vector machine (SVM). The results are refined by using a spatial-feature domain prototype selection method and two-class SVM reclassification. The final decision on each voxel is made by comparing its probabilities of functionally connected and unconnected instead of a threshold. Multiple features for resting-state analysis were extracted and examined using an SVM-based feature selection method, and the most representative features were identified. The proposed method was evaluated using synthetic and experimental fMRI data. A comparison study was also performed with independent component analysis (ICA) and correlation analysis. The experimental results show that the proposed method can provide comparable or better network detection performance than ICA and correlation analysis. The method is potentially applicable to various resting-state quantitative fMRI studies. Copyright © 2014 Elsevier Inc. All rights reserved.

Reducing Field Distortion in Magnetic Resonance Imaging

NASA Technical Reports Server (NTRS)

Eom, Byeong Ho; Penanen, Konstantin; Hahn, Inseob

2010-01-01

A concept for a magnetic resonance imaging (MRI) system that would utilize a relatively weak magnetic field provides for several design features that differ significantly from the corresponding features of conventional MRI systems. Notable among these features are a magnetic-field configuration that reduces (relative to the conventional configuration) distortion and blurring of the image, the use of a superconducting quantum interference device (SQUID) magnetometer as the detector, and an imaging procedure suited for the unconventional field configuration and sensor. In a typical application of MRI, a radio-frequency pulse is used to excite precession of the magnetic moments of protons in an applied magnetic field, and the decaying precession is detected for a short time following the pulse. The precession occurs at a resonance frequency proportional to the strengths of the magnetic field and the proton magnetic moment. The magnetic field is configured to vary with position in a known way; hence, by virtue of the aforesaid proportionality, the resonance frequency varies with position in a known way. In other words, position is encoded as resonance frequency. MRI using magnetic fields weaker than those of conventional MRI offers several advantages, including cheaper and smaller equipment, greater compatibility with metallic objects, and higher image quality because of low susceptibility distortion and enhanced spin-lattice-relaxation- time contrast. SQUID MRI is being developed into a practical MRI method for applied magnetic flux densities of the order of only 100 T

Brain Morphometry Using Anatomical Magnetic Resonance Imaging

ERIC Educational Resources Information Center

Bansal, Ravi; Gerber, Andrew J.; Peterson, Bradley S.

2008-01-01

The efficacy of anatomical magnetic resonance imaging (MRI) in studying the morphological features of various regions of the brain is described, also providing the steps used in the processing and studying of the images. The ability to correlate these features with several clinical and psychological measures can help in using anatomical MRI to…

WE-B-BRD-00: MRI for Radiation Oncology

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

NONE

The use of MRI in radiation therapy is rapidly increasing. Applications vary from the MRI simulator, to the MRI fused with CT, and to the integrated MRI+RT system. Compared with the standard MRI QA, a broader scope of QA features has to be defined in order to maximize the benefits of using MRI in radiation therapy. These QA features include geometric fidelity, image registration, motion management, cross-system alignment, and hardware interference. Advanced MRI techniques require a specific type of QA, as they are being widely used in radiation therapy planning, dose calculations, post-implant dosimetry, and prognoses. A vigorous and adaptivemore » QA program is crucial to defining the responsibility of the entire radiation therapy group and detecting deviations from the performance of high-quality treatment. As a drastic departure from CT simulation, MRI simulation requires changes in the work flow of treatment planning and image guidance. MRI guided radiotherapy platforms are being developed and commercialized to take the advantage of the advance in knowledge, technology and clinical experience. This symposium will from an educational perspective discuss the scope and specific issues related to MRI guided radiotherapy. Learning Objectives: Understand the difference between a standard and a radiotherapy-specific MRI QA program. Understand the effects of MRI artifacts (geometric distortion and motion) on radiotherapy. Understand advanced MRI techniques (ultrashort echo, fast MRI including dynamic MRI and 4DMRI, diffusion, perfusion, and MRS) and related QA. Understand the methods to prepare MRI for treatment planning (electron density assignment, multimodality image registration, segmentation and motion management). Current status of MRI guided treatment platforms. Dr. Jihong Wang has a research grant with Elekta-MRL project. Dr. Ke Sheng receives research grants from Varian Medical systems.« less

On application of kernel PCA for generating stimulus features for fMRI during continuous music listening.

PubMed

Tsatsishvili, Valeri; Burunat, Iballa; Cong, Fengyu; Toiviainen, Petri; Alluri, Vinoo; Ristaniemi, Tapani

2018-06-01

There has been growing interest towards naturalistic neuroimaging experiments, which deepen our understanding of how human brain processes and integrates incoming streams of multifaceted sensory information, as commonly occurs in real world. Music is a good example of such complex continuous phenomenon. In a few recent fMRI studies examining neural correlates of music in continuous listening settings, multiple perceptual attributes of music stimulus were represented by a set of high-level features, produced as the linear combination of the acoustic descriptors computationally extracted from the stimulus audio. NEW METHOD: fMRI data from naturalistic music listening experiment were employed here. Kernel principal component analysis (KPCA) was applied to acoustic descriptors extracted from the stimulus audio to generate a set of nonlinear stimulus features. Subsequently, perceptual and neural correlates of the generated high-level features were examined. The generated features captured musical percepts that were hidden from the linear PCA features, namely Rhythmic Complexity and Event Synchronicity. Neural correlates of the new features revealed activations associated to processing of complex rhythms, including auditory, motor, and frontal areas. Results were compared with the findings in the previously published study, which analyzed the same fMRI data but applied linear PCA for generating stimulus features. To enable comparison of the results, methodology for finding stimulus-driven functional maps was adopted from the previous study. Exploiting nonlinear relationships among acoustic descriptors can lead to the novel high-level stimulus features, which can in turn reveal new brain structures involved in music processing. Copyright © 2018 Elsevier B.V. All rights reserved.

Textural kinetics: a novel dynamic contrast-enhanced (DCE)-MRI feature for breast lesion classification.

PubMed

Agner, Shannon C; Soman, Salil; Libfeld, Edward; McDonald, Margie; Thomas, Kathleen; Englander, Sarah; Rosen, Mark A; Chin, Deanna; Nosher, John; Madabhushi, Anant

2011-06-01

Dynamic contrast-enhanced (DCE)-magnetic resonance imaging (MRI) of the breast has emerged as an adjunct imaging tool to conventional X-ray mammography due to its high detection sensitivity. Despite the increasing use of breast DCE-MRI, specificity in distinguishing malignant from benign breast lesions is low, and interobserver variability in lesion classification is high. The novel contribution of this paper is in the definition of a new DCE-MRI descriptor that we call textural kinetics, which attempts to capture spatiotemporal changes in breast lesion texture in order to distinguish malignant from benign lesions. We qualitatively and quantitatively demonstrated on 41 breast DCE-MRI studies that textural kinetic features outperform signal intensity kinetics and lesion morphology features in distinguishing benign from malignant lesions. A probabilistic boosting tree (PBT) classifier in conjunction with textural kinetic descriptors yielded an accuracy of 90%, sensitivity of 95%, specificity of 82%, and an area under the curve (AUC) of 0.92. Graph embedding, used for qualitative visualization of a low-dimensional representation of the data, showed the best separation between benign and malignant lesions when using textural kinetic features. The PBT classifier results and trends were also corroborated via a support vector machine classifier which showed that textural kinetic features outperformed the morphological, static texture, and signal intensity kinetics descriptors. When textural kinetic attributes were combined with morphologic descriptors, the resulting PBT classifier yielded 89% accuracy, 99% sensitivity, 76% specificity, and an AUC of 0.91.

Targeting SRC Family Kinases and HSP90 in Lung Cancer

DTIC Science & Technology

2016-12-01

inhalation of Adeno-Cre, followed by MRI imaging at regular intervals to detect tumor initiation and growth, followed by euthanasia and processing of...experimental endpoint. 10 mice were used per time point Representative MRI data describing tumor volume (TV) are shown in Figure 1. Quantification of data is...dasatinib, we were able to make several conclusions. Figure 1. Representative MRI images from Nedd9wt or Nedd9 null Kras mutant mice, treated with

MRI-Targeted or Standard Biopsy for Prostate-Cancer Diagnosis.

PubMed

Kasivisvanathan, Veeru; Rannikko, Antti S; Borghi, Marcelo; Panebianco, Valeria; Mynderse, Lance A; Vaarala, Markku H; Briganti, Alberto; Budäus, Lars; Hellawell, Giles; Hindley, Richard G; Roobol, Monique J; Eggener, Scott; Ghei, Maneesh; Villers, Arnauld; Bladou, Franck; Villeirs, Geert M; Virdi, Jaspal; Boxler, Silvan; Robert, Grégoire; Singh, Paras B; Venderink, Wulphert; Hadaschik, Boris A; Ruffion, Alain; Hu, Jim C; Margolis, Daniel; Crouzet, Sébastien; Klotz, Laurence; Taneja, Samir S; Pinto, Peter; Gill, Inderbir; Allen, Clare; Giganti, Francesco; Freeman, Alex; Morris, Stephen; Punwani, Shonit; Williams, Norman R; Brew-Graves, Chris; Deeks, Jonathan; Takwoingi, Yemisi; Emberton, Mark; Moore, Caroline M

2018-05-10

Multiparametric magnetic resonance imaging (MRI), with or without targeted biopsy, is an alternative to standard transrectal ultrasonography-guided biopsy for prostate-cancer detection in men with a raised prostate-specific antigen level who have not undergone biopsy. However, comparative evidence is limited. In a multicenter, randomized, noninferiority trial, we assigned men with a clinical suspicion of prostate cancer who had not undergone biopsy previously to undergo MRI, with or without targeted biopsy, or standard transrectal ultrasonography-guided biopsy. Men in the MRI-targeted biopsy group underwent a targeted biopsy (without standard biopsy cores) if the MRI was suggestive of prostate cancer; men whose MRI results were not suggestive of prostate cancer were not offered biopsy. Standard biopsy was a 10-to-12-core, transrectal ultrasonography-guided biopsy. The primary outcome was the proportion of men who received a diagnosis of clinically significant cancer. Secondary outcomes included the proportion of men who received a diagnosis of clinically insignificant cancer. A total of 500 men underwent randomization. In the MRI-targeted biopsy group, 71 of 252 men (28%) had MRI results that were not suggestive of prostate cancer, so they did not undergo biopsy. Clinically significant cancer was detected in 95 men (38%) in the MRI-targeted biopsy group, as compared with 64 of 248 (26%) in the standard-biopsy group (adjusted difference, 12 percentage points; 95% confidence interval [CI], 4 to 20; P=0.005). MRI, with or without targeted biopsy, was noninferior to standard biopsy, and the 95% confidence interval indicated the superiority of this strategy over standard biopsy. Fewer men in the MRI-targeted biopsy group than in the standard-biopsy group received a diagnosis of clinically insignificant cancer (adjusted difference, -13 percentage points; 95% CI, -19 to -7; P<0.001). The use of risk assessment with MRI before biopsy and MRI-targeted biopsy was superior to standard transrectal ultrasonography-guided biopsy in men at clinical risk for prostate cancer who had not undergone biopsy previously. (Funded by the National Institute for Health Research and the European Association of Urology Research Foundation; PRECISION ClinicalTrials.gov number, NCT02380027 .).

Classifying brain metastases by their primary site of origin using a radiomics approach based on texture analysis: a feasibility study.

PubMed

Ortiz-Ramón, Rafael; Larroza, Andrés; Ruiz-España, Silvia; Arana, Estanislao; Moratal, David

2018-05-14

To examine the capability of MRI texture analysis to differentiate the primary site of origin of brain metastases following a radiomics approach. Sixty-seven untreated brain metastases (BM) were found in 3D T1-weighted MRI of 38 patients with cancer: 27 from lung cancer, 23 from melanoma and 17 from breast cancer. These lesions were segmented in 2D and 3D to compare the discriminative power of 2D and 3D texture features. The images were quantized using different number of gray-levels to test the influence of quantization. Forty-three rotation-invariant texture features were examined. Feature selection and random forest classification were implemented within a nested cross-validation structure. Classification was evaluated with the area under receiver operating characteristic curve (AUC) considering two strategies: multiclass and one-versus-one. In the multiclass approach, 3D texture features were more discriminative than 2D features. The best results were achieved for images quantized with 32 gray-levels (AUC = 0.873 ± 0.064) using the top four features provided by the feature selection method based on the p-value. In the one-versus-one approach, high accuracy was obtained when differentiating lung cancer BM from breast cancer BM (four features, AUC = 0.963 ± 0.054) and melanoma BM (eight features, AUC = 0.936 ± 0.070) using the optimal dataset (3D features, 32 gray-levels). Classification of breast cancer and melanoma BM was unsatisfactory (AUC = 0.607 ± 0.180). Volumetric MRI texture features can be useful to differentiate brain metastases from different primary cancers after quantizing the images with the proper number of gray-levels. • Texture analysis is a promising source of biomarkers for classifying brain neoplasms. • MRI texture features of brain metastases could help identifying the primary cancer. • Volumetric texture features are more discriminative than traditional 2D texture features.

There is less MRI brain lesions and no characteristic MRI Brain findings in IIDDs patients with positive AQP4 serology among Malaysians.

PubMed

Abdullah, Suhailah; Fadzli, Farhana; Ramli, Norlisah; Tan, Chong Tin

2017-02-01

The recently introduced International Consensus diagnostic criteria for diagnosis of neuromyelitis spectrum disorder include patients who are seronegative for AQP4 antibody. The criteria are dependent on typical MRI changes in the spinal cord, optic nerve and brain. This study aims to determine whether there are significant differences in the MRI brain images between AQP4 positive and negative patients with IIDDs. MRI brain of patients with a diagnosis of IIDDs presented to the Hospital from 2010 to 2015 was analysed. The MRI was assessed by 2 radiologists blinded to the AQP4 status, on features said to be typical of NMOSD and MS. Thirty nine patients fulfilled the criteria and were included in the study. They consisted of 19 AQP4 seropositive and 20 AQP4 seronegative patients. The mean age was older (37.0 vs. 28.8 years) among the AQP4 positive group. The majority of the patients were ethnic Chinese (72%), followed by the Malays and Indians. Those with AQP4 seropositive status generally has less brain lesions, and significantly less fulfilling the McDonald DIS criteria as compared to those with AQP4 seronegative status (15.8% vs. 60.0%, p=0.005). None of the seven cerebral MRI features highlighted in NMOSD 2015 diagnostic criteria, said to be characteristic of NMOSD was more common among the AQP4 positive patients. These features were in fact seen less frequently among the AQP4 seropositive patients. An example was the extensive hemispheric lesion seen in 10.5% of AQP4 seropositive patients vs. 45% of that AQP4 seronegative group. There was no characteristic MRI brain features in the Malaysian AQP4 seropositive IIDD patients versus those who are seronegative. This could be a reflection of ethnical difference. Copyright © 2017 Elsevier B.V. All rights reserved.

The effect of feature-based attention on flanker interference processing: An fMRI-constrained source analysis.

PubMed

Siemann, Julia; Herrmann, Manfred; Galashan, Daniela

2018-01-25

The present study examined whether feature-based cueing affects early or late stages of flanker conflict processing using EEG and fMRI. Feature cues either directed participants' attention to the upcoming colour of the target or were neutral. Validity-specific modulations during interference processing were investigated using the N200 event-related potential (ERP) component and BOLD signal differences. Additionally, both data sets were integrated using an fMRI-constrained source analysis. Finally, the results were compared with a previous study in which spatial instead of feature-based cueing was applied to an otherwise identical flanker task. Feature-based and spatial attention recruited a common fronto-parietal network during conflict processing. Irrespective of attention type (feature-based; spatial), this network responded to focussed attention (valid cueing) as well as context updating (invalid cueing), hinting at domain-general mechanisms. However, spatially and non-spatially directed attention also demonstrated domain-specific activation patterns for conflict processing that were observable in distinct EEG and fMRI data patterns as well as in the respective source analyses. Conflict-specific activity in visual brain regions was comparable between both attention types. We assume that the distinction between spatially and non-spatially directed attention types primarily applies to temporal differences (domain-specific dynamics) between signals originating in the same brain regions (domain-general localization).

Improved parallel image reconstruction using feature refinement.

PubMed

Cheng, Jing; Jia, Sen; Ying, Leslie; Liu, Yuanyuan; Wang, Shanshan; Zhu, Yanjie; Li, Ye; Zou, Chao; Liu, Xin; Liang, Dong

2018-07-01

The aim of this study was to develop a novel feature refinement MR reconstruction method from highly undersampled multichannel acquisitions for improving the image quality and preserve more detail information. The feature refinement technique, which uses a feature descriptor to pick up useful features from residual image discarded by sparsity constrains, is applied to preserve the details of the image in compressed sensing and parallel imaging in MRI (CS-pMRI). The texture descriptor and structure descriptor recognizing different types of features are required for forming the feature descriptor. Feasibility of the feature refinement was validated using three different multicoil reconstruction methods on in vivo data. Experimental results show that reconstruction methods with feature refinement improve the quality of reconstructed image and restore the image details more accurately than the original methods, which is also verified by the lower values of the root mean square error and high frequency error norm. A simple and effective way to preserve more useful detailed information in CS-pMRI is proposed. This technique can effectively improve the reconstruction quality and has superior performance in terms of detail preservation compared with the original version without feature refinement. Magn Reson Med 80:211-223, 2018. © 2017 International Society for Magnetic Resonance in Medicine. © 2017 International Society for Magnetic Resonance in Medicine.

Accuracy of Ultrasonography and Magnetic Resonance Imaging in the Diagnosis of Placenta Accreta

PubMed Central

Riteau, Anne-Sophie; Tassin, Mikael; Chambon, Guillemette; Le Vaillant, Claudine; de Laveaucoupet, Jocelyne; Quéré, Marie-Pierre; Joubert, Madeleine; Prevot, Sophie; Philippe, Henri-Jean; Benachi, Alexandra

2014-01-01

Purpose To evaluate the accuracy of ultrasonography and magnetic resonance imaging (MRI) in the diagnosis of placenta accreta and to define the most relevant specific ultrasound and MRI features that may predict placental invasion. Material and Methods This study was approved by the institutional review board of the French College of Obstetricians and Gynecologists. We retrospectively reviewed the medical records of all patients referred for suspected placenta accreta to two university hospitals from 01/2001 to 05/2012. Our study population included 42 pregnant women who had been investigated by both ultrasonography and MRI. Ultrasound images and MRI were blindly reassessed for each case by 2 raters in order to score features that predict abnormal placental invasion. Results Sensitivity in the diagnosis of placenta accreta was 100% with ultrasound and 76.9% for MRI (P = 0.03). Specificity was 37.5% with ultrasonography and 50% for MRI (P = 0.6). The features of greatest sensitivity on ultrasonography were intraplacental lacunae and loss of the normal retroplacental clear space. Increased vascularization in the uterine serosa-bladder wall interface and vascularization perpendicular to the uterine wall had the best positive predictive value (92%). At MRI, uterine bulging had the best positive predictive value (85%) and its combination with the presence of dark intraplacental bands on T2-weighted images improved the predictive value to 90%. Conclusion Ultrasound imaging is the mainstay of screening for placenta accreta. MRI appears to be complementary to ultrasonography, especially when there are few ultrasound signs. PMID:24733409

Computed tomography synthesis from magnetic resonance images in the pelvis using multiple random forests and auto-context features

NASA Astrophysics Data System (ADS)

Andreasen, Daniel; Edmund, Jens M.; Zografos, Vasileios; Menze, Bjoern H.; Van Leemput, Koen

2016-03-01

In radiotherapy treatment planning that is only based on magnetic resonance imaging (MRI), the electron density information usually obtained from computed tomography (CT) must be derived from the MRI by synthesizing a so-called pseudo CT (pCT). This is a non-trivial task since MRI intensities are neither uniquely nor quantitatively related to electron density. Typical approaches involve either a classification or regression model requiring specialized MRI sequences to solve intensity ambiguities, or an atlas-based model necessitating multiple registrations between atlases and subject scans. In this work, we explore a machine learning approach for creating a pCT of the pelvic region from conventional MRI sequences without using atlases. We use a random forest provided with information about local texture, edges and spatial features derived from the MRI. This helps to solve intensity ambiguities. Furthermore, we use the concept of auto-context by sequentially training a number of classification forests to create and improve context features, which are finally used to train a regression forest for pCT prediction. We evaluate the pCT quality in terms of the voxel-wise error and the radiologic accuracy as measured by water-equivalent path lengths. We compare the performance of our method against two baseline pCT strategies, which either set all MRI voxels in the subject equal to the CT value of water, or in addition transfer the bone volume from the real CT. We show an improved performance compared to both baseline pCTs suggesting that our method may be useful for MRI-only radiotherapy.

The effects of orientation and attention during surround suppression of small image features: A 7 Tesla fMRI study.

PubMed

Schallmo, Michael-Paul; Grant, Andrea N; Burton, Philip C; Olman, Cheryl A

2016-08-01

Although V1 responses are driven primarily by elements within a neuron's receptive field, which subtends about 1° visual angle in parafoveal regions, previous work has shown that localized fMRI responses to visual elements reflect not only local feature encoding but also long-range pattern attributes. However, separating the response to an image feature from the response to the surrounding stimulus and studying the interactions between these two responses demands both spatial precision and signal independence, which may be challenging to attain with fMRI. The present study used 7 Tesla fMRI with 1.2-mm resolution to measure the interactions between small sinusoidal grating patches (targets) at 3° eccentricity and surrounds of various sizes and orientations to test the conditions under which localized, context-dependent fMRI responses could be predicted from either psychophysical or electrophysiological data. Targets were presented at 8%, 16%, and 32% contrast while manipulating (a) spatial extent of parallel (strongly suppressive) or orthogonal (weakly suppressive) surrounds, (b) locus of attention, (c) stimulus onset asynchrony between target and surround, and (d) blocked versus event-related design. In all experiments, the V1 fMRI signal was lower when target stimuli were flanked by parallel versus orthogonal context. Attention amplified fMRI responses to all stimuli but did not show a selective effect on central target responses or a measurable effect on orientation-dependent surround suppression. Suppression of the V1 fMRI response by parallel surrounds was stronger than predicted from psychophysics but showed a better match to previous electrophysiological reports.

Fine-tuning convolutional deep features for MRI based brain tumor classification

NASA Astrophysics Data System (ADS)

Ahmed, Kaoutar B.; Hall, Lawrence O.; Goldgof, Dmitry B.; Liu, Renhao; Gatenby, Robert A.

2017-03-01

Prediction of survival time from brain tumor magnetic resonance images (MRI) is not commonly performed and would ordinarily be a time consuming process. However, current cross-sectional imaging techniques, particularly MRI, can be used to generate many features that may provide information on the patient's prognosis, including survival. This information can potentially be used to identify individuals who would benefit from more aggressive therapy. Rather than using pre-defined and hand-engineered features as with current radiomics methods, we investigated the use of deep features extracted from pre-trained convolutional neural networks (CNNs) in predicting survival time. We also provide evidence for the power of domain specific fine-tuning in improving the performance of a pre-trained CNN's, even though our data set is small. We fine-tuned a CNN initially trained on a large natural image recognition dataset (Imagenet ILSVRC) and transferred the learned feature representations to the survival time prediction task, obtaining over 81% accuracy in a leave one out cross validation.

Multiclass Classification for the Differential Diagnosis on the ADHD Subtypes Using Recursive Feature Elimination and Hierarchical Extreme Learning Machine: Structural MRI Study

PubMed Central

Qureshi, Muhammad Naveed Iqbal; Min, Beomjun; Jo, Hang Joon; Lee, Boreom

2016-01-01

The classification of neuroimaging data for the diagnosis of certain brain diseases is one of the main research goals of the neuroscience and clinical communities. In this study, we performed multiclass classification using a hierarchical extreme learning machine (H-ELM) classifier. We compared the performance of this classifier with that of a support vector machine (SVM) and basic extreme learning machine (ELM) for cortical MRI data from attention deficit/hyperactivity disorder (ADHD) patients. We used 159 structural MRI images of children from the publicly available ADHD-200 MRI dataset. The data consisted of three types, namely, typically developing (TDC), ADHD-inattentive (ADHD-I), and ADHD-combined (ADHD-C). We carried out feature selection by using standard SVM-based recursive feature elimination (RFE-SVM) that enabled us to achieve good classification accuracy (60.78%). In this study, we found the RFE-SVM feature selection approach in combination with H-ELM to effectively enable the acquisition of high multiclass classification accuracy rates for structural neuroimaging data. In addition, we found that the most important features for classification were the surface area of the superior frontal lobe, and the cortical thickness, volume, and mean surface area of the whole cortex. PMID:27500640

Multiclass Classification for the Differential Diagnosis on the ADHD Subtypes Using Recursive Feature Elimination and Hierarchical Extreme Learning Machine: Structural MRI Study.

PubMed

Qureshi, Muhammad Naveed Iqbal; Min, Beomjun; Jo, Hang Joon; Lee, Boreom

2016-01-01

The classification of neuroimaging data for the diagnosis of certain brain diseases is one of the main research goals of the neuroscience and clinical communities. In this study, we performed multiclass classification using a hierarchical extreme learning machine (H-ELM) classifier. We compared the performance of this classifier with that of a support vector machine (SVM) and basic extreme learning machine (ELM) for cortical MRI data from attention deficit/hyperactivity disorder (ADHD) patients. We used 159 structural MRI images of children from the publicly available ADHD-200 MRI dataset. The data consisted of three types, namely, typically developing (TDC), ADHD-inattentive (ADHD-I), and ADHD-combined (ADHD-C). We carried out feature selection by using standard SVM-based recursive feature elimination (RFE-SVM) that enabled us to achieve good classification accuracy (60.78%). In this study, we found the RFE-SVM feature selection approach in combination with H-ELM to effectively enable the acquisition of high multiclass classification accuracy rates for structural neuroimaging data. In addition, we found that the most important features for classification were the surface area of the superior frontal lobe, and the cortical thickness, volume, and mean surface area of the whole cortex.

Magnetic resonance imaging is more sensitive than radiographs in detecting change in size of erosions in rheumatoid arthritis.

PubMed

Chen, Timothy S; Crues, John V; Ali, Muhammad; Troum, Orrin M

2006-10-01

To evaluate the technological performance of magnetic resonance imaging (MRI) with respect to projection radiography by determining the incidence of changes in the size of individual bone lesions in inflammatory arthritis, using serial high-resolution in-office MRI over short time intervals (8 months average followup), and by comparing the sensitivity of 3-view projection radiography with in-office MRI for detecting changes in size and number of individual erosions. MR examinations of the wrists and second and third metacarpophalangeal joints were performed using a portable in-office MR system in a total of 405 patients with inflammatory arthritis, from one rheumatologist's practice, who were undergoing aggressive disease modifying antirheumatic drug therapy. Of the patients, 156 were imaged at least twice, allowing evaluation of 246 followup examinations (mean followup interval of 8 months over a 2-year period). Baseline and followup plain radiographs were obtained in 165 patient intervals. Patients refused radiographic examination on 81 followup visits. MRI demonstrated no detectable changes in 124 of the 246 (50%) followup MRI examinations. An increase in the size or number of erosions was demonstrated in 74 (30%) examinations, a decrease in the size or number of erosions in 36 (15%), and both increases and decreases in erosions were seen in 11 (4%). In the 165 studies with followup radiographic comparisons, only one examination (0.8%) showed an erosion not seen on the prior examination and one (0.8%) showed an increase in a previously noted erosion. We showed that high-resolution in-office MRI with an average followup of 8 months detects changes in bony disease in 50% of compliant patients during aggressive treatment for inflammatory arthritis in a single rheumatologist's office practice. Plain radiography is insensitive for detecting changes in bone erosions for this patient population in this time frame.

MRI for the detection of calcific features of vertebral haemangioma.

PubMed

Bender, Y Y; Böker, S M; Diederichs, G; Walter, T; Wagner, M; Fallenberg, E; Liebig, T; Rickert, M; Hamm, B; Makowski, M R

2017-08-01

To evaluate the diagnostic performance of susceptibility-weighted-magnetic-resonance imaging (SW-MRI) for the detection of vertebral haemangiomas (VHs) compared to T1/T2-weighted MRI sequences, radiographs, and computed tomography (CT). The study was approved by the local ethics review board. An SW-MRI sequence was added to the clinical spine imaging protocol. The image-based diagnosis of 56 VHs in 46 patients was established using T1/T2 MRI in combination with radiography/CT as the reference standard. VHs were assessed based on T1/T2-weighted MRI images alone and in combination with SW-MRI, while radiographs/CT images were excluded from the analysis. Fifty-one of 56 VHs could be identified on T1/T2 MRI images alone, if radiographs/CT images were excluded from analysis. In five cases (9.1%), additional radiographs/CT images were required for the imaging-based diagnosis. If T1/T2 and SW-MRI images were used in combination, all VHs could be diagnosed, without the need for radiography/CT. Size measurements revealed a close correlation between CT and SW-MRI (R 2 =0.94; p<0.05). This study demonstrates that SW-MRI enables reliable detection of the typical calcified features of VHs. This is of importance for routine MRI of the spine, as the use of additional CT/radiography can be minimized. Copyright © 2017 The Royal College of Radiologists. Published by Elsevier Ltd. All rights reserved.

Travel Burden to Breast MRI and Utilization: Are Risk and Sociodemographics Related?

PubMed

Onega, Tracy; Lee, Christoph I; Benkeser, David; Alford-Teaster, Jennifer; Haas, Jennifer S; Tosteson, Anna N A; Hill, Deirdre; Shi, Xun; Henderson, Louise M; Hubbard, Rebecca A

2016-06-01

Mammography, unlike MRI, is relatively geographically accessible. Additional travel time is often required to access breast MRI. However, the amount of additional travel time and whether it varies on the basis of sociodemographic or breast cancer risk factors is unknown. The investigators examined screening mammography and MRI between 2005 and 2012 in the Breast Cancer Surveillance Consortium by (1) travel time to the closest and actual mammography facility used and the difference between the two, (2) women's breast cancer risk factors, and (3) sociodemographic characteristics. Logistic regression was used to examine the odds of traveling farther than the closest facility in relation to women's characteristics. Among 821,683 screening mammographic examinations, 76.6% occurred at the closest facility, compared with 51.9% of screening MRI studies (n = 3,687). The median differential travel time among women not using the closest facility for mammography was 14 min (interquartile range, 8-25 min) versus 20 min (interquartile range, 11-40 min) for breast MRI. Differential travel time for both imaging modalities did not vary notably by breast cancer risk factors but was significantly longer for nonurban residents. For non-Hispanic black compared with non-Hispanic white women, the adjusted odds of traveling farther than the closest facility were 9% lower for mammography (odds ratio, 0.91; 95% confidence interval, 0.87-0.95) but more than two times higher for MRI (odds ratio, 2.64; 95% confidence interval, 1.36-5.13). Breast cancer risk factors were not related to excess travel time for screening MRI, but sociodemographic factors were, suggesting the possibility that geographic distribution of advanced imaging may exacerbated disparities for some vulnerable populations. Copyright © 2016 American College of Radiology. Published by Elsevier Inc. All rights reserved.

Neuroimaging and Neurodevelopmental Outcome in Extremely Preterm Infants

PubMed Central

Barnes, Patrick D.; Bulas, Dorothy; Slovis, Thomas L.; Finer, Neil N.; Wrage, Lisa A.; Das, Abhik; Tyson, Jon E.; Stevenson, David K.; Carlo, Waldemar A.; Walsh, Michele C.; Laptook, Abbot R.; Yoder, Bradley A.; Van Meurs, Krisa P.; Faix, Roger G.; Rich, Wade; Newman, Nancy S.; Cheng, Helen; Heyne, Roy J.; Vohr, Betty R.; Acarregui, Michael J.; Vaucher, Yvonne E.; Pappas, Athina; Peralta-Carcelen, Myriam; Wilson-Costello, Deanne E.; Evans, Patricia W.; Goldstein, Ricki F.; Myers, Gary J.; Poindexter, Brenda B.; McGowan, Elisabeth C.; Adams-Chapman, Ira; Fuller, Janell; Higgins, Rosemary D.

2015-01-01

BACKGROUND: Extremely preterm infants are at risk for neurodevelopmental impairment (NDI). Early cranial ultrasound (CUS) is usual practice, but near-term brain MRI has been reported to better predict outcomes. We prospectively evaluated MRI white matter abnormality (WMA) and cerebellar lesions, and serial CUS adverse findings as predictors of outcomes at 18 to 22 months’ corrected age. METHODS: Early and late CUS, and brain MRI were read by masked central readers, in a large cohort (n = 480) of infants <28 weeks’ gestation surviving to near term in the Neonatal Research Network. Outcomes included NDI or death after neuroimaging, and significant gross motor impairment or death, with NDI defined as cognitive composite score <70, significant gross motor impairment, and severe hearing or visual impairment. Multivariable models evaluated the relative predictive value of neuroimaging while controlling for other factors. RESULTS: Of 480 infants, 15 died and 20 were lost. Increasing severity of WMA and significant cerebellar lesions on MRI were associated with adverse outcomes. Cerebellar lesions were rarely identified by CUS. In full multivariable models, both late CUS and MRI, but not early CUS, remained independently associated with NDI or death (MRI cerebellar lesions: odds ratio, 3.0 [95% confidence interval: 1.3–6.8]; late CUS: odds ratio, 9.8 [95% confidence interval: 2.8–35]), and significant gross motor impairment or death. In models that did not include late CUS, MRI moderate-severe WMA was independently associated with adverse outcomes. CONCLUSIONS: Both late CUS and near-term MRI abnormalities were associated with outcomes, independent of early CUS and other factors, underscoring the relative prognostic value of near-term neuroimaging. PMID:25554820

Framework for 3D histologic reconstruction and fusion with in vivo MRI: Preliminary results of characterizing pulmonary inflammation in a mouse model

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

Rusu, Mirabela, E-mail: mirabela.rusu@gmail.com; Wang, Haibo; Madabhushi, Anant

Purpose: Pulmonary inflammation is associated with a variety of diseases. Assessing pulmonary inflammation on in vivo imaging may facilitate the early detection and treatment of lung diseases. Although routinely used in thoracic imaging, computed tomography has thus far not been compellingly shown to characterize inflammation in vivo. Alternatively, magnetic resonance imaging (MRI) is a nonionizing radiation technique to better visualize and characterize pulmonary tissue. Prior to routine adoption of MRI for early characterization of inflammation in humans, a rigorous and quantitative characterization of the utility of MRI to identify inflammation is required. Such characterization may be achieved by considering exmore » vivo histology as the ground truth, since it enables the definitive spatial assessment of inflammation. In this study, the authors introduce a novel framework to integrate 2D histology, ex vivo and in vivo imaging to enable the mapping of the extent of disease from ex vivo histology onto in vivo imaging, with the goal of facilitating computerized feature analysis and interrogation of disease appearance on in vivo imaging. The authors’ framework was evaluated in a preclinical preliminary study aimed to identify computer extracted features on in vivo MRI associated with chronic pulmonary inflammation. Methods: The authors’ image analytics framework first involves reconstructing the histologic volume in 3D from individual histology slices. Second, the authors map the disease ground truth onto in vivo MRI via coregistration with 3D histology using the ex vivo lung MRI as a conduit. Finally, computerized feature analysis of the disease extent is performed to identify candidate in vivo imaging signatures of disease presence and extent. Results: The authors evaluated the framework by assessing the quality of the 3D histology reconstruction and the histology—MRI fusion, in the context of an initial use case involving characterization of chronic inflammation in a mouse model. The authors’ evaluation considered three mice, two with an inflammation phenotype and one control. The authors’ iterative 3D histology reconstruction yielded a 70.1% ± 2.7% overlap with the ex vivo MRI volume. Across a total of 17 anatomic landmarks manually delineated at the division of airways, the target registration error between the ex vivo MRI and 3D histology reconstruction was 0.85 ± 0.44 mm, suggesting that a good alignment of the ex vivo 3D histology and ex vivo MRI had been achieved. The 3D histology-in vivo MRI coregistered volumes resulted in an overlap of 73.7% ± 0.9%. Preliminary computerized feature analysis was performed on an additional four control mice, for a total of seven mice considered in this study. Gabor texture filters appeared to best capture differences between the inflamed and noninflamed regions on MRI. Conclusions: The authors’ 3D histology reconstruction and multimodal registration framework were successfully employed to reconstruct the histology volume of the lung and fuse it with in vivo MRI to create a ground truth map for inflammation on in vivo MRI. The analytic platform presented here lays the framework for a rigorous validation of the identified imaging features for chronic lung inflammation on MRI in a large prospective cohort.« less

Automated discrimination of dementia spectrum disorders using extreme learning machine and structural T1 MRI features.

PubMed

Jongin Kim; Boreom Lee

2017-07-01

The classification of neuroimaging data for the diagnosis of Alzheimer's Disease (AD) is one of the main research goals of the neuroscience and clinical fields. In this study, we performed extreme learning machine (ELM) classifier to discriminate the AD, mild cognitive impairment (MCI) from normal control (NC). We compared the performance of ELM with that of a linear kernel support vector machine (SVM) for 718 structural MRI images from Alzheimer's Disease Neuroimaging Initiative (ADNI) database. The data consisted of normal control, MCI converter (MCI-C), MCI non-converter (MCI-NC), and AD. We employed SVM-based recursive feature elimination (RFE-SVM) algorithm to find the optimal subset of features. In this study, we found that the RFE-SVM feature selection approach in combination with ELM shows the superior classification accuracy to that of linear kernel SVM for structural T1 MRI data.

Topologic analysis and comparison of brain activation in children with epilepsy versus controls: an fMRI study

NASA Astrophysics Data System (ADS)

Oweis, Khalid J.; Berl, Madison M.; Gaillard, William D.; Duke, Elizabeth S.; Blackstone, Kaitlin; Loew, Murray H.; Zara, Jason M.

2010-03-01

This paper describes the development of novel computer-aided analysis algorithms to identify the language activation patterns at a certain Region of Interest (ROI) in Functional Magnetic Resonance Imaging (fMRI). Previous analysis techniques have been used to compare typical and pathologic activation patterns in fMRI images resulting from identical tasks but none of them analyzed activation topographically in a quantitative manner. This paper presents new analysis techniques and algorithms capable of identifying a pattern of language activation associated with localization related epilepsy. fMRI images of 64 healthy individuals and 31 patients with localization related epilepsy have been studied and analyzed on an ROI basis. All subjects are right handed with normal MRI scans and have been classified into three age groups (4-6, 7-9, 10-12 years). Our initial efforts have focused on investigating activation in the Left Inferior Frontal Gyrus (LIFG). A number of volumetric features have been extracted from the data. The LIFG has been cut into slices and the activation has been investigated topographically on a slice by slice basis. Overall, a total of 809 features have been extracted, and correlation analysis was applied to eliminate highly correlated features. Principal Component analysis was then applied to account only for major components in the data and One-Way Analysis of Variance (ANOVA) has been applied to test for significantly different features between normal and patient groups. Twenty Nine features have were found to be significantly different (p<0.05) between patient and control groups

Hepatic capsular retraction: spectrum of diagnosis at MRI

PubMed Central

Mons, Antoine; Braidy, Chadi; Montoriol, Pierre François; Garcier, Jean-Marc; Vilgrain, Valérie

2014-01-01

Hepatic capsular retraction is an imaging feature that deserves the attention of the radiologist. Hepatic capsular retraction is associated with a number of hepatic lesions, benign or malignant, treated or untreated. The purpose of this pictorial review is to discuss the most common benign and malignant hepatic lesions associated with this feature with an emphasis on magnetic resonance imaging (MRI). PMID:25535571

Magnetic resonance imaging criteria for thrombolysis in hyperacute cerebral infarction.

PubMed

Ahmetgjekaj, Ilir; Kabashi-Muçaj, Serbeze; Lascu, Luana Corina; Kabashi, Antigona; Bondari, A; Bondari, Simona; Dedushi-Hoti, Kreshnike; Biçaku, Ardian; Shatri, Jeton

2014-01-01

Selection of patients with cerebral infarction for MRI that is suitable for thrombolytic therapy as an emerging application. Although the efficiency of the therapy with i.v. tissue plasminogen activator (tPA) within 3 hours after onset of symptoms has been proven in selected patients with CT, now these criteria are determined by MRI, as the data we gather are fast and accurate in the first hours. MRI screening in patients with acute cerebral infarction before application of thrombolytic therapy was done in a UCC Mannheim in Germany. Unlike trials with CT, MRI studies demonstrated the benefits of therapy up to 6 hours after the onset of symptoms. We studied 21 patients hospitalized in Clinic of Neuroradiology at University Clinical Centre in Mannheim-Germany. They all undergo brain MRI evaluation for stroke. This article reviews literature that has followed application of thrombolysis in patients with cerebral infarction based on MRI. We have analyzed the MRI criteria for i.v. application of tPA at this University Centre. Alongside the personal viewpoints of clinicians, survey reveals a variety of clinical aspects and MRI features that are opened for further more exploration: therapeutic effects, the use of the MRI angiography, dynamics, and other. MRI is a tested imaging method for rapid evaluation of patients with hyperacute cerebral infarction, replacing the use of CT imaging and clinical features. MRI criteria for thrombolytic therapy are being applied in some cerebral vascular centres. In Kosovo, the application of thrombolytic therapy has not started yet.

Pathologic Findings of Breast Lesions Detected on Magnetic Resonance Imaging.

PubMed

Jabbar, Seema B; Lynch, Beverly; Seiler, Stephen; Hwang, Helena; Sahoo, Sunati

2017-11-01

- Breast magnetic resonance imaging (MRI) is now used routinely for high-risk screening and in the evaluation of the extent of disease in newly diagnosed breast cancer patients. Morphologic characteristics and the kinetic pattern largely determine how suspicious a breast lesion is on MRI. Because of its high sensitivity, MRI identifies a large number of suspicious lesions. However, the low to moderate specificity and the additional cost have raised questions regarding its frequent use. - To identify the pathologic entities that frequently present as suspicious enhancing lesions and to identify specific MRI characteristics that may be predictive of malignancy. - One hundred seventy-seven MRI-guided biopsies from 152 patients were included in the study. The indication for MRI, MRI features, pathologic findings, and patient demographics were recorded. The MRI findings and the pathology slides were reviewed by a dedicated breast radiologist and breast pathologists. - Seventy-one percent (126 of 177) of MRI-guided breast biopsies were benign, 11% (20 of 177) showed epithelial atypia, and 18% (31 of 177) showed malignancy. The vast majority (84%; 62 of 74) of MRI lesions with persistent kinetics were benign. However, 57% (17 of 30) of lesions with washout kinetics and 65% (62 of 95) of mass lesions were also benign. - Magnetic resonance imaging detects malignancies undetected by other imaging modalities but also detects a wide variety of benign lesions. Benign and malignant lesions identified by MRI share similar morphologic and kinetic features, necessitating biopsy for histologic confirmation.

A survey on abnormal uterine bleeding among radiographers with frequent MRI exposure using intrauterine contraceptive devices.

PubMed

Huss, A; Schaap, K; Kromhout, H

2018-02-01

Based on a previous case report of menometrorrhagia (prolonged/excessive uterine bleeding, occurring at irregular and/or frequent intervals) in MRI workers with intrauterine devices (IUDs), it was evaluated whether this association could be confirmed. A survey was performed among 381 female radiographers registered with their national association. Logistic regression was used to analyze associations of abnormal uterine bleeding with the frequency of working with MRI scanners, presence near the scanner/in the scanner room during image acquisition, and with scanner strength or type. A total of 68 women reported using IUDs, and 72 reported abnormal uterine bleeding. Compared with unexposed women not using IUDs, the odds ratio in women with IUDs working with MRI scanners was 2.09 (95% confidence interval 0.83-3.66). Associations were stronger if women working with MRI reported being present during image acquisition (odds ratio 3.43, 95% CI 1.26-9.34). Associations with scanner strength or type were not consistent. Radiographers using IUDs who are occupationally exposed to stray fields from MRI scanners report abnormal uterine bleeding more often than their co-workers without an IUD, or nonexposed co-workers with an IUD. In particular, radiographers present inside the scanner room during image acquisition showed an increased risk. Magn Reson Med 79:1083-1089, 2018. © 2017 International Society for Magnetic Resonance in Medicine. © 2017 International Society for Magnetic Resonance in Medicine.

Unsupervised spatiotemporal analysis of fMRI data using graph-based visualizations of self-organizing maps.

PubMed

Katwal, Santosh B; Gore, John C; Marois, Rene; Rogers, Baxter P

2013-09-01

We present novel graph-based visualizations of self-organizing maps for unsupervised functional magnetic resonance imaging (fMRI) analysis. A self-organizing map is an artificial neural network model that transforms high-dimensional data into a low-dimensional (often a 2-D) map using unsupervised learning. However, a postprocessing scheme is necessary to correctly interpret similarity between neighboring node prototypes (feature vectors) on the output map and delineate clusters and features of interest in the data. In this paper, we used graph-based visualizations to capture fMRI data features based upon 1) the distribution of data across the receptive fields of the prototypes (density-based connectivity); and 2) temporal similarities (correlations) between the prototypes (correlation-based connectivity). We applied this approach to identify task-related brain areas in an fMRI reaction time experiment involving a visuo-manual response task, and we correlated the time-to-peak of the fMRI responses in these areas with reaction time. Visualization of self-organizing maps outperformed independent component analysis and voxelwise univariate linear regression analysis in identifying and classifying relevant brain regions. We conclude that the graph-based visualizations of self-organizing maps help in advanced visualization of cluster boundaries in fMRI data enabling the separation of regions with small differences in the timings of their brain responses.

Lumbar spinal canal MRI diameter is smaller in herniated disc cauda equina syndrome patients

PubMed Central

Kruit, Mark C.; Peul, Wilco C.; Vleggeert-Lankamp, Carmen L. A.

2017-01-01

Introduction Correlation between magnetic resonance imaging (MRI) and clinical features in cauda equina syndrome (CES) is unknown; nor is known whether there are differences in MRI spinal canal size between lumbar herniated disc patients with CES versus lumbar herniated discs patients without CES, operated for sciatica. The aims of this study are 1) evaluating the association of MRI features with clinical presentation and outcome of CES and 2) comparing lumbar spinal canal diameters of lumbar herniated disc patients with CES versus lumbar herniated disc patients without CES, operated because of sciatica. Methods MRIs of CES patients were assessed for the following features: level of disc lesion, type (uni- or bilateral) and severity of caudal compression. Pre- and postoperative clinical features (micturition dysfunction, defecation dysfunction, altered sensation of the saddle area) were retrieved from the medical files. In addition, anteroposterior (AP) lumbar spinal canal diameters of CES patients were measured at MRI. AP diameters of lumbar herniated disc patients without CES, operated for sciatica, were measured for comparison. Results 48 CES patients were included. At MRI, bilateral compression was seen in 82%; complete caudal compression in 29%. MRI features were not associated with clinical presentation nor outcome. AP diameter was measured for 26 CES patients and for 31 lumbar herniated disc patients without CES, operated for sciatica. Comparison displayed a significant smaller AP diameter of the lumbar spinal canal in CES patients (largest p = 0.002). Compared to average diameters in literature, diameters of CES patients were significantly more often below average than that of the sciatica patients (largest p = 0.021). Conclusion This is the first study demonstrating differences in lumbar spinal canal size between lumbar herniated disc patients with CES and lumbar herniated disc patients without CES, operated for sciatica. This finding might imply that lumbar herniated disc patients with a relative small lumbar spinal canal might need to be approached differently in managing complaints of herniated disc. Since the number of studied patients is relatively small, further research should be conducted before clinical consequences are considered. PMID:29023556

Association between dynamic features of breast DCE-MR imaging and clinical response of neoadjuvant chemotherapy: a preliminary analysis

NASA Astrophysics Data System (ADS)

Huang, Lijuan; Fan, Ming; Li, Lihua; Zhang, Juan; Shao, Guoliang; Zheng, Bin

2016-03-01

Neoadjuvant chemotherapy (NACT) is being used increasingly in the management of patients with breast cancer for systemically reducing the size of primary tumor before surgery in order to improve survival. The clinical response of patients to NACT is correlated with reduced or abolished of their primary tumor, which is important for treatment in the next stage. Recently, the dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is used for evaluation of the response of patients to NACT. To measure this correlation, we extracted the dynamic features from the DCE- MRI and performed association analysis between these features and the clinical response to NACT. In this study, 59 patients are screened before NATC, of which 47 are complete or partial response, and 12 are no response. We segmented the breast areas depicted on each MR image by a computer-aided diagnosis (CAD) scheme, registered images acquired from the sequential MR image scan series, and calculated eighteen features extracted from DCE-MRI. We performed SVM with the 18 features for classification between patients of response and no response. Furthermore, 6 of the 18 features are selected to refine the classification by using Genetic Algorithm. The accuracy, sensitivity and specificity are 87%, 95.74% and 50%, respectively. The calculated area under a receiver operating characteristic (ROC) curve is 0.79+/-0.04. This study indicates that the features of DCE-MRI of breast cancer are associated with the response of NACT. Therefore, our method could be helpful for evaluation of NACT in treatment of breast cancer.

Magnetic Resonance Imaging–Guided versus Surrogate-Based Motion Tracking in Liver Radiation Therapy: A Prospective Comparative Study

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

Paganelli, Chiara, E-mail: chiara.paganelli@polimi.it; Seregni, Matteo; Fattori, Giovanni

Purpose: This study applied automatic feature detection on cine–magnetic resonance imaging (MRI) liver images in order to provide a prospective comparison between MRI-guided and surrogate-based tracking methods for motion-compensated liver radiation therapy. Methods and Materials: In a population of 30 subjects (5 volunteers plus 25 patients), 2 oblique sagittal slices were acquired across the liver at high temporal resolution. An algorithm based on scale invariant feature transform (SIFT) was used to extract and track multiple features throughout the image sequence. The position of abdominal markers was also measured directly from the image series, and the internal motion of each featuremore » was quantified through multiparametric analysis. Surrogate-based tumor tracking with a state-of-the-art external/internal correlation model was simulated. The geometrical tracking error was measured, and its correlation with external motion parameters was also investigated. Finally, the potential gain in tracking accuracy relying on MRI guidance was quantified as a function of the maximum allowed tracking error. Results: An average of 45 features was extracted for each subject across the whole liver. The multi-parametric motion analysis reported relevant inter- and intrasubject variability, highlighting the value of patient-specific and spatially-distributed measurements. Surrogate-based tracking errors (relative to the motion amplitude) were were in the range 7% to 23% (1.02-3.57mm) and were significantly influenced by external motion parameters. The gain of MRI guidance compared to surrogate-based motion tracking was larger than 30% in 50% of the subjects when considering a 1.5-mm tracking error tolerance. Conclusions: Automatic feature detection applied to cine-MRI allows detailed liver motion description to be obtained. Such information was used to quantify the performance of surrogate-based tracking methods and to provide a prospective comparison with respect to MRI-guided radiation therapy, which could support the definition of patient-specific optimal treatment strategies.« less

Development and reliability of a preliminary Foot Osteoarthritis Magnetic Resonance Imaging Score

PubMed Central

Halstead, Jill; Martín-Hervás, Carmen; Hensor, Elizabeth MA; McGonagle, Dennis; Keenan, Anne-Maree

2017-01-01

Objective Foot osteoarthritis (OA) is very common but under-investigated musculoskeletal condition and there is little consensus as to common MRI imaging features. The aim of this study was to develop a preliminary foot OA MRI score (FOAMRIS) and evaluate its reliability. Methods This preliminary semi-quantitative score included the hindfoot, midfoot and metatarsophalangeal joints. Joints were scored for joint space narrowing (JSN, 0-3), osteophytes (0-3), joint effusion-synovitis and bone cysts (present/absent). Erosions and bone marrow lesions (BMLs) were scored (0-3) and BMLs were evaluated adjacent to entheses and at sub-tendon sites (present/absent). Additionally, tenosynovitis was scored (0-3) and midfoot ligament pathology was scored (present/absent). Reliability was evaluated in 15 people with foot pain and MRI-detected OA using 3.0T MRI multi-sequence protocols and assessed using intraclass correlation coefficients (ICC) as an overall score and per anatomical site (see supplementary data). Results Intra-reader agreement (ICC) was generally good to excellent across the foot in joint features (JSN 0.94, osteophytes 0.94, effusion-synovitis 0.62 and cysts 0.93), bone features (BML 0.89, erosion 0.78, BML-entheses 0.79, BML sub-tendon 0.75) and soft-tissue features (tenosynovitis 0.90, ligaments 0.87). Inter-reader agreement was lower for joint features (JSN 0.60, osteophytes 0.41, effusion-synovitis 0.03) and cysts 0.65, bone features (BML 0.80, erosion 0.00, BML-entheses 0.49, BML sub-tendon -0.24) and soft-tissue features (tenosynovitis 0.48, ligaments 0.50). Conclusion This preliminary FOAMRIS demonstrated good intra-reader reliability and fair inter-reader reliability when assessing the total feature scores. Further development is required in cohorts with a range of pathologies and to assess the psychometric measurement properties. PMID:28572462

Empirical evaluation of cross-site reproducibility in radiomic features for characterizing prostate MRI

NASA Astrophysics Data System (ADS)

Chirra, Prathyush; Leo, Patrick; Yim, Michael; Bloch, B. Nicolas; Rastinehad, Ardeshir R.; Purysko, Andrei; Rosen, Mark; Madabhushi, Anant; Viswanath, Satish

2018-02-01

The recent advent of radiomics has enabled the development of prognostic and predictive tools which use routine imaging, but a key question that still remains is how reproducible these features may be across multiple sites and scanners. This is especially relevant in the context of MRI data, where signal intensity values lack tissue specific, quantitative meaning, as well as being dependent on acquisition parameters (magnetic field strength, image resolution, type of receiver coil). In this paper we present the first empirical study of the reproducibility of 5 different radiomic feature families in a multi-site setting; specifically, for characterizing prostate MRI appearance. Our cohort comprised 147 patient T2w MRI datasets from 4 different sites, all of which were first pre-processed to correct acquisition-related for artifacts such as bias field, differing voxel resolutions, as well as intensity drift (non-standardness). 406 3D voxel wise radiomic features were extracted and evaluated in a cross-site setting to determine how reproducible they were within a relatively homogeneous non-tumor tissue region; using 2 different measures of reproducibility: Multivariate Coefficient of Variation and Instability Score. Our results demonstrated that Haralick features were most reproducible between all 4 sites. By comparison, Laws features were among the least reproducible between sites, as well as performing highly variably across their entire parameter space. Similarly, the Gabor feature family demonstrated good cross-site reproducibility, but for certain parameter combinations alone. These trends indicate that despite extensive pre-processing, only a subset of radiomic features and associated parameters may be reproducible enough for use within radiomics-based machine learning classifier schemes.

Optic Nerve Assessment Using 7-Tesla Magnetic Resonance Imaging.

PubMed

Singh, Arun D; Platt, Sean M; Lystad, Lisa; Lowe, Mark; Oh, Sehong; Jones, Stephen E; Alzahrani, Yahya; Plesec, Thomas

2016-04-01

The purpose of this study was to correlate high-resolution magnetic resonance imaging (MRI) and histologic findings in a case of juxtapapillary choroidal melanoma with clinical evidence of optic nerve invasion. With institutional review board approval, an enucleated globe with choroidal melanoma and optic nerve invasion was imaged using a 7-tesla MRI followed by histopathologic evaluation. Optical coherence tomography, B-scan ultrasonography, and 1.5-tesla MRI of the orbit (1-mm sections) could not detect optic disc invasion. Ex vivo, 7-tesla MRI detected optic nerve invasion, which correlated with histopathologic features. Our case demonstrates the potential to document the existence of optic nerve invasion in the presence of an intraocular tumor, a feature that has a major bearing on decision making, particularly for consideration of enucleation.

A pathophysiologic approach for subacute encephalopathy with seizures in alcoholics (SESA) syndrome.

PubMed

Choi, Jun Yong; Kwon, Jiwon; Bae, Eun-Kee

2014-09-01

Subacute encephalopathy with seizures in alcoholics (SESA) syndrome is a unique disease entity characterized by typical clinical and electroencephalographic (EEG) features in the setting of chronic alcoholism. We present two patients with distinctive serial MRI and EEG findings which suggest a clue to the underlying pathophysiologic mechanisms of SESA syndrome. Two patients with chronic alcoholism and alcoholic liver cirrhosis presented with generalized seizures and confused mental status. Brain MRI demonstrated restricted diffusion, increased T2-weighted signal intensity, and hyperperfusion in the presumed seizure focus and nearby posterior regions of the cerebral hemispheres. EEG showed periodic lateralized epileptiform discharges which were prominent in the posterior regions of the cerebral hemispheres ipsilateral to the side of brain MRI abnormalities. Even after patients clinically improved, these brain abnormalities persisted with progressive atrophic changes on follow-up brain MRI. These patients had not only the distinguishing clinical and EEG features of SESA syndrome, but also showed novel brain MRI abnormalities. These changes on MRI displayed characteristics of seizure-related changes. The posterior dominance of abnormalities on MRI and EEG suggests that the pathophysiologic mechanisms of SESA syndrome may share those of posterior reversible encephalopathy syndrome. Copyright © 2014 Elsevier Ltd. All rights reserved.

Predicting conversion from MCI to AD using resting-state fMRI, graph theoretical approach and SVM.

PubMed

Hojjati, Seyed Hani; Ebrahimzadeh, Ata; Khazaee, Ali; Babajani-Feremi, Abbas

2017-04-15

We investigated identifying patients with mild cognitive impairment (MCI) who progress to Alzheimer's disease (AD), MCI converter (MCI-C), from those with MCI who do not progress to AD, MCI non-converter (MCI-NC), based on resting-state fMRI (rs-fMRI). Graph theory and machine learning approach were utilized to predict progress of patients with MCI to AD using rs-fMRI. Eighteen MCI converts (average age 73.6 years; 11 male) and 62 age-matched MCI non-converters (average age 73.0 years, 28 male) were included in this study. We trained and tested a support vector machine (SVM) to classify MCI-C from MCI-NC using features constructed based on the local and global graph measures. A novel feature selection algorithm was developed and utilized to select an optimal subset of features. Using subset of optimal features in SVM, we classified MCI-C from MCI-NC with an accuracy, sensitivity, specificity, and the area under the receiver operating characteristic (ROC) curve of 91.4%, 83.24%, 90.1%, and 0.95, respectively. Furthermore, results of our statistical analyses were used to identify the affected brain regions in AD. To the best of our knowledge, this is the first study that combines the graph measures (constructed based on rs-fMRI) with machine learning approach and accurately classify MCI-C from MCI-NC. Results of this study demonstrate potential of the proposed approach for early AD diagnosis and demonstrate capability of rs-fMRI to predict conversion from MCI to AD by identifying affected brain regions underlying this conversion. Copyright © 2017 Elsevier B.V. All rights reserved.

Hierarchical Neural Representation of Dreamed Objects Revealed by Brain Decoding with Deep Neural Network Features.

PubMed

Horikawa, Tomoyasu; Kamitani, Yukiyasu

2017-01-01

Dreaming is generally thought to be generated by spontaneous brain activity during sleep with patterns common to waking experience. This view is supported by a recent study demonstrating that dreamed objects can be predicted from brain activity during sleep using statistical decoders trained with stimulus-induced brain activity. However, it remains unclear whether and how visual image features associated with dreamed objects are represented in the brain. In this study, we used a deep neural network (DNN) model for object recognition as a proxy for hierarchical visual feature representation, and DNN features for dreamed objects were analyzed with brain decoding of fMRI data collected during dreaming. The decoders were first trained with stimulus-induced brain activity labeled with the feature values of the stimulus image from multiple DNN layers. The decoders were then used to decode DNN features from the dream fMRI data, and the decoded features were compared with the averaged features of each object category calculated from a large-scale image database. We found that the feature values decoded from the dream fMRI data positively correlated with those associated with dreamed object categories at mid- to high-level DNN layers. Using the decoded features, the dreamed object category could be identified at above-chance levels by matching them to the averaged features for candidate categories. The results suggest that dreaming recruits hierarchical visual feature representations associated with objects, which may support phenomenal aspects of dream experience.

Magnetic resonance imaging-guided core needle breast biopsies resulting in high-risk histopathologic findings: upstage frequency and lesion characteristics.

PubMed

Weinfurtner, R Jared; Patel, Bhavika; Laronga, Christine; Lee, Marie C; Falcon, Shannon L; Mooney, Blaise P; Yue, Binglin; Drukteinis, Jennifer S

2015-06-01

Analysis of magnetic resonance imaging-guided breast biopsies yielding high-risk histopathologic features at a single institution found an overall upstage rate to malignancy of 14% at surgical excision. All upstaged lesions were associated with atypical ductal hyperplasia. Flat epithelial atypia and atypical lobular hyperplasia alone or with lobular carcinoma in situ were not associated with an upstage to malignancy. The purpose of the present study w as to determine the malignancy upstage rates and imaging features of high-risk histopathologic findings resulting from magnetic resonance imaging (MRI)-guided core needle breast biopsies. These features include atypical ductal hyperplasia (ADH), atypical lobular hyperplasia (ALH), flat epithelial atypia (FEA), and lobular carcinoma in situ (LCIS). A retrospective medical record review was performed on all MRI-guided core needle breast biopsies at a single institution from June 1, 2007 to December 1, 2013 to select biopsies yielding high-risk histopathologic findings. The patient demographics, MRI lesion characteristics, and histopathologic features at biopsy and surgical excision were analyzed. A total of 257 MRI-guided biopsies had been performed, and 50 yielded high-risk histopathologic features (19%). Biopsy site and surgical excision site correlation was confirmed in 29 of 50 cases. Four of 29 lesions (14%) were upstaged: 1 case to invasive ductal carcinoma and 3 cases to ductal carcinoma in situ. ADH alone had an overall upstage rate of 7% (1 of 14), mixed ADH/ALH a rate of 75% (3 of 4), ALH alone or with LCIS a rate of 0% (0 of 7), and FEA a rate of 0% (0 of 4). Only mixed ADH/ALH had a statistically significant upstage rate to malignancy compared with the other high-risk histopathologic subtypes combined. No specific imaging characteristics on MRI were associated with an upstage to malignancy on the statistical analysis. MRI-guided breast biopsies yielding high-risk histopathologic features were associated with an overall upstage to malignancy rate of 14% at surgical excision. All upstaged lesions were associated with ADH. FEA and ALH alone or with LCIS were not associated with an upstage to malignancy. Copyright © 2015 Elsevier Inc. All rights reserved.

Competitive Advantage of PET/MRI

PubMed Central

Jadvar, Hossein; Colletti, Patrick M.

2013-01-01

Multimodality imaging has made great strides in the imaging evaluation of patients with a variety of diseases. Positron emission tomography/computed tomography (PET/CT) is now established as the imaging modality of choice in many clinical conditions, particularly in oncology. While the initial development of combined PET/magnetic resonance imaging (PET/MRI) was in the preclinical arena, hybrid PET/MR scanners are now available for clinical use. PET/MRI combines the unique features of MRI including excellent soft tissue contrast, diffusion-weighted imaging, dynamic contrast-enhanced imaging, fMRI and other specialized sequences as well as MR spectroscopy with the quantitative physiologic information that is provided by PET. Most evidence for the potential clinical utility of PET/MRI is based on studies performed with side-by-side comparison or software-fused MRI and PET images. Data on distinctive utility of hybrid PET/MRI are rapidly emerging. There are potential competitive advantages of PET/MRI over PET/CT. In general, PET/MRI may be preferred over PET/CT where the unique features of MRI provide more robust imaging evaluation in certain clinical settings. The exact role and potential utility of simultaneous data acquisition in specific research and clinical settings will need to be defined. It may be that simultaneous PET/MRI will be best suited for clinical situations that are disease-specific, organ-specific, related to diseases of the children or in those patients undergoing repeated imaging for whom cumulative radiation dose must be kept as low as reasonably achievable. PET/MRI also offers interesting opportunities for use of dual modality probes. Upon clear definition of clinical utility, other important and practical issues related to business operational model, clinical workflow and reimbursement will also be resolved. PMID:23791129

Competitive advantage of PET/MRI.

PubMed

Jadvar, Hossein; Colletti, Patrick M

2014-01-01

Multimodality imaging has made great strides in the imaging evaluation of patients with a variety of diseases. Positron emission tomography/computed tomography (PET/CT) is now established as the imaging modality of choice in many clinical conditions, particularly in oncology. While the initial development of combined PET/magnetic resonance imaging (PET/MRI) was in the preclinical arena, hybrid PET/MR scanners are now available for clinical use. PET/MRI combines the unique features of MRI including excellent soft tissue contrast, diffusion-weighted imaging, dynamic contrast-enhanced imaging, fMRI and other specialized sequences as well as MR spectroscopy with the quantitative physiologic information that is provided by PET. Most evidence for the potential clinical utility of PET/MRI is based on studies performed with side-by-side comparison or software-fused MRI and PET images. Data on distinctive utility of hybrid PET/MRI are rapidly emerging. There are potential competitive advantages of PET/MRI over PET/CT. In general, PET/MRI may be preferred over PET/CT where the unique features of MRI provide more robust imaging evaluation in certain clinical settings. The exact role and potential utility of simultaneous data acquisition in specific research and clinical settings will need to be defined. It may be that simultaneous PET/MRI will be best suited for clinical situations that are disease-specific, organ-specific, related to diseases of the children or in those patients undergoing repeated imaging for whom cumulative radiation dose must be kept as low as reasonably achievable. PET/MRI also offers interesting opportunities for use of dual modality probes. Upon clear definition of clinical utility, other important and practical issues related to business operational model, clinical workflow and reimbursement will also be resolved. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

MRI predictors of treatment response for perianal fistulizing Crohn disease in children and young adults.

PubMed

Shenoy-Bhangle, Anuradha; Nimkin, Katherine; Goldner, Dana; Bradley, William F; Israel, Esther J; Gee, Michael S

2014-01-01

Magnetic resonance imaging (MRI) is considered the imaging standard for diagnosis and characterization of perianal complications associated with Crohn disease in children and adults. To define MRI criteria that could act as potential predictors of treatment response in fistulizing Crohn disease in children, in order to guide more informed study interpretation. We performed a retrospective database query to identify all children and young adults with Crohn disease who underwent serial MRI studies for assessment of perianal symptoms between 2003 and 2010. We examined imaging features of perianal disease including fistula number, type and length, presence and size of associated abscess, and disease response/progression on follow-up MRI. We reviewed imaging studies and electronic medical records. Statistical analysis, including logistic regression, was performed to associate MR imaging features with treatment response and disease progression. We included 36 patients (22 male, 14 female; age range 8-21 years). Of these, 32 had a second MRI exam and 4 had clinical evidence of complete response, obviating the need for repeat imaging. Of the parameters analyzed, presence of abscess, type of fistula according to the Parks classification, and multiplicity were not predictors of treatment outcome. Maximum length of the dominant fistula and aggregate fistula length in the case of multiple fistulae were the best predictors of treatment outcome. Maximum fistula length <2.5 cm was a predictor of treatment response, while aggregate fistula length ≥2.5 cm was a predictor of disease progression. Perianal fistula length is an important imaging feature to assess on MRI of fistulizing Crohn disease.

New method for predicting estrogen receptor status utilizing breast MRI texture kinetic analysis

NASA Astrophysics Data System (ADS)

Chaudhury, Baishali; Hall, Lawrence O.; Goldgof, Dmitry B.; Gatenby, Robert A.; Gillies, Robert; Drukteinis, Jennifer S.

2014-03-01

Magnetic Resonance Imaging (MRI) of breast cancer typically shows that tumors are heterogeneous with spatial variations in blood flow and cell density. Here, we examine the potential link between clinical tumor imaging and the underlying evolutionary dynamics behind heterogeneity in the cellular expression of estrogen receptors (ER) in breast cancer. We assume, in an evolutionary environment, that ER expression will only occur in the presence of significant concentrations of estrogen, which is delivered via the blood stream. Thus, we hypothesize, the expression of ER in breast cancer cells will correlate with blood flow on gadolinium enhanced breast MRI. To test this hypothesis, we performed quantitative analysis of blood flow on dynamic contrast enhanced MRI (DCE-MRI) and correlated it with the ER status of the tumor. Here we present our analytic methods, which utilize a novel algorithm to analyze 20 volumetric DCE-MRI breast cancer tumors. The algorithm generates post initial enhancement (PIE) maps from DCE-MRI and then performs texture features extraction from the PIE map, feature selection, and finally classification of tumors into ER positive and ER negative status. The combined gray level co-occurrence matrices, gray level run length matrices and local binary pattern histogram features allow quantification of breast tumor heterogeneity. The algorithm predicted ER expression with an accuracy of 85% using a Naive Bayes classifier in leave-one-out cross-validation. Hence, we conclude that our data supports the hypothesis that imaging characteristics can, through application of evolutionary principles, provide insights into the cellular and molecular properties of cancer cells.

A multi-layer MRI description of Parkinson's disease

NASA Astrophysics Data System (ADS)

La Rocca, M.; Amoroso, N.; Lella, E.; Bellotti, R.; Tangaro, S.

2017-09-01

Magnetic resonance imaging (MRI) along with complex network is currently one of the most widely adopted techniques for detection of structural changes in neurological diseases, such as Parkinson's Disease (PD). In this paper, we present a digital image processing study, within the multi-layer network framework, combining more classifiers to evaluate the informative power of the MRI features, for the discrimination of normal controls (NC) and PD subjects. We define a network for each MRI scan; the nodes are the sub-volumes (patches) the images are divided into and the links are defined using the Pearson's pairwise correlation between patches. We obtain a multi-layer network whose important network features, obtained with different feature selection methods, are used to feed a supervised multi-level random forest classifier which exploits this base of knowledge for accurate classification. Method evaluation has been carried out using T1 MRI scans of 354 individuals, including 177 PD subjects and 177 NC from the Parkinson's Progression Markers Initiative (PPMI) database. The experimental results demonstrate that the features obtained from multiplex networks are able to accurately describe PD patterns. Besides, also if a privileged scale for studying PD disease exists, exploring the informative content of more scales leads to a significant improvement of the performances in the discrimination between disease and healthy subjects. In particular, this method gives a comprehensive overview of brain regions statistically affected by the disease, an additional value to the presented study.

[Magnetic resonance imaging study and cochlear implantation in post-meningitic deaf patients].

PubMed

Liu, Xiuli; Yao, Yiwen; He, Guili; Zhai, Lijie

2004-07-01

To investigate the clinical application of magnetic resonance imaging (MRI) in post-meningitic patients and its impact on surgical decision. The pre-operative MRI data and auditory brainstem response (ABR) examination of five post-meningitic patients were studied. They were implanted with cochleas. The interval between the onset of bacterial meningitis and the hearing loss was (15.8 +/- 15.0)d and it was longer in children than adults. Five ears showed membranous cochlear labyrinth abnormality; 3 ears had vestibule vestibule abnormality; 8 ears demonstrated semicircular canal abnormality on MRI examinations in totally 10 ears. The mean hearing threshold of 10 ears was (102.0 +/- 7.1)dB HL,that of the operated ears was (98.0 +/- 5.7)dB HL and that of the un-operated ears was (106.0 +/- 6.5)dB HL. It was (15.8 +/- 15.0)d from the bacterial meningitis onset to hearing loss. The interval is longer in children than adults. There were 3 ears that electrodes could not be inserted completely. The bacterial meningitis may cause the abnormalities of inner ears and the MRI before surgery is essential for the pre-operative planning of cochlear implant.

[Clinical value of MRI united-sequences examination in diagnosis and differentiation of morphological sub-type of hilar and extrahepatic big bile duct cholangiocarcinoma].

PubMed

Yin, Long-Lin; Song, Bin; Guan, Ying; Li, Ying-Chun; Chen, Guang-Wen; Zhao, Li-Ming; Lai, Li

2014-09-01

To investigate MRI features and associated histological and pathological changes of hilar and extrahepatic big bile duct cholangiocarcinoma with different morphological sub-types, and its value in differentiating between nodular cholangiocarcinoma (NCC) and intraductal growing cholangiocarcinoma (IDCC). Imaging data of 152 patients with pathologically confirmed hilar and extrahepatic big bile duct cholangiocarcinoma were reviewed, which included 86 periductal infiltrating cholangiocarcinoma (PDCC), 55 NCC, and 11 IDCC. Imaging features of the three morphological sub-types were compared. Each of the subtypes demonstrated its unique imaging features. Significant differences (P < 0.05) were found between NCC and IDCC in tumor shape, dynamic enhanced pattern, enhancement degree during equilibrium phase, multiplicity or singleness of tumor, changes in wall and lumen of bile duct at the tumor-bearing segment, dilatation of tumor upstream or downstream bile duct, and invasion of adjacent organs. Imaging features reveal tumor growth patterns of hilar and extrahepatic big bile duct cholangiocarcinoma. MRI united-sequences examination can accurately describe those imaging features for differentiation diagnosis.

Quantifying heterogeneity of lesion uptake in dynamic contrast enhanced MRI for breast cancer diagnosis

NASA Astrophysics Data System (ADS)

Karahaliou, A.; Vassiou, K.; Skiadopoulos, S.; Kanavou, T.; Yiakoumelos, A.; Costaridou, L.

2009-07-01

The current study investigates whether texture features extracted from lesion kinetics feature maps can be used for breast cancer diagnosis. Fifty five women with 57 breast lesions (27 benign, 30 malignant) were subjected to dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) on 1.5T system. A linear-slope model was fitted pixel-wise to a representative lesion slice time series and fitted parameters were used to create three kinetic maps (wash out, time to peak enhancement and peak enhancement). 28 grey level co-occurrence matrices features were extracted from each lesion kinetic map. The ability of texture features per map in discriminating malignant from benign lesions was investigated using a Probabilistic Neural Network classifier. Additional classification was performed by combining classification outputs of most discriminating feature subsets from the three maps, via majority voting. The combined scheme outperformed classification based on individual maps achieving area under Receiver Operating Characteristics curve 0.960±0.029. Results suggest that heterogeneity of breast lesion kinetics, as quantified by texture analysis, may contribute to computer assisted tissue characterization in DCE-MRI.

Effects of non-neuronal components for functional connectivity analysis from resting-state functional MRI toward automated diagnosis of schizophrenia

NASA Astrophysics Data System (ADS)

Kim, Junghoe; Lee, Jong-Hwan

2014-03-01

A functional connectivity (FC) analysis from resting-state functional MRI (rsfMRI) is gaining its popularity toward the clinical application such as diagnosis of neuropsychiatric disease. To delineate the brain networks from rsfMRI data, non-neuronal components including head motions and physiological artifacts mainly observed in cerebrospinal fluid (CSF), white matter (WM) along with a global brain signal have been regarded as nuisance variables in calculating the FC level. However, it is still unclear how the non-neuronal components can affect the performance toward diagnosis of neuropsychiatric disease. In this study, a systematic comparison of classification performance of schizophrenia patients was provided employing the partial correlation coefficients (CCs) as feature elements. Pair-wise partial CCs were calculated between brain regions, in which six combinatorial sets of nuisance variables were considered. The partial CCs were used as candidate feature elements followed by feature selection based on the statistical significance test between two groups in the training set. Once a linear support vector machine was trained using the selected features from the training set, the classification performance was evaluated using the features from the test set (i.e. leaveone- out cross validation scheme). From the results, the error rate using all non-neuronal components as nuisance variables (12.4%) was significantly lower than those using remaining combination of non-neuronal components as nuisance variables (13.8 ~ 20.0%). In conclusion, the non-neuronal components substantially degraded the automated diagnosis performance, which supports our hypothesis that the non-neuronal components are crucial in controlling the automated diagnosis performance of the neuropsychiatric disease using an fMRI modality.

Identifying patients with Alzheimer's disease using resting-state fMRI and graph theory.

PubMed

Khazaee, Ali; Ebrahimzadeh, Ata; Babajani-Feremi, Abbas

2015-11-01

Study of brain network on the basis of resting-state functional magnetic resonance imaging (fMRI) has provided promising results to investigate changes in connectivity among different brain regions because of diseases. Graph theory can efficiently characterize different aspects of the brain network by calculating measures of integration and segregation. In this study, we combine graph theoretical approaches with advanced machine learning methods to study functional brain network alteration in patients with Alzheimer's disease (AD). Support vector machine (SVM) was used to explore the ability of graph measures in diagnosis of AD. We applied our method on the resting-state fMRI data of twenty patients with AD and twenty age and gender matched healthy subjects. The data were preprocessed and each subject's graph was constructed by parcellation of the whole brain into 90 distinct regions using the automated anatomical labeling (AAL) atlas. The graph measures were then calculated and used as the discriminating features. Extracted network-based features were fed to different feature selection algorithms to choose most significant features. In addition to the machine learning approach, statistical analysis was performed on connectivity matrices to find altered connectivity patterns in patients with AD. Using the selected features, we were able to accurately classify patients with AD from healthy subjects with accuracy of 100%. Results of this study show that pattern recognition and graph of brain network, on the basis of the resting state fMRI data, can efficiently assist in the diagnosis of AD. Classification based on the resting-state fMRI can be used as a non-invasive and automatic tool to diagnosis of Alzheimer's disease. Copyright © 2015 International Federation of Clinical Neurophysiology. All rights reserved.

Machine-learning in grading of gliomas based on multi-parametric magnetic resonance imaging at 3T.

PubMed

Citak-Er, Fusun; Firat, Zeynep; Kovanlikaya, Ilhami; Ture, Ugur; Ozturk-Isik, Esin

2018-06-15

The objective of this study was to assess the contribution of multi-parametric (mp) magnetic resonance imaging (MRI) quantitative features in the machine learning-based grading of gliomas with a multi-region-of-interests approach. Forty-three patients who were newly diagnosed as having a glioma were included in this study. The patients were scanned prior to any therapy using a standard brain tumor magnetic resonance (MR) imaging protocol that included T1 and T2-weighted, diffusion-weighted, diffusion tensor, MR perfusion and MR spectroscopic imaging. Three different regions-of-interest were drawn for each subject to encompass tumor, immediate tumor periphery, and distant peritumoral edema/normal. The normalized mp-MRI features were used to build machine-learning models for differentiating low-grade gliomas (WHO grades I and II) from high grades (WHO grades III and IV). In order to assess the contribution of regional mp-MRI quantitative features to the classification models, a support vector machine-based recursive feature elimination method was applied prior to classification. A machine-learning model based on support vector machine algorithm with linear kernel achieved an accuracy of 93.0%, a specificity of 86.7%, and a sensitivity of 96.4% for the grading of gliomas using ten-fold cross validation based on the proposed subset of the mp-MRI features. In this study, machine-learning based on multiregional and multi-parametric MRI data has proven to be an important tool in grading glial tumors accurately even in this limited patient population. Future studies are needed to investigate the use of machine learning algorithms for brain tumor classification in a larger patient cohort. Copyright © 2018. Published by Elsevier Ltd.

Predictors of Residual Disease after Unplanned Excision of Soft Tissue Sarcomas

PubMed Central

Gingrich, Alicia A.; Elias, Alexandra; Michael Lee, Chia-Yuan; Nakache, Yves-Paul N.; Li, Chin-Shang; Shah, Dhruvil R.; Boutin, Robert D.; Canter, Robert J.

2016-01-01

Background Unplanned excision of soft tissue sarcomas (STS) is an important quality of care issue given the morbidity related to tumor bed excision. Since not all patients harbor residual disease at the time of re-excision, we sought to determine predictors of residual STS following unplanned excision. Methods We identified 76 patients from a prospective database (1/1/2008 – 9/30/2014) who received a diagnosis of primary STS following unplanned excision on the trunk or extremities. We used univariable and multivariable analyses to evaluate predictors of residual STS as the primary endpoint. We calculated the sensitivity/specificity and accuracy of interval magnetic resonance imaging (MRI) to predict residual sarcoma at re-excision. Results Mean age was 52 years, and 63.2% were male. 50% had fragmented unplanned excision. Among patients undergoing re-excision, residual STS was identified in 70%. On univariable analysis, MRI showing gross disease and fragmented excision were significant predictors of residual STS (OR 10.59, 95% CI 2.14–52.49, P=0.004 and OR 3.61, 95% CI 1.09–11.94, P=0.035, respectively). On multivariable analysis, tumor size predicted distant recurrence and overall survival. When we combined equivocal and positive MRI, the sensitivity and specificity of MRI for predicting residual STS were 86.7% (95% CI 73.2–95.0%) and 57.9% (95% CI 33.5–79.8%), with an overall accuracy of 78.1% (95% CI 66.0–87.5%). Conclusions 70% of patients undergoing repeat excision after unplanned excision of STS harbor residual sarcoma. Although interval MRI and fragmented excision appear to be the most significant predictors of residual STS, the accuracy of MRI remains modest, especially given the incidence of equivocal MRI. PMID:27993214

Reproducibility of Brain Morphometry from Short-Term Repeat Clinical MRI Examinations: A Retrospective Study

PubMed Central

Liu, Hon-Man; Chen, Shan-Kai; Chen, Ya-Fang; Lee, Chung-Wei; Yeh, Lee-Ren

2016-01-01

Purpose To assess the inter session reproducibility of automatic segmented MRI-derived measures by FreeSurfer in a group of subjects with normal-appearing MR images. Materials and Methods After retrospectively reviewing a brain MRI database from our institute consisting of 14,758 adults, those subjects who had repeat scans and had no history of neurodegenerative disorders were selected for morphometry analysis using FreeSurfer. A total of 34 subjects were grouped by MRI scanner model. After automatic segmentation using FreeSurfer, label-wise comparison (involving area, thickness, and volume) was performed on all segmented results. An intraclass correlation coefficient was used to estimate the agreement between sessions. Wilcoxon signed rank test was used to assess the population mean rank differences across sessions. Mean-difference analysis was used to evaluate the difference intervals across scanners. Absolute percent difference was used to estimate the reproducibility errors across the MRI models. Kruskal-Wallis test was used to determine the across-scanner effect. Results The agreement in segmentation results for area, volume, and thickness measurements of all segmented anatomical labels was generally higher in Signa Excite and Verio models when compared with Sonata and TrioTim models. There were significant rank differences found across sessions in some labels of different measures. Smaller difference intervals in global volume measurements were noted on images acquired by Signa Excite and Verio models. For some brain regions, significant MRI model effects were observed on certain segmentation results. Conclusions Short-term scan-rescan reliability of automatic brain MRI morphometry is feasible in the clinical setting. However, since repeatability of software performance is contingent on the reproducibility of the scanner performance, the scanner performance must be calibrated before conducting such studies or before using such software for retrospective reviewing. PMID:26812647

Classifying Cognitive Profiles Using Machine Learning with Privileged Information in Mild Cognitive Impairment.

PubMed

Alahmadi, Hanin H; Shen, Yuan; Fouad, Shereen; Luft, Caroline Di B; Bentham, Peter; Kourtzi, Zoe; Tino, Peter

2016-01-01

Early diagnosis of dementia is critical for assessing disease progression and potential treatment. State-or-the-art machine learning techniques have been increasingly employed to take on this diagnostic task. In this study, we employed Generalized Matrix Learning Vector Quantization (GMLVQ) classifiers to discriminate patients with Mild Cognitive Impairment (MCI) from healthy controls based on their cognitive skills. Further, we adopted a "Learning with privileged information" approach to combine cognitive and fMRI data for the classification task. The resulting classifier operates solely on the cognitive data while it incorporates the fMRI data as privileged information (PI) during training. This novel classifier is of practical use as the collection of brain imaging data is not always possible with patients and older participants. MCI patients and healthy age-matched controls were trained to extract structure from temporal sequences. We ask whether machine learning classifiers can be used to discriminate patients from controls and whether differences between these groups relate to individual cognitive profiles. To this end, we tested participants in four cognitive tasks: working memory, cognitive inhibition, divided attention, and selective attention. We also collected fMRI data before and after training on a probabilistic sequence learning task and extracted fMRI responses and connectivity as features for machine learning classifiers. Our results show that the PI guided GMLVQ classifiers outperform the baseline classifier that only used the cognitive data. In addition, we found that for the baseline classifier, divided attention is the only relevant cognitive feature. When PI was incorporated, divided attention remained the most relevant feature while cognitive inhibition became also relevant for the task. Interestingly, this analysis for the fMRI GMLVQ classifier suggests that (1) when overall fMRI signal is used as inputs to the classifier, the post-training session is most relevant; and (2) when the graph feature reflecting underlying spatiotemporal fMRI pattern is used, the pre-training session is most relevant. Taken together these results suggest that brain connectivity before training and overall fMRI signal after training are both diagnostic of cognitive skills in MCI.

Single trial decoding of belief decision making from EEG and fMRI data using independent components features

PubMed Central

Douglas, Pamela K.; Lau, Edward; Anderson, Ariana; Head, Austin; Kerr, Wesley; Wollner, Margalit; Moyer, Daniel; Li, Wei; Durnhofer, Mike; Bramen, Jennifer; Cohen, Mark S.

2013-01-01

The complex task of assessing the veracity of a statement is thought to activate uniquely distributed brain regions based on whether a subject believes or disbelieves a given assertion. In the current work, we present parallel machine learning methods for predicting a subject's decision response to a given propositional statement based on independent component (IC) features derived from EEG and fMRI data. Our results demonstrate that IC features outperformed features derived from event related spectral perturbations derived from any single spectral band, yet were similar to accuracy across all spectral bands combined. We compared our diagnostic IC spatial maps with our conventional general linear model (GLM) results, and found that informative ICs had significant spatial overlap with our GLM results, yet also revealed unique regions like amygdala that were not statistically significant in GLM analyses. Overall, these results suggest that ICs may yield a parsimonious feature set that can be used along with a decision tree structure for interpretation of features used in classifying complex cognitive processes such as belief and disbelief across both fMRI and EEG neuroimaging modalities. PMID:23914164

Magnetic Resonance Imaging Criteria for Thrombolysis in Hyperacute Cerebral Infarction

PubMed Central

AHMETGJEKAJ, ILIR; KABASHI-MUÇAJ, SERBEZE; LASCU, LUANA CORINA; KABASHI, ANTIGONA; BONDARI, A.; BONDARI, SIMONA; DEDUSHI-HOTI, KRESHNIKE; BIÇAKU, ARDIAN; SHATRI, JETON

2014-01-01

Purpose: Selection of patients with cerebral infarction for MRI that is suitable for thrombolytic therapy as an emerging application. Although the efficiency of the therapy with i.v. tissue plasminogen activator (tPA) within 3 hours after onset of symptoms has been proven in selected patients with CT, now these criteria are determined by MRI, as the data we gather are fast and accurate in the first hours. Material and methods: MRI screening in patients with acute cerebral infarction before application of thrombolytic therapy was done in a UCC Mannheim in Germany. Unlike trials with CT, MRI studies demonstrated the benefits of therapy up to 6 hours after the onset of symptoms. We studied 21 patients hospitalized in Clinic of Neuroradiology at University Clinical Centre in Mannheim-Germany. They all undergo brain MRI evaluation for stroke. This article reviews literature that has followed application of thrombolysis in patients with cerebral infarction based on MRI. Results: We have analyzed the MRI criteria for i.v. application of tPA at this University Centre. Alongside the personal viewpoints of clinicians, survey reveals a variety of clinical aspects and MRI features that are opened for further more exploration: therapeutic effects, the use of the MRI angiography, dynamics, and other. Conclusions: MRI is a tested imaging method for rapid evaluation of patients with hyperacute cerebral infarction, replacing the use of CT imaging and clinical features. MRI criteria for thrombolytic therapy are being applied in some cerebral vascular centres. In Kosovo, the application of thrombolytic therapy has not started yet. PMID:25729591

Computed tomography for occult fractures of the proximal femur, pelvis, and sacrum in clinical practice: single institution, dual-site experience.

PubMed

Mandell, Jacob C; Weaver, Michael J; Khurana, Bharti

2018-06-01

The purpose of this study was to evaluate the diagnostic performance of CT for assessment of occult fractures of the proximal femur, pelvis, and sacrum. A retrospective review was performed on patients who received a CT of the hip or pelvis for suspected occult fracture after negative or equivocal radiographs performed within 24 h. The official radiology report was utilized for the determination of CT findings and calculation of sensitivity and specificity. Surgical reports, MRI reports, and clinical follow-up were used as the standard of reference. Sensitivity and specificity were calculated with 95% confidence intervals. Seventy-four patients received CT of the hip or pelvis for clinical concern for occult fracture after negative or equivocal radiographs. By the reference standard, a total of 40 fractures were present in 25/74 (33.8%) patients, including 35 conservatively treated fractures of the greater trochanter, pelvis, and sacrum, and 5 operatively treated proximal femoral fractures. A total of 14/74 (18.9%) of patients had an MRI within 1 day of CT. MRI identified an operatively treated femoral neck fracture not seen on CT and an operatively treated intertrochanteric fracture, which CT described as a greater trochanteric fracture. There were two false negative conservatively treated pelvic fractures not seen on CT but diagnosed on MRI. On a per-patient basis, CT had an overall sensitivity of 88% (22/25; 95% confidence intervals 69-97%), specificity of 98% (48/49; 95% confidence intervals 89-100%), and negative predictive value of 94%. For the five operative proximal femoral fractures, the sensitivity of CT was 60% (3/5; 95% confidence intervals 15-95%), specificity was 99% (68/69; 95% confidence intervals 92-100%), and negative predictive value was 97%. In the clinical setting of suspected occult fracture, the sensitivity of clinical CT reports for detection of any type of fracture of the proximal femur, pelvis, or sacrum was 88%. For the small number of operatively treated proximal femoral fractures seen in the study, sensitivity of CT was 60% (3/5) and negative predictive value was 97%, although the relatively few patients needing fixation precludes statistical analysis.

Microstructure Imaging of Crossing (MIX) White Matter Fibers from diffusion MRI

PubMed Central

Farooq, Hamza; Xu, Junqian; Nam, Jung Who; Keefe, Daniel F.; Yacoub, Essa; Georgiou, Tryphon; Lenglet, Christophe

2016-01-01

Diffusion MRI (dMRI) reveals microstructural features of the brain white matter by quantifying the anisotropic diffusion of water molecules within axonal bundles. Yet, identifying features such as axonal orientation dispersion, density, diameter, etc., in complex white matter fiber configurations (e.g. crossings) has proved challenging. Besides optimized data acquisition and advanced biophysical models, computational procedures to fit such models to the data are critical. However, these procedures have been largely overlooked by the dMRI microstructure community and new, more versatile, approaches are needed to solve complex biophysical model fitting problems. Existing methods are limited to models assuming single fiber orientation, relevant to limited brain areas like the corpus callosum, or multiple orientations but without the ability to extract detailed microstructural features. Here, we introduce a new and versatile optimization technique (MIX), which enables microstructure imaging of crossing white matter fibers. We provide a MATLAB implementation of MIX, and demonstrate its applicability to general microstructure models in fiber crossings using synthetic as well as ex-vivo and in-vivo brain data. PMID:27982056

A hybrid method for classifying cognitive states from fMRI data.

PubMed

Parida, S; Dehuri, S; Cho, S-B; Cacha, L A; Poznanski, R R

2015-09-01

Functional magnetic resonance imaging (fMRI) makes it possible to detect brain activities in order to elucidate cognitive-states. The complex nature of fMRI data requires under-standing of the analyses applied to produce possible avenues for developing models of cognitive state classification and improving brain activity prediction. While many models of classification task of fMRI data analysis have been developed, in this paper, we present a novel hybrid technique through combining the best attributes of genetic algorithms (GAs) and ensemble decision tree technique that consistently outperforms all other methods which are being used for cognitive-state classification. Specifically, this paper illustrates the combined effort of decision-trees ensemble and GAs for feature selection through an extensive simulation study and discusses the classification performance with respect to fMRI data. We have shown that our proposed method exhibits significant reduction of the number of features with clear edge classification accuracy over ensemble of decision-trees.

Consistency of signal intensity and T2* in frozen ex vivo heart muscle, kidney, and liver tissue.

PubMed

Kaye, Elena A; Josan, Sonal; Lu, Aiming; Rosenberg, Jarrett; Daniel, Bruce L; Pauly, Kim Butts

2010-03-01

To investigate tissue dependence of the MRI-based thermometry in frozen tissue by quantification and comparison of signal intensity and T2* of ex vivo frozen tissue of three different types: heart muscle, kidney, and liver. Tissue samples were frozen and imaged on a 0.5 Tesla MRI scanner with ultrashort echo time (UTE) sequence. Signal intensity and T2* were determined as the temperature of the tissue samples was decreased from room temperature to approximately -40 degrees C. Statistical analysis was performed for (-20 degrees C, -5 degrees C) temperature interval. The findings of this study demonstrate that signal intensity and T2* are consistent across three types of tissue for (-20 degrees C, -5 degrees C) temperature interval. Both parameters can be used to calculate a single temperature calibration curve for all three types of tissue and potentially in the future serve as a foundation for tissue-independent MRI-based thermometry.

Enhancing the discrimination accuracy between metastases, gliomas and meningiomas on brain MRI by volumetric textural features and ensemble pattern recognition methods.

PubMed

Georgiadis, Pantelis; Cavouras, Dionisis; Kalatzis, Ioannis; Glotsos, Dimitris; Athanasiadis, Emmanouil; Kostopoulos, Spiros; Sifaki, Koralia; Malamas, Menelaos; Nikiforidis, George; Solomou, Ekaterini

2009-01-01

Three-dimensional (3D) texture analysis of volumetric brain magnetic resonance (MR) images has been identified as an important indicator for discriminating among different brain pathologies. The purpose of this study was to evaluate the efficiency of 3D textural features using a pattern recognition system in the task of discriminating benign, malignant and metastatic brain tissues on T1 postcontrast MR imaging (MRI) series. The dataset consisted of 67 brain MRI series obtained from patients with verified and untreated intracranial tumors. The pattern recognition system was designed as an ensemble classification scheme employing a support vector machine classifier, specially modified in order to integrate the least squares features transformation logic in its kernel function. The latter, in conjunction with using 3D textural features, enabled boosting up the performance of the system in discriminating metastatic, malignant and benign brain tumors with 77.14%, 89.19% and 93.33% accuracy, respectively. The method was evaluated using an external cross-validation process; thus, results might be considered indicative of the generalization performance of the system to "unseen" cases. The proposed system might be used as an assisting tool for brain tumor characterization on volumetric MRI series.

Contrast-enhanced MRI compared with the physical examination in the evaluation of disease activity in juvenile idiopathic arthritis.

PubMed

Hemke, Robert; Maas, Mario; van Veenendaal, Mira; Dolman, Koert M; van Rossum, Marion A J; van den Berg, J Merlijn; Kuijpers, Taco W

2014-02-01

To assess the value of magnetic resonance imaging (MRI) in discriminating between active and inactive juvenile idiopathic arthritis (JIA) patients and to compare physical examination outcomes with MRI outcomes in the assessment of disease status in JIA patients. Consecutive JIA patients with knee involvement were prospectively studied using an open-bore MRI. Imaging findings from 146 JIA patients were analysed (59.6% female; mean age, 12.9 years). Patients were classified as clinically active or inactive. MRI features were evaluated using the JAMRIS system, comprising validated scores for synovial hypertrophy, bone marrow oedema, cartilage lesions and bone erosions. Inter-reader reliability was good for all MRI features (intra-class correlation coefficient [ICC] = 0.87-0.94). No differences were found between the two groups regarding MRI scores of bone marrow oedema, cartilage lesions or bone erosions. Synovial hypertrophy scores differed significantly between groups (P = 0.016). Nonetheless, synovial hypertrophy was also present in 14 JIA patients (35.9%) with clinically inactive disease. Of JIA patients considered clinically active, 48.6% showed no signs of MRI-based synovitis. MRI can discriminate between clinically active and inactive JIA patients. However, physical examination is neither very sensitive nor specific in evaluating JIA disease activity compared with MRI. Subclinical synovitis was present in >35% of presumed clinically inactive patients. • MRI is sensitive for evaluating juvenile idiopathic arthritis (JIA) disease activity. • Contrast-enhanced MRI can distinguish clinically active and inactive JIA patients. • Subclinical synovitis is present in 35.9 % of presumed clinically inactive patients. • Physical examination is neither sensitive nor specific in evaluating JIA disease activity.

Multi-task linear programming discriminant analysis for the identification of progressive MCI individuals.

PubMed

Yu, Guan; Liu, Yufeng; Thung, Kim-Han; Shen, Dinggang

2014-01-01

Accurately identifying mild cognitive impairment (MCI) individuals who will progress to Alzheimer's disease (AD) is very important for making early interventions. Many classification methods focus on integrating multiple imaging modalities such as magnetic resonance imaging (MRI) and fluorodeoxyglucose positron emission tomography (FDG-PET). However, the main challenge for MCI classification using multiple imaging modalities is the existence of a lot of missing data in many subjects. For example, in the Alzheimer's Disease Neuroimaging Initiative (ADNI) study, almost half of the subjects do not have PET images. In this paper, we propose a new and flexible binary classification method, namely Multi-task Linear Programming Discriminant (MLPD) analysis, for the incomplete multi-source feature learning. Specifically, we decompose the classification problem into different classification tasks, i.e., one for each combination of available data sources. To solve all different classification tasks jointly, our proposed MLPD method links them together by constraining them to achieve the similar estimated mean difference between the two classes (under classification) for those shared features. Compared with the state-of-the-art incomplete Multi-Source Feature (iMSF) learning method, instead of constraining different classification tasks to choose a common feature subset for those shared features, MLPD can flexibly and adaptively choose different feature subsets for different classification tasks. Furthermore, our proposed MLPD method can be efficiently implemented by linear programming. To validate our MLPD method, we perform experiments on the ADNI baseline dataset with the incomplete MRI and PET images from 167 progressive MCI (pMCI) subjects and 226 stable MCI (sMCI) subjects. We further compared our method with the iMSF method (using incomplete MRI and PET images) and also the single-task classification method (using only MRI or only subjects with both MRI and PET images). Experimental results show very promising performance of our proposed MLPD method.

Multi-Task Linear Programming Discriminant Analysis for the Identification of Progressive MCI Individuals

PubMed Central

Yu, Guan; Liu, Yufeng; Thung, Kim-Han; Shen, Dinggang

2014-01-01

Accurately identifying mild cognitive impairment (MCI) individuals who will progress to Alzheimer's disease (AD) is very important for making early interventions. Many classification methods focus on integrating multiple imaging modalities such as magnetic resonance imaging (MRI) and fluorodeoxyglucose positron emission tomography (FDG-PET). However, the main challenge for MCI classification using multiple imaging modalities is the existence of a lot of missing data in many subjects. For example, in the Alzheimer's Disease Neuroimaging Initiative (ADNI) study, almost half of the subjects do not have PET images. In this paper, we propose a new and flexible binary classification method, namely Multi-task Linear Programming Discriminant (MLPD) analysis, for the incomplete multi-source feature learning. Specifically, we decompose the classification problem into different classification tasks, i.e., one for each combination of available data sources. To solve all different classification tasks jointly, our proposed MLPD method links them together by constraining them to achieve the similar estimated mean difference between the two classes (under classification) for those shared features. Compared with the state-of-the-art incomplete Multi-Source Feature (iMSF) learning method, instead of constraining different classification tasks to choose a common feature subset for those shared features, MLPD can flexibly and adaptively choose different feature subsets for different classification tasks. Furthermore, our proposed MLPD method can be efficiently implemented by linear programming. To validate our MLPD method, we perform experiments on the ADNI baseline dataset with the incomplete MRI and PET images from 167 progressive MCI (pMCI) subjects and 226 stable MCI (sMCI) subjects. We further compared our method with the iMSF method (using incomplete MRI and PET images) and also the single-task classification method (using only MRI or only subjects with both MRI and PET images). Experimental results show very promising performance of our proposed MLPD method. PMID:24820966

Optic Nerve Assessment Using 7-Tesla Magnetic Resonance Imaging

PubMed Central

Singh, Arun D.; Platt, Sean M.; Lystad, Lisa; Lowe, Mark; Oh, Sehong; Jones, Stephen E.; Alzahrani, Yahya; Plesec, Thomas

2016-01-01

Purpose The purpose of this study was to correlate high-resolution magnetic resonance imaging (MRI) and histologic findings in a case of juxtapapillary choroidal melanoma with clinical evidence of optic nerve invasion. Methods With institutional review board approval, an enucleated globe with choroidal melanoma and optic nerve invasion was imaged using a 7-tesla MRI followed by histopathologic evaluation. Results Optical coherence tomography, B-scan ultrasonography, and 1.5-tesla MRI of the orbit (1-mm sections) could not detect optic disc invasion. Ex vivo, 7-tesla MRI detected optic nerve invasion, which correlated with histopathologic features. Conclusions Our case demonstrates the potential to document the existence of optic nerve invasion in the presence of an intraocular tumor, a feature that has a major bearing on decision making, particularly for consideration of enucleation. PMID:27239461

[Central nervous system involvement in systemic diseases: Spectrum of MRI findings].

PubMed

Drier, A; Bonneville, F; Haroche, J; Amoura, Z; Dormont, D; Chiras, J

2010-12-01

Central nervous system (CNS) involvement in systemic disease (SD) is unusual. MRI features of such lesions are unfamiliar to most radiologists. The diagnosis of SD is still based on clinical features and laboratory findings but some characteristic MRI findings exist for each SD: micronodular leptomeningeal enhancement in sarcoidosis, diffuse or focal pachymeningeal involvement in Wegener disease, dentate nuclei and brain stem lesions in Langerhans cell histiocytosis, meningeal masses, dentate nuclei lesions and periarterial infiltration in Erdheim-Chester disease, meningeal masses in Rosai-Dorfman disease, veinular pontic lesions and cerebral vein thrombosis in Behçet, supratentorial microvascular lesions in lupus and antiphospholipid and Gougerot-Sjögren syndrome. In this work, we explain, describe and illustrate the most characteristic MRI findings for each disease. Copyright © 2010 Elsevier Masson SAS. All rights reserved.

fMRI Mapping of Brain Activity Associated with the Vocal Production of Consonant and Dissonant Intervals.

PubMed

González-García, Nadia; Rendón, Pablo L

2017-05-23

The neural correlates of consonance and dissonance perception have been widely studied, but not the neural correlates of consonance and dissonance production. The most straightforward manner of musical production is singing, but, from an imaging perspective, it still presents more challenges than listening because it involves motor activity. The accurate singing of musical intervals requires integration between auditory feedback processing and vocal motor control in order to correctly produce each note. This protocol presents a method that permits the monitoring of neural activations associated with the vocal production of consonant and dissonant intervals. Four musical intervals, two consonant and two dissonant, are used as stimuli, both for an auditory discrimination test and a task that involves first listening to and then reproducing given intervals. Participants, all female vocal students at the conservatory level, were studied using functional Magnetic Resonance Imaging (fMRI) during the performance of the singing task, with the listening task serving as a control condition. In this manner, the activity of both the motor and auditory systems was observed, and a measure of vocal accuracy during the singing task was also obtained. Thus, the protocol can also be used to track activations associated with singing different types of intervals or with singing the required notes more accurately. The results indicate that singing dissonant intervals requires greater participation of the neural mechanisms responsible for the integration of external feedback from the auditory and sensorimotor systems than does singing consonant intervals.

Magnetic resonance imaging features of dogs with incomplete recovery after acute, severe spinal cord injury

PubMed Central

Lewis, Melissa J.; Cohen, Eli B.; Olby, Natasha J.

2017-01-01

Study Design Retrospective case series Objectives Describe the magnetic resonance imaging (MRI) features of dogs chronically impaired after severe spinal cord injury (SCI) and investigate associations between imaging variables and residual motor function. Setting United States of America Methods Thoracolumbar MRI from dogs with incomplete recovery months to years after clinically complete (paralysis with loss of pain perception) thoracolumbar SCI were reviewed. Lesion features were described and quantified. Gait was quantified using an ordinal, open field scale (OFS). Associations between imaging features and gait scores, duration of injury (DOI) or SCI treatment were determined. Results 35 dogs were included. Median OFS was 2 (0–6), median DOI was 13 months (3–83) and intervertebral disc herniation was the most common diagnosis (n=27). Myelomalacia was the most common qualitative feature followed by cystic change; syringomyelia and fibrosis were uncommon. Lesion length corrected to L2 length (LL:L2) was variable (median LL:L2=3.5 (1.34–11.54)). Twenty-nine dogs had 100% maximum cross-sectional spinal cord compromise (MSCC) at the lesion epicenter and the length of 100% compromised area varied widely (median length 100% MSCC:L2=1.29 (0.39–7.64). Length 100% MSCC:L2 was associated with OFS (p=0.012). OFS was not associated with any qualitative features. DOI or treatment type were not associated with imaging features or lesion quantification. Conclusions Lesion characteristics on MRI in dogs with incomplete recovery after severe SCI were established. Length of 100% MSCC was associated with hind limb motor function. Findings demonstrate a spectrum of injury severity on MRI amongst severely affected dogs which is related to functional status. PMID:29057987

Hierarchical feature representation and multimodal fusion with deep learning for AD/MCI diagnosis.

PubMed

Suk, Heung-Il; Lee, Seong-Whan; Shen, Dinggang

2014-11-01

For the last decade, it has been shown that neuroimaging can be a potential tool for the diagnosis of Alzheimer's Disease (AD) and its prodromal stage, Mild Cognitive Impairment (MCI), and also fusion of different modalities can further provide the complementary information to enhance diagnostic accuracy. Here, we focus on the problems of both feature representation and fusion of multimodal information from Magnetic Resonance Imaging (MRI) and Positron Emission Tomography (PET). To our best knowledge, the previous methods in the literature mostly used hand-crafted features such as cortical thickness, gray matter densities from MRI, or voxel intensities from PET, and then combined these multimodal features by simply concatenating into a long vector or transforming into a higher-dimensional kernel space. In this paper, we propose a novel method for a high-level latent and shared feature representation from neuroimaging modalities via deep learning. Specifically, we use Deep Boltzmann Machine (DBM)(2), a deep network with a restricted Boltzmann machine as a building block, to find a latent hierarchical feature representation from a 3D patch, and then devise a systematic method for a joint feature representation from the paired patches of MRI and PET with a multimodal DBM. To validate the effectiveness of the proposed method, we performed experiments on ADNI dataset and compared with the state-of-the-art methods. In three binary classification problems of AD vs. healthy Normal Control (NC), MCI vs. NC, and MCI converter vs. MCI non-converter, we obtained the maximal accuracies of 95.35%, 85.67%, and 74.58%, respectively, outperforming the competing methods. By visual inspection of the trained model, we observed that the proposed method could hierarchically discover the complex latent patterns inherent in both MRI and PET. Copyright © 2014 Elsevier Inc. All rights reserved.

Predicting axillary lymph node metastasis from kinetic statistics of DCE-MRI breast images

NASA Astrophysics Data System (ADS)

Ashraf, Ahmed B.; Lin, Lilie; Gavenonis, Sara C.; Mies, Carolyn; Xanthopoulos, Eric; Kontos, Despina

2012-03-01

The presence of axillary lymph node metastases is the most important prognostic factor in breast cancer and can influence the selection of adjuvant therapy, both chemotherapy and radiotherapy. In this work we present a set of kinetic statistics derived from DCE-MRI for predicting axillary node status. Breast DCE-MRI images from 69 women with known nodal status were analyzed retrospectively under HIPAA and IRB approval. Axillary lymph nodes were positive in 12 patients while 57 patients had no axillary lymph node involvement. Kinetic curves for each pixel were computed and a pixel-wise map of time-to-peak (TTP) was obtained. Pixels were first partitioned according to the similarity of their kinetic behavior, based on TTP values. For every kinetic curve, the following pixel-wise features were computed: peak enhancement (PE), wash-in-slope (WIS), wash-out-slope (WOS). Partition-wise statistics for every feature map were calculated, resulting in a total of 21 kinetic statistic features. ANOVA analysis was done to select features that differ significantly between node positive and node negative women. Using the computed kinetic statistic features a leave-one-out SVM classifier was learned that performs with AUC=0.77 under the ROC curve, outperforming the conventional kinetic measures, including maximum peak enhancement (MPE) and signal enhancement ratio (SER), (AUCs of 0.61 and 0.57 respectively). These findings suggest that our DCE-MRI kinetic statistic features can be used to improve the prediction of axillary node status in breast cancer patients. Such features could ultimately be used as imaging biomarkers to guide personalized treatment choices for women diagnosed with breast cancer.

Hierarchical Feature Representation and Multimodal Fusion with Deep Learning for AD/MCI Diagnosis

PubMed Central

Suk, Heung-Il; Lee, Seong-Whan; Shen, Dinggang

2014-01-01

For the last decade, it has been shown that neuroimaging can be a potential tool for the diagnosis of Alzheimer’s Disease (AD) and its prodromal stage, Mild Cognitive Impairment (MCI), and also fusion of different modalities can further provide the complementary information to enhance diagnostic accuracy. Here, we focus on the problems of both feature representation and fusion of multimodal information from Magnetic Resonance Imaging (MRI) and Positron Emission Tomography (PET). To our best knowledge, the previous methods in the literature mostly used hand-crafted features such as cortical thickness, gray matter densities from MRI, or voxel intensities from PET, and then combined these multimodal features by simply concatenating into a long vector or transforming into a higher-dimensional kernel space. In this paper, we propose a novel method for a high-level latent and shared feature representation from neuroimaging modalities via deep learning. Specifically, we use Deep Boltzmann Machine (DBM)1, a deep network with a restricted Boltzmann machine as a building block, to find a latent hierarchical feature representation from a 3D patch, and then devise a systematic method for a joint feature representation from the paired patches of MRI and PET with a multimodal DBM. To validate the effectiveness of the proposed method, we performed experiments on ADNI dataset and compared with the state-of-the-art methods. In three binary classification problems of AD vs. healthy Normal Control (NC), MCI vs. NC, and MCI converter vs. MCI non-converter, we obtained the maximal accuracies of 95.35%, 85.67%, and 74.58%, respectively, outperforming the competing methods. By visual inspection of the trained model, we observed that the proposed method could hierarchically discover the complex latent patterns inherent in both MRI and PET. PMID:25042445

Evaluation of the Prostate Bed for Local Recurrence After Radical Prostatectomy Using Endorectal Magnetic Resonance Imaging

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

Liauw, Stanley L., E-mail: sliauw@radonc.uchicago.edu; Pitroda, Sean P.; Eggener, Scott E.

Purpose: To summarize the results of a 4-year period in which endorectal magnetic resonance imaging (MRI) was considered for all men referred for salvage radiation therapy (RT) at a single academic center; to describe the incidence and location of locally recurrent disease in a contemporary cohort of men with biochemical failure after radical prostatectomy (RP), and to identify prognostic variables associated with MRI findings in order to define which patients may have the highest yield of the study. Methods and Materials: Between 2007 and 2011, 88 men without clinically palpable disease underwent eMRI for detectable prostate-specific antigen (PSA) after RP.more » The median interval between RP and eMRI was 32 months (interquartile range, 14-57 months), and the median PSA level was 0.30 ng/mL (interquartile range, 0.19-0.72 ng/mL). Magnetic resonance imaging scans consisting of T2-weighted, diffusion-weighted, and dynamic contrast-enhanced imaging were evaluated for features consistent with local recurrence. The prostate bed was scored from 0-4, whereby 0 was definitely normal, 1 probably normal, 2 indeterminate, 3 probably abnormal, and 4 definitely abnormal. Local recurrence was defined as having a score of 3-4. Results: Local recurrence was identified in 21 men (24%). Abnormalities were best appreciated on T2-weighted axial images (90%) as focal hypointense lesions. Recurrence locations were perianastomotic (67%) or retrovesical (33%). The only risk factor associated with local recurrence was PSA; recurrence was seen in 37% of men with PSA >0.3 ng/mL vs 13% if PSA {<=}0.3 ng/mL (P<.01). The median volume of recurrence was 0.26 cm{sup 3} and was directly associated with PSA (r=0.5, P=.02). The correlation between MRI-based tumor volume and PSA was even stronger in men with positive margins (r=0.8, P<.01). Conclusions: Endorectal MRI can define areas of local recurrence after RP in a minority of men without clinical evidence of disease, with yield related to PSA. Further study is necessary to determine whether eMRI can improve patient selection and success of salvage RT.« less

Dynamic Contrast-Enhanced MRI of Cervical Cancers: Temporal Percentile Screening of Contrast Enhancement Identifies Parameters for Prediction of Chemoradioresistance

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

Andersen, Erlend K.F.; Hole, Knut Hakon; Lund, Kjersti V.

Purpose: To systematically screen the tumor contrast enhancement of locally advanced cervical cancers to assess the prognostic value of two descriptive parameters derived from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). Methods and Materials: This study included a prospectively collected cohort of 81 patients who underwent DCE-MRI with gadopentetate dimeglumine before chemoradiotherapy. The following descriptive DCE-MRI parameters were extracted voxel by voxel and presented as histograms for each time point in the dynamic series: normalized relative signal increase (nRSI) and normalized area under the curve (nAUC). The first to 100th percentiles of the histograms were included in a log-rank survival test,more » resulting in p value and relative risk maps of all percentile-time intervals for each DCE-MRI parameter. The maps were used to evaluate the robustness of the individual percentile-time pairs and to construct prognostic parameters. Clinical endpoints were locoregional control and progression-free survival. The study was approved by the institutional ethics committee. Results: The p value maps of nRSI and nAUC showed a large continuous region of percentile-time pairs that were significantly associated with locoregional control (p < 0.05). These parameters had prognostic impact independent of tumor stage, volume, and lymph node status on multivariate analysis. Only a small percentile-time interval of nRSI was associated with progression-free survival. Conclusions: The percentile-time screening identified DCE-MRI parameters that predict long-term locoregional control after chemoradiotherapy of cervical cancer.« less

Distal border fragments of the equine navicular bone: association between magnetic resonance imaging characteristics and clinical lameness

USGS Publications Warehouse

Yorke, Elizabeth H.; Judy, Carter E.; Saveraid, Travis C.; McGowan, Conor P.; Caldwell, Fred J.

2014-01-01

Distal border fragments of the navicular bone are increasingly being detected due to the improved capabilities of magnetic resonance imaging (MRI), but their clinical significance remains unclear. The purpose of this retrospective study was to describe the location, size, and frequency of fragments in a cohort of horses presented for MRI of the foot and to compare MRI findings with severity of lameness. Archived MRI studies and medical records were searched from March 2006 to June 2008. Horses were included if a distal border fragment of the navicular bone was visible in MRI scans. Confidence interval comparisons and linear regression analyses were used to test hypotheses that fragments were associated with lameness and lameness severity was positively correlated with fragment volume and biaxial location. A total of 453 horses (874 limbs) were included. Fragments were identified in 60 horses (13.25%) and 90 limbs (10.3%). Fifty percent of the horses had unilateral fragments and 50% had bilateral fragments. Fragments were located at the lateral (62.2%), medial (8.89%), or medial and lateral (28.9%) angles of the distal border of the navicular bone. There was no increased probability of being categorized as lame if a fragment was present. There was no significant difference in fragment volume across lameness severity categorizations. Confidence intervals indicated a slightly increased probability of being classified as lame if both medial and lateral fragments were present. Findings indicated that distal border fragments of the navicular bone in equine MRI studies are unlikely to be related to existing lameness.

Postoperative enhancement on breast MRI: Time course and pattern of changes.

PubMed

Mahoney, Mary C; Sharda, Radhika G

2018-04-23

Expected postoperative enhancement on breast MRI can appear similar to enhancement seen in recurrent or residual malignancy. Our aim was to assess the time course and patterns of enhancement at the surgical site, thereby helping to distinguish between benign and malignant postoperative enhancement. In 200 MRI scans performed in 153 patients after breast conservation treatment, 43 after surgical excision of atypia, and 4 patients after benign excisional biopsy were categorized by postoperative time interval. We defined 4 patterns of morphologic enhancement on MRI: cavity wall/seroma (Pattern I); thin linear (Pattern II); mass (Pattern III); and fat necrosis (Pattern IV). Of 200 MRI scans, 66 (33%) demonstrated enhancement at the surgical site. Enhancement typically decreased through the postoperative follow-up period. Enhancement was observed in 41% (28/68) of cases beyond the 18-month interval but was uncommon after 5 years. Pattern III enhancement was the morphologic pattern seen most commonly with malignancy (5/19 cases, 26%). When associated with delayed washout kinetics, it was even more strongly predictive of malignancy (4/5 cases, 80%). In patients with a history of excisional biopsy and no prior radiation treatment, the percentage of MRI scans showing enhancement was significantly lower than (21% vs 49% with P-value .0027) in patients who had undergone radiation. Enhancement at the surgical site occurred in one-third of cases up to 5 years after surgery, particularly in patients who underwent both radiation and surgery. Mass enhancement, particularly in conjunction with delayed washout kinetics, is most predictive of malignancy and should prompt biopsy or re-excision. © 2018 Wiley Periodicals, Inc.

Hemorrhage detection in MRI brain images using images features

NASA Astrophysics Data System (ADS)

Moraru, Luminita; Moldovanu, Simona; Bibicu, Dorin; Stratulat (Visan), Mirela

2013-11-01

The abnormalities appear frequently on Magnetic Resonance Images (MRI) of brain in elderly patients presenting either stroke or cognitive impairment. Detection of brain hemorrhage lesions in MRI is an important but very time-consuming task. This research aims to develop a method to extract brain tissue features from T2-weighted MR images of the brain using a selection of the most valuable texture features in order to discriminate between normal and affected areas of the brain. Due to textural similarity between normal and affected areas in brain MR images these operation are very challenging. A trauma may cause microstructural changes, which are not necessarily perceptible by visual inspection, but they could be detected by using a texture analysis. The proposed analysis is developed in five steps: i) in the pre-processing step: the de-noising operation is performed using the Daubechies wavelets; ii) the original images were transformed in image features using the first order descriptors; iii) the regions of interest (ROIs) were cropped from images feature following up the axial symmetry properties with respect to the mid - sagittal plan; iv) the variation in the measurement of features was quantified using the two descriptors of the co-occurrence matrix, namely energy and homogeneity; v) finally, the meaningful of the image features is analyzed by using the t-test method. P-value has been applied to the pair of features in order to measure they efficacy.

Clinical use of MRI for the evaluation of acute appendicitis during pregnancy.

PubMed

Patel, Darshan; Fingard, Jordan; Winters, Sean; Low, Gavin

2017-07-01

The purpose of this study was to determine the diagnostic accuracy of MRI for detecting acute appendicitis in pregnancy in a multi-institution study involving general body MR readers with no specific expertise in MR imaging of the pregnant patient. Retrospective review of MRI examinations on PACS in 42 pregnant patients was evaluated for acute right lower quadrant pain. Three fellowship-trained general body radiologists analyzed the MRI examinations in consensus and attempted to localize the appendix, assess for features of appendicitis, and exclude alternative etiologies for the right lower quadrant pain. Of the 42 MRI examinations, the readers noted 6 cases of acute appendicitis, 16 cases of a normal appendix, and 20 cases involving non-visualization of the appendix but where there were no secondary features of acute appendicitis. Based on the surgical data and clinical follow-up, there were 3 true-positive cases, 3 false-positive cases, 34 true-negative cases, and 2 false-negative cases of acute appendicitis on MRI. This yielded an accuracy of 88.1%, sensitivity of 60%, specificity of 91.9%, positive predictive value of 50%, and negative predictive value of 94.4% for the detection of acute appendicitis in the pregnant patient on MRI. Alternative etiologies for the right lower quadrant pain on MRI included torsion of an ovarian dermoid in 1 case and pyelonephritis in 1 case. MRI is an excellent modality for excluding acute appendicitis in pregnant patients presenting with right lower quadrant pain.

Bayesian uncertainty quantification in linear models for diffusion MRI.

PubMed

Sjölund, Jens; Eklund, Anders; Özarslan, Evren; Herberthson, Magnus; Bånkestad, Maria; Knutsson, Hans

2018-03-29

Diffusion MRI (dMRI) is a valuable tool in the assessment of tissue microstructure. By fitting a model to the dMRI signal it is possible to derive various quantitative features. Several of the most popular dMRI signal models are expansions in an appropriately chosen basis, where the coefficients are determined using some variation of least-squares. However, such approaches lack any notion of uncertainty, which could be valuable in e.g. group analyses. In this work, we use a probabilistic interpretation of linear least-squares methods to recast popular dMRI models as Bayesian ones. This makes it possible to quantify the uncertainty of any derived quantity. In particular, for quantities that are affine functions of the coefficients, the posterior distribution can be expressed in closed-form. We simulated measurements from single- and double-tensor models where the correct values of several quantities are known, to validate that the theoretically derived quantiles agree with those observed empirically. We included results from residual bootstrap for comparison and found good agreement. The validation employed several different models: Diffusion Tensor Imaging (DTI), Mean Apparent Propagator MRI (MAP-MRI) and Constrained Spherical Deconvolution (CSD). We also used in vivo data to visualize maps of quantitative features and corresponding uncertainties, and to show how our approach can be used in a group analysis to downweight subjects with high uncertainty. In summary, we convert successful linear models for dMRI signal estimation to probabilistic models, capable of accurate uncertainty quantification. Copyright © 2018 Elsevier Inc. All rights reserved.

Development of a Comprehensive Osteochondral Allograft MRI Scoring System (OCAMRISS) With Histopathologic, Micro–Computed Tomography, and Biomechanical Validation

PubMed Central

Pallante-Kichura, Andrea L.; Bae, Won C.; Du, Jiang; Statum, Sheronda; Wolfson, Tanya; Gamst, Anthony C.; Cory, Esther; Amiel, David; Bugbee, William D.; Sah, Robert L.; Chung, Christine B.

2014-01-01

Objective: To describe and apply a semiquantitative MRI scoring system for multifeature analysis of cartilage defect repair in the knee by osteochondral allografts and to correlate this scoring system with histopathologic, micro–computed tomography (µCT), and biomechanical reference standards using a goat repair model. Design: Fourteen adult goats had 2 osteochondral allografts implanted into each knee: one in the medial femoral condyle and one in the lateral trochlea. At 12 months, goats were euthanized and MRI was performed. Two blinded radiologists independently rated 9 primary features for each graft, including cartilage signal, fill, edge integration, surface congruity, calcified cartilage integrity, subchondral bone plate congruity, subchondral bone marrow signal, osseous integration, and presence of cystic changes. Four ancillary features of the joint were also evaluated, including opposing cartilage, meniscal tears, synovitis, and fat-pad scarring. Comparison was made with histologic and µCT reference standards as well as biomechanical measures. Interobserver agreement and agreement with reference standards was assessed. Cohen’s κ, Spearman’s correlation, and Kruskal-Wallis tests were used as appropriate. Results: There was substantial agreement (κ > 0.6, P < 0.001) for each MRI feature and with comparison against reference standards, except for cartilage edge integration (κ = 0.6). There was a strong positive correlation between MRI and reference standard scores (ρ = 0.86, P < 0.01). Osteochondral allograft MRI scoring system was sensitive to differences in outcomes between the types of allografts. Conclusions: We have described a comprehensive MRI scoring system for osteochondral allografts and have validated this scoring system with histopathologic and µCT reference standards as well as biomechanical indentation testing. PMID:24489999

Prognostic significance of MRI findings in patients with myxoid-round cell liposarcoma.

PubMed

Tateishi, Ukihide; Hasegawa, Tadashi; Beppu, Yasuo; Kawai, Akira; Satake, Mitsuo; Moriyama, Noriyuki

2004-03-01

The aims of this study were to determine the prognostic significance of MRI findings in patients with myxoid-round cell liposarcomas and to clarify which MRI features best indicate tumors with adverse clinical behavior. The initial MRI studies of 36 pathologically confirmed myxoid-round cell liposarcomas were retrospectively reviewed, and observations from this review were correlated with the histopathologic features. MR images were evaluated by two radiologists with agreement by consensus, and both univariate and multivariate analyses were conducted to evaluate survival with a median clinical follow-up of 33 months (range, 9-276 months). Statistically significant MRI findings that favored a diagnosis of intermediate- or high-grade tumor were large tumor size (> 10 cm), deeply situated tumor, tumor possessing irregular contours, absence of lobulation, absence of thin septa, presence of thick septa, absence of tumor capsule, high-intensity signal pattern, pronounced enhancement, and globular or nodular enhancement. Of these MRI findings, thin septa (p < 0.05), a tumor capsule (p < 0.01), and pronounced enhancement (p < 0.01) were associated significantly, according to univariate analysis, with overall survival. Multivariate analysis indicated that pronounced enhancement was associated significantly with overall survival (p < 0.05). Contrast-enhanced MRI findings can indicate a good or adverse prognosis in patients with myxoid-round cell liposarcomas.

[Optimization of diagnosis indicator selection and inspection plan by 3.0T MRI in breast cancer].

PubMed

Jiang, Zhongbiao; Wang, Yunhua; He, Zhong; Zhang, Lejun; Zheng, Kai

2013-08-01

To optimize 3.0T MRI diagnosis indicator in breast cancer and to select the best MRI scan program. Totally 45 patients with breast cancers were collected, and another 35 patients with benign breast tumor served as the control group. All patients underwent 3.0T MRI, including T1- weighted imaging (T1WI), fat suppression of the T2-weighted imaging (T2WI), diffusion weighted imaging (DWI), 1H magnetic resonance spectroscopy (1H-MRS) and dynamic contrast enhanced (DCE) sequence. With operation pathology results as the gold standard in the diagnosis of breast diseases, the pathological results of benign and malignant served as dependent variables, and the diagnostic indicators of MRI were taken as independent variables. We put all the indicators of MRI examination under Logistic regression analysis, established the Logistic model, and optimized the diagnosis indicators of MRI examination to further improve MRI scan of breast cancer. By Logistic regression analysis, some indicators were selected in the equation, including the edge feature of the tumor, the time-signal intensity curve (TIC) type and the apparent diffusion coefficient (ADC) value when b=500 s/mm2. The regression equation was Logit (P)=-21.936+20.478X6+3.267X7+ 21.488X3. Valuable indicators in the diagnosis of breast cancer are the edge feature of the tumor, the TIC type and the ADC value when b=500 s/mm2. Combining conventional MRI scan, DWI and dynamic enhanced MRI is a better examination program, while MRS is the complementary program when diagnosis is difficult.

Canonical Correlation Analysis for Feature-Based Fusion of Biomedical Imaging Modalities and Its Application to Detection of Associative Networks in Schizophrenia.

PubMed

Correa, Nicolle M; Li, Yi-Ou; Adalı, Tülay; Calhoun, Vince D

2008-12-01

Typically data acquired through imaging techniques such as functional magnetic resonance imaging (fMRI), structural MRI (sMRI), and electroencephalography (EEG) are analyzed separately. However, fusing information from such complementary modalities promises to provide additional insight into connectivity across brain networks and changes due to disease. We propose a data fusion scheme at the feature level using canonical correlation analysis (CCA) to determine inter-subject covariations across modalities. As we show both with simulation results and application to real data, multimodal CCA (mCCA) proves to be a flexible and powerful method for discovering associations among various data types. We demonstrate the versatility of the method with application to two datasets, an fMRI and EEG, and an fMRI and sMRI dataset, both collected from patients diagnosed with schizophrenia and healthy controls. CCA results for fMRI and EEG data collected for an auditory oddball task reveal associations of the temporal and motor areas with the N2 and P3 peaks. For the application to fMRI and sMRI data collected for an auditory sensorimotor task, CCA results show an interesting joint relationship between fMRI and gray matter, with patients with schizophrenia showing more functional activity in motor areas and less activity in temporal areas associated with less gray matter as compared to healthy controls. Additionally, we compare our scheme with an independent component analysis based fusion method, joint-ICA that has proven useful for such a study and note that the two methods provide complementary perspectives on data fusion.

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

Parekh, V; Jacobs, MA

Purpose: Multiparametric radiological imaging is used for diagnosis in patients. Potentially extracting useful features specific to a patient’s pathology would be crucial step towards personalized medicine and assessing treatment options. In order to automatically extract features directly from multiparametric radiological imaging datasets, we developed an advanced unsupervised machine learning algorithm called the multidimensional imaging radiomics-geodesics(MIRaGe). Methods: Seventy-six breast tumor patients underwent 3T MRI breast imaging were used for this study. We tested the MIRaGe algorithm to extract features for classification of breast tumors into benign or malignant. The MRI parameters used were T1-weighted, T2-weighted, dynamic contrast enhanced MR imaging (DCE-MRI)more » and diffusion weighted imaging(DWI). The MIRaGe algorithm extracted the radiomics-geodesics features (RGFs) from multiparametric MRI datasets. This enable our method to learn the intrinsic manifold representations corresponding to the patients. To determine the informative RGF, a modified Isomap algorithm(t-Isomap) was created for a radiomics-geodesics feature space(tRGFS) to avoid overfitting. Final classification was performed using SVM. The predictive power of the RGFs was tested and validated using k-fold cross validation. Results: The RGFs extracted by the MIRaGe algorithm successfully classified malignant lesions from benign lesions with a sensitivity of 93% and a specificity of 91%. The top 50 RGFs identified as the most predictive by the t-Isomap procedure were consistent with the radiological parameters known to be associated with breast cancer diagnosis and were categorized as kinetic curve characterizing RGFs, wash-in rate characterizing RGFs, wash-out rate characterizing RGFs and morphology characterizing RGFs. Conclusion: In this paper, we developed a novel feature extraction algorithm for multiparametric radiological imaging. The results demonstrated the power of the MIRaGe algorithm at automatically discovering useful feature representations directly from the raw multiparametric MRI data. In conclusion, the MIRaGe informatics model provides a powerful tool with applicability in cancer diagnosis and a possibility of extension to other kinds of pathologies. NIH (P50CA103175, 5P30CA006973 (IRAT), R01CA190299, U01CA140204), Siemens Medical Systems (JHU-2012-MR-86-01) and Nivida Graphics Corporation.« less

Automatic diagnosis of lumbar disc herniation with shape and appearance features from MRI

NASA Astrophysics Data System (ADS)

Alomari, Raja'S.; Corso, Jason J.; Chaudhary, Vipin; Dhillon, Gurmeet

2010-03-01

Intervertebral disc herniation is a major reason for lower back pain (LBP), which is the second most common neurological ailment in the United States. Automation of herniated disc diagnosis reduces the large burden on radiologists who have to diagnose hundreds of cases each day using clinical MRI. We present a method for automatic diagnosis of lumbar disc herniation using appearance and shape features. We jointly use the intensity signal for modeling the appearance of herniated disc and the active shape model for modeling the shape of herniated disc. We utilize a Gibbs distribution for classification of discs using appearance and shape features. We use 33 clinical MRI cases of the lumbar area for training and testing both appearance and shape models. We achieve over 91% accuracy in detection of herniation in a cross-validation experiment with specificity of 91% and sensitivity of 94%.

Cost-effectiveness of alternative strategies for integrating MRI into breast cancer screening for women at high risk.

PubMed

Ahern, C H; Shih, Y-C T; Dong, W; Parmigiani, G; Shen, Y

2014-10-14

Magnetic resonance imaging (MRI) is recommended for women at high risk for breast cancer. We evaluated the cost-effectiveness of alternative screening strategies involving MRI. Using a microsimulation model, we generated life histories under different risk profiles, and assessed the impact of screening on quality-adjusted life-years, and lifetime costs, both discounted at 3%. We compared 12 screening strategies combining annual or biennial MRI with mammography and clinical breast examination (CBE) in intervals of 0.5, 1, or 2 years vs without, and reported incremental cost-effectiveness ratios (ICERs). Based on an ICER threshold of $100,000/QALY, the most cost-effective strategy for women at 25% lifetime risk was to stagger MRI and mammography plus CBE every year from age 30 to 74, yielding ICER $58,400 (compared to biennial MRI alone). At 50% lifetime risk and with 70% reduction in MRI cost, the recommended strategy was to stagger MRI and mammography plus CBE every 6 months (ICER=$84,400). At 75% lifetime risk, the recommended strategy is biennial MRI combined with mammography plus CBE every 6 months (ICER=$62,800). The high costs of MRI and its lower specificity are limiting factors for annual screening schedule of MRI, except for women at sufficiently high risk.

A narrative overview of the current status of MRI of the hip and its relevance for osteoarthritis research - what we know, what has changed and where are we going?

PubMed

Crema, M D; Watts, G J; Guermazi, A; Kim, Y-J; Kijowski, R; Roemer, F W

2017-01-01

To review and discuss the role of magnetic resonance imaging (MRI) in the context of hip osteoarthritis (OA) research. The content of this narrative review, based on an extensive PubMed database research including English literature only, describes the advances in MRI of the hip joint and its potential usefulness in hip OA research, reviews the relevance of different MRI features in regard to symptomatic and structural progression in hip OA, and gives an outlook regarding future use of MRI in hip OA research endeavors. Recent technical advances have helped to overcome many of the past difficulties related to MRI assessment of hip OA. MRI-based morphologic scoring systems allow for detailed assessment of several hip joint tissues and, in combination with the recent advances in MRI, may increase reproducibility and sensitivity to change. Compositional MRI techniques may add to our understanding of disease onset and progression. Knowledge about imaging pitfalls and anatomical variants is crucial to avoid misinterpretation. In comparison to research on knee OA, the associations between MRI features and the incidence and progression of disease as well as with clinical symptoms have been little explored. Anatomic alterations of the hip joint as seen in femoro-acetabular impingement (FAI) seem to play a role in the onset and progression of structural damage. With the technical advances occurring in recent years, MRI may play a major role in investigating the natural history of hip OA and provide an improved method for assessment of the efficacy of new therapeutic approaches. Copyright © 2016 Osteoarthritis Research Society International. Published by Elsevier Ltd. All rights reserved.

MRI-guided Breast Biopsy: Outcomes and Impact on Patient Management

PubMed Central

Kamel, Ihab R; Macura, Katarzyna J

2014-01-01

Introduction The purpose of this study was to correlate the pathology results of magnetic resonance imaging (MRI)-guided breast biopsies at our institution to MRI findings and patient clinical history characteristics. The impact of MRI-guided breast biopsies on surgical management in patients with a new diagnosis of breast cancer was also assessed. Patients and Methods In this HIPAA-compliant study we retrospectively reviewed all MRI-guided breast biopsies performed 3/2006–5/2012. Clinical history, MRI features and pathology outcomes were reviewed. In patients undergoing breast MRI to evaluate extent of disease, any change in surgical management resulting from the MRI-guided biopsy was recorded. Statistical analysis included binary logistic regression and independent student’s t-test. Results Two-hundred fifteen lesions in 168 patients were included, of which 23 (10.7%) were malignant, 43 (20%) were high risk, and 149 (69.3%) were benign. No clinical characteristic was associated with malignancy in our cohort. MRI features associated with malignancy were: larger size (mean 2.6 cm versus 1.3 cm, p=0.046), washout kinetics (18% malignancy rate, p=0.02) and marked background parenchymal enhancement (40% malignancy rate, p-value <0.001 to 0.03). Nineteen (28%) of the 67 patients with a new diagnosis of breast cancer undergoing MRI-guided breast biopsy had a change in surgical management based on the biopsy result. Conclusions Malignancy rate was associated with lesion size, washout kinetics and marked background enhancement of the breast parenchyma but was not associated with any clinical history characteristics. Pre-operative MRI-guided breast biopsies changed surgical management in 28% of women with a new diagnosis of breast cancer. PMID:25499596

Carotid Artery Disease and Stroke: Assessing Risk with Vessel Wall MRI

PubMed Central

Kerwin, William S.

2012-01-01

Although MRI is widely used to diagnose stenotic carotid arteries, it also detects characteristics of the atherosclerotic plaque itself, including its size, composition, and activity. These features are emerging as additional risk factors for stroke that can be feasibly acquired clinically. This paper summarizes the state of evidence for a clinical role for MRI of carotid atherosclerosis. PMID:23209940

Support vector machine for breast cancer classification using diffusion-weighted MRI histogram features: Preliminary study.

PubMed

Vidić, Igor; Egnell, Liv; Jerome, Neil P; Teruel, Jose R; Sjøbakk, Torill E; Østlie, Agnes; Fjøsne, Hans E; Bathen, Tone F; Goa, Pål Erik

2018-05-01

Diffusion-weighted MRI (DWI) is currently one of the fastest developing MRI-based techniques in oncology. Histogram properties from model fitting of DWI are useful features for differentiation of lesions, and classification can potentially be improved by machine learning. To evaluate classification of malignant and benign tumors and breast cancer subtypes using support vector machine (SVM). Prospective. Fifty-one patients with benign (n = 23) and malignant (n = 28) breast tumors (26 ER+, whereof six were HER2+). Patients were imaged with DW-MRI (3T) using twice refocused spin-echo echo-planar imaging with echo time / repetition time (TR/TE) = 9000/86 msec, 90 × 90 matrix size, 2 × 2 mm in-plane resolution, 2.5 mm slice thickness, and 13 b-values. Apparent diffusion coefficient (ADC), relative enhanced diffusivity (RED), and the intravoxel incoherent motion (IVIM) parameters diffusivity (D), pseudo-diffusivity (D*), and perfusion fraction (f) were calculated. The histogram properties (median, mean, standard deviation, skewness, kurtosis) were used as features in SVM (10-fold cross-validation) for differentiation of lesions and subtyping. Accuracies of the SVM classifications were calculated to find the combination of features with highest prediction accuracy. Mann-Whitney tests were performed for univariate comparisons. For benign versus malignant tumors, univariate analysis found 11 histogram properties to be significant differentiators. Using SVM, the highest accuracy (0.96) was achieved from a single feature (mean of RED), or from three feature combinations of IVIM or ADC. Combining features from all models gave perfect classification. No single feature predicted HER2 status of ER + tumors (univariate or SVM), although high accuracy (0.90) was achieved with SVM combining several features. Importantly, these features had to include higher-order statistics (kurtosis and skewness), indicating the importance to account for heterogeneity. Our findings suggest that SVM, using features from a combination of diffusion models, improves prediction accuracy for differentiation of benign versus malignant breast tumors, and may further assist in subtyping of breast cancer. 3 Technical Efficacy: Stage 3 J. Magn. Reson. Imaging 2018;47:1205-1216. © 2017 International Society for Magnetic Resonance in Medicine.

Brain imaging in mitochondrial respiratory chain deficiency: combination of brain MRI features as a useful tool for genotype/phenotype correlations.

PubMed

Bricout, M; Grévent, D; Lebre, A S; Rio, M; Desguerre, I; De Lonlay, P; Valayannopoulos, V; Brunelle, F; Rötig, A; Munnich, A; Boddaert, N

2014-07-01

Mitochondrial diseases are characterised by a broad clinical and genetic heterogeneity that makes diagnosis difficult. Owing to the wide pattern of symptoms in mitochondrial disorders and the constantly growing number of disease genes, their genetic diagnosis is difficult and genotype/phenotype correlations remain elusive. Brain MRI appears as a useful tool for genotype/phenotype correlations. Here, we summarise the various combinations of MRI lesions observed in the most frequent mitochondrial respiratory chain deficiencies so as to direct molecular genetic test in patients at risk of such diseases. We believe that the combination of brain MRI features is of value to support respiratory chain deficiency and direct molecular genetic tests. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.

A Hybrid of Deep Network and Hidden Markov Model for MCI Identification with Resting-State fMRI.

PubMed

Suk, Heung-Il; Lee, Seong-Whan; Shen, Dinggang

2015-10-01

In this paper, we propose a novel method for modelling functional dynamics in resting-state fMRI (rs-fMRI) for Mild Cognitive Impairment (MCI) identification. Specifically, we devise a hybrid architecture by combining Deep Auto-Encoder (DAE) and Hidden Markov Model (HMM). The roles of DAE and HMM are, respectively, to discover hierarchical non-linear relations among features, by which we transform the original features into a lower dimension space, and to model dynamic characteristics inherent in rs-fMRI, i.e. , internal state changes. By building a generative model with HMMs for each class individually, we estimate the data likelihood of a test subject as MCI or normal healthy control, based on which we identify the clinical label. In our experiments, we achieved the maximal accuracy of 81.08% with the proposed method, outperforming state-of-the-art methods in the literature.

A Hybrid of Deep Network and Hidden Markov Model for MCI Identification with Resting-State fMRI

PubMed Central

Suk, Heung-Il; Lee, Seong-Whan; Shen, Dinggang

2015-01-01

In this paper, we propose a novel method for modelling functional dynamics in resting-state fMRI (rs-fMRI) for Mild Cognitive Impairment (MCI) identification. Specifically, we devise a hybrid architecture by combining Deep Auto-Encoder (DAE) and Hidden Markov Model (HMM). The roles of DAE and HMM are, respectively, to discover hierarchical non-linear relations among features, by which we transform the original features into a lower dimension space, and to model dynamic characteristics inherent in rs-fMRI, i.e., internal state changes. By building a generative model with HMMs for each class individually, we estimate the data likelihood of a test subject as MCI or normal healthy control, based on which we identify the clinical label. In our experiments, we achieved the maximal accuracy of 81.08% with the proposed method, outperforming state-of-the-art methods in the literature. PMID:27054199

MR imaging of the traumatic triangular fibrocartilaginous complex tear

PubMed Central

Griffith, James F.; Fung, Cindy S. Y.; Lee, Ryan K. L.; Tong, Cina S. L.; Wong, Clara W. Y.; Tse, Wing Lim; Ho, Pak Cheong

2017-01-01

Triangular fibrocartilage complex is a major stabilizer of the distal radioulnar joint (DRUJ). However, triangular fibrocartilage complex (TFCC) tear is difficult to be diagnosed on MRI for its intrinsic small and thin structure with complex anatomy. The purpose of this article is to review the anatomy of TFCC, state of art MRI imaging technique, normal appearance and features of tear on MRI according to the Palmar’s classification. Atypical tear and limitations of MRI in diagnosis of TFCC tear are also discussed. PMID:28932701

MRI texture analysis (MRTA) of T2-weighted images in Crohn's disease may provide information on histological and MRI disease activity in patients undergoing ileal resection.

PubMed

Makanyanga, Jesica; Ganeshan, Balaji; Rodriguez-Justo, Manuel; Bhatnagar, Gauraang; Groves, Ashley; Halligan, Steve; Miles, Ken; Taylor, Stuart A

2017-02-01

To associate MRI textural analysis (MRTA) with MRI and histological Crohn's disease (CD) activity. Sixteen patients (mean age 39.5 years, 9 male) undergoing MR enterography before ileal resection were retrospectively analysed. Thirty-six small (≤3 mm) ROIs were placed on T2-weighted images and location-matched histological acute inflammatory scores (AIS) measured. MRI activity (mural thickness, T2 signal, T1 enhancement) (CDA) was scored in large ROIs. MRTA features (mean, standard deviation, mean of positive pixels (MPP), entropy, kurtosis, skewness) were extracted using a filtration histogram technique. Spatial scale filtration (SSF) ranged from 2 to 5 mm. Regression (linear/logistic) tested associations between MRTA and AIS (small ROIs), and CDA/constituent parameters (large ROIs). Skewness (SSF = 2 mm) was associated with AIS [regression coefficient (rc) 4.27, p = 0.02]. Of 120 large ROI analyses (for each MRI, MRTA feature and SSF), 15 were significant. Entropy (SSF = 2, 3 mm) and kurtosis (SSF = 3 mm) were associated with CDA (rc 0.9, 1.0, -0.45, p = 0.006-0.01). Entropy and mean (SSF = 2-4 mm) were associated with T2 signal [odds ratio (OR) 2.32-3.16, p = 0.02-0.004], [OR 1.22-1.28, p = 0.03-0.04]. MPP (SSF = 2 mm) was associated with mural thickness (OR 0.91, p = 0.04). Kurtosis (SSF = 3 mm), standard deviation (SSF = 5 mm) were associated with decreased T1 enhancement (OR 0.59, 0.42, p = 0.004, 0.007). MRTA features may be associated with CD activity. • MR texture analysis features may be associated with Crohn's disease histological activity. • Texture analysis features may correlate with MR-dependent Crohn's disease activity scores. • The utility of MR texture analysis in Crohn's disease merits further investigation.

Evaluation of Blalock-Taussig shunts in newborns: value of oblique MRI planes.

PubMed

Kastler, B; Livolsi, A; Germain, P; Zöllner, G; Dietemann, J L

1991-01-01

Eight infants with systemic-pulmonary Blalock-Taussig shunts were evaluated by spin-echo ECG-gated MRI. Contrary to Echocardiography, MRI using coronal oblique projections successfully visualized all palliative shunts entirely in one single plane (including one carried out on a right aberrant subclavian artery). MRI allowed assessment of size, course and patency of the shunt, including pulmonary and subclavian insertion. The proximal portion of the pulmonary and subclavian arteries were also visualized. We conclude that MRI with axial scans completed by coronal oblique planes is a promising, non invasive method for imaging the anatomical features of Blalock-Taussig shunts.

Are rheumatoid arthritis patients discernible from other early arthritis patients using 1.5T extremity magnetic resonance imaging? a large cross-sectional study.

PubMed

Stomp, Wouter; Krabben, Annemarie; van der Heijde, Désirée; Huizinga, Tom W J; Bloem, Johan L; van der Helm-van Mil, Annette H M; Reijnierse, Monique

2014-08-01

Magnetic resonance imaging (MRI) is increasingly used in rheumatoid arthritis (RA) research. A European League Against Rheumatism (EULAR) task force recently suggested that MRI can improve the certainty of RA diagnosis. Because this recommendation may reflect a tendency to use MRI in daily practice, thorough studies on the value of MRI are required. Thus far no large studies have evaluated the accuracy of MRI to differentiate early RA from other patients with early arthritis. We performed a large cross-sectional study to determine whether patients who are clinically classified with RA differ in MRI features compared to patients with other diagnoses. In our study, 179 patients presenting with early arthritis (median symptom duration 15.4 weeks) underwent 1.5T extremity MRI of unilateral wrist, metacarpophalangeal, and metatarsophalangeal joints according to our arthritis protocol, the foot without contrast. Images were scored according to OMERACT Rheumatoid Arthritis Magnetic Resonance Imaging Scoring (RAMRIS) by 2 independent readers. Tenosynovitis was also assessed. The main outcome was fulfilling the 1987 American College of Rheumatology (ACR) criteria for RA. Test characteristics and areas under the receiver-operator-characteristic curves (AUC) were evaluated. In subanalyses, the 2010 ACR/EULAR criteria were used as outcome, and analyses were stratified for anticitrullinated protein antibodies (ACPA). The ACR 1987 criteria were fulfilled in 43 patients (24.0%). Patients with RA had higher scores for synovitis, tenosynovitis, and bone marrow edema (BME) than patients without RA (p < 0.05). ACPA-positive patients had more BME (median scores 6.5 vs. 4.25, p = 0.016) than ACPA-negative patients. For all MRI features, the predictive value for the presence of RA was low (< 50%). For all MRI features the AUC were < 0.70. Patients who fulfilled ACR/EULAR 2010 criteria but not ACR87 criteria for RA had less synovitis than patients who were positive for RA according to both sets of criteria (p = 0.029). Although patients with RA had higher scores of MRI inflammation and ACPA-positive patients had more BME, the severity of MRI inflammation assessed according to RAMRIS does not accurately differentiate patients with RA from other early arthritis patients.

Efficacy Evaluation of Different Wavelet Feature Extraction Methods on Brain MRI Tumor Detection

NASA Astrophysics Data System (ADS)

Nabizadeh, Nooshin; John, Nigel; Kubat, Miroslav

2014-03-01

Automated Magnetic Resonance Imaging brain tumor detection and segmentation is a challenging task. Among different available methods, feature-based methods are very dominant. While many feature extraction techniques have been employed, it is still not quite clear which of feature extraction methods should be preferred. To help improve the situation, we present the results of a study in which we evaluate the efficiency of using different wavelet transform features extraction methods in brain MRI abnormality detection. Applying T1-weighted brain image, Discrete Wavelet Transform (DWT), Discrete Wavelet Packet Transform (DWPT), Dual Tree Complex Wavelet Transform (DTCWT), and Complex Morlet Wavelet Transform (CMWT) methods are applied to construct the feature pool. Three various classifiers as Support Vector Machine, K Nearest Neighborhood, and Sparse Representation-Based Classifier are applied and compared for classifying the selected features. The results show that DTCWT and CMWT features classified with SVM, result in the highest classification accuracy, proving of capability of wavelet transform features to be informative in this application.

An Automatic Image Processing Workflow for Daily Magnetic Resonance Imaging Quality Assurance.

PubMed

Peltonen, Juha I; Mäkelä, Teemu; Sofiev, Alexey; Salli, Eero

2017-04-01

The performance of magnetic resonance imaging (MRI) equipment is typically monitored with a quality assurance (QA) program. The QA program includes various tests performed at regular intervals. Users may execute specific tests, e.g., daily, weekly, or monthly. The exact interval of these measurements varies according to the department policies, machine setup and usage, manufacturer's recommendations, and available resources. In our experience, a single image acquired before the first patient of the day offers a low effort and effective system check. When this daily QA check is repeated with identical imaging parameters and phantom setup, the data can be used to derive various time series of the scanner performance. However, daily QA with manual processing can quickly become laborious in a multi-scanner environment. Fully automated image analysis and results output can positively impact the QA process by decreasing reaction time, improving repeatability, and by offering novel performance evaluation methods. In this study, we have developed a daily MRI QA workflow that can measure multiple scanner performance parameters with minimal manual labor required. The daily QA system is built around a phantom image taken by the radiographers at the beginning of day. The image is acquired with a consistent phantom setup and standardized imaging parameters. Recorded parameters are processed into graphs available to everyone involved in the MRI QA process via a web-based interface. The presented automatic MRI QA system provides an efficient tool for following the short- and long-term stability of MRI scanners.

Device for sectioning prostatectomy specimens to facilitate comparison between histology and in vivo MRI

PubMed Central

Drew, Bryn; Jones, Edward C.; Reinsberg, Stefan; Yung, Andrew C.; Goldenberg, S. Larry; Kozlowski, Piotr

2012-01-01

Purpose To develop a device for sectioning prostatectomy specimens that would facilitate comparison between histology and in vivo MRI. Materials and methods A multi-bladed cutting device was developed, which consists of an adjustable box capable of accommodating a prostatectomy specimen up to 85 mm in size in the lateral direction, a “plunger” tool to press on the excised gland from the top to prevent it from rolling or sliding during sectioning, and a multi-bladed knife assembly capable of holding up to 21 blades at 4 mm intervals. The device was tested on a formalin fixed piece of meat and subsequently used to section a prostatectomy specimen. Histology sections were compared with T2-weighted MR images acquired in vivo prior to the prostatectomy procedure. Results The prostatectomy specimen slices were very uniform in thickness with each face parallel to the other with no visible sawing marks on the sections by the blades after the cut. MRI and histology comparison showed good correspondence between the two images. Conclusion The developed device allows sectioning of prostatectomy specimens into parallel cuts at a specific orientation and fixed intervals. Such a device is useful in facilitating accurate correlation between histology and MRI data. PMID:20882632

Spoiled gradient recalled acquisition in the steady state technique is superior to conventional postcontrast spin echo technique for magnetic resonance imaging detection of adrenocorticotropin-secreting pituitary tumors.

PubMed

Patronas, Nicholas; Bulakbasi, Nail; Stratakis, Constantine A; Lafferty, Antony; Oldfield, Edward H; Doppman, John; Nieman, Lynnette K

2003-04-01

Recent studies show that the standard T1-weighted spin echo (SE) technique for magnetic resonance imaging (MRI) fails to identify 40% of corticotrope adenomas. We hypothesized that the superior soft tissue contrast and thinner sections obtained with spoiled gradient recalled acquisition in the steady state (SPGR) would improve tumor detection. We compared the performance of SE and SPGR MRI in 50 patients (age, 7-67 yr) with surgically confirmed corticotrope adenoma. Coronal SE and SPGR MR images were obtained before and after administration of gadolinium contrast, using a 1.5 T scanner. SE scans were obtained over 5.1 min (12-cm field of view; interleaved sections, 3 mm). SPGR scans were obtained over 3.45 min (12- or 18-cm field of view, contiguous 1- or 2-mm slices). The MRI interpretations of two radiologists were compared with findings at surgical resection. Compared with SE for detection of tumor, SPGR had superior sensitivity (80%; confidence interval, 68-91; vs. 49%; confidence interval, 34-63%), but a higher false positive rate (2% vs. 4%). We recommend the addition of SPGR to SE sequences using pituitary-specific technical parameters to improve the MRI detection of ACTH-secreting pituitary tumors.

Thalamofrontal neurodevelopment in new-onset pediatric idiopathic generalized epilepsy

PubMed Central

Dabbs, K.; Tuchsherer, V.; Sheth, R.D.; Koehn, M.A.; Hermann, B.P.; Seidenberg, M.

2011-01-01

Background: Quantitative MRI techniques have demonstrated thalamocortical abnormalities in idiopathic generalized epilepsy (IGE). However, there are few studies examining IGE early in its course and the neurodevelopmental course of this region is not adequately defined. Objective: We examined the 2-year developmental course of the thalamus and frontal lobes in pediatric new-onset IGE (i.e., within 12 months of diagnosis). Methods: We performed whole-brain MRI in 22 patients with new-onset IGE and 36 age-matched healthy controls. MRI was repeated 24 months after baseline MRI. Quantitative volumetrics were used to examine thalamic and frontal lobe volumes. Results: The IGE group showed significant differences in thalamic volume within 1 year of seizure onset (baseline) and went on to show thalamic volume loss at a significantly faster rate than healthy control children over the 2-year interval. The control group also showed a significantly greater increase in frontal white matter expansion than the IGE group. In contrast, frontal lobe gray matter volume differences were moderate at baseline and persisted over time, indicating similar developmental trajectories with differences early in the disease process that are maintained. Conclusions: Brain tissue abnormalities in thalamic and frontal regions can be identified very early in the course of IGE and an abnormal trajectory of growth continues over a 2-year interval. PMID:21205692

Thalamofrontal neurodevelopment in new-onset pediatric idiopathic generalized epilepsy.

PubMed

Pulsipher, D T; Dabbs, K; Tuchsherer, V; Sheth, R D; Koehn, M A; Hermann, B P; Seidenberg, M

2011-01-04

Quantitative MRI techniques have demonstrated thalamocortical abnormalities in idiopathic generalized epilepsy (IGE). However, there are few studies examining IGE early in its course and the neurodevelopmental course of this region is not adequately defined. We examined the 2-year developmental course of the thalamus and frontal lobes in pediatric new-onset IGE (i.e., within 12 months of diagnosis). We performed whole-brain MRI in 22 patients with new-onset IGE and 36 age-matched healthy controls. MRI was repeated 24 months after baseline MRI. Quantitative volumetrics were used to examine thalamic and frontal lobe volumes. The IGE group showed significant differences in thalamic volume within 1 year of seizure onset (baseline) and went on to show thalamic volume loss at a significantly faster rate than healthy control children over the 2-year interval. The control group also showed a significantly greater increase in frontal white matter expansion than the IGE group. In contrast, frontal lobe gray matter volume differences were moderate at baseline and persisted over time, indicating similar developmental trajectories with differences early in the disease process that are maintained. Brain tissue abnormalities in thalamic and frontal regions can be identified very early in the course of IGE and an abnormal trajectory of growth continues over a 2-year interval.

Spatio-temporal texture (SpTeT) for distinguishing vulnerable from stable atherosclerotic plaque on dynamic contrast enhancement (DCE) MRI in a rabbit model

PubMed Central

Wan, Tao; Madabhushi, Anant; Phinikaridou, Alkystis; Hamilton, James A.; Hua, Ning; Pham, Tuan; Danagoulian, Jovanna; Kleiman, Ross; Buckler, Andrew J.

2014-01-01

Purpose: To develop a new spatio-temporal texture (SpTeT) based method for distinguishing vulnerable versus stable atherosclerotic plaques on DCE-MRI using a rabbit model of atherothrombosis. Methods: Aortic atherosclerosis was induced in 20 New Zealand White rabbits by cholesterol diet and endothelial denudation. MRI was performed before (pretrigger) and after (posttrigger) inducing plaque disruption with Russell's-viper-venom and histamine. Of the 30 vascular targets (segments) under histology analysis, 16 contained thrombus (vulnerable) and 14 did not (stable). A total of 352 voxel-wise computerized SpTeT features, including 192 Gabor, 36 Kirsch, 12 Sobel, 52 Haralick, and 60 first-order textural features, were extracted on DCE-MRI to capture subtle texture changes in the plaques over the course of contrast uptake. Different combinations of SpTeT feature sets, in which the features were ranked by a minimum-redundancy-maximum-relevance feature selection technique, were evaluated via a random forest classifier. A 500 iterative 2-fold cross validation was performed for discriminating the vulnerable atherosclerotic plaque and stable atherosclerotic plaque on per voxel basis. Four quantitative metrics were utilized to measure the classification results in separating between vulnerable and stable plaques. Results: The quantitative results show that the combination of five classes of SpTeT features can distinguish between vulnerable (disrupted plaques with an overlying thrombus) and stable plaques with the best AUC values of 0.9631 ± 0.0088, accuracy of 89.98% ± 0.57%, sensitivity of 83.71% ± 1.71%, and specificity of 94.55% ± 0.48%. Conclusions: Vulnerable and stable plaque can be distinguished by SpTeT based features. The SpTeT features, following validation on larger datasets, could be established as effective and reliable imaging biomarkers for noninvasively assessing atherosclerotic risk. PMID:24694153

Is hypertension predictive of clinical recurrence in posterior reversible encephalopathy syndrome?

PubMed

Li, Richard; Mitchell, Peter; Dowling, Richard; Yan, Bernard

2013-02-01

Posterior reversible encephalopathy syndrome (PRES) has a distinctive clinical presentation and typical neuroimaging findings. However, data on its clinical course and recurrence are scarce. This study aims to investigate its clinical profile and factors that predict recurrence. We included patients diagnosed with PRES between 2005 and 2010 and collected data on demographics, presenting symptoms, co-morbidities, risk factors, clinical parameters, MRI findings, complications and recurrence. Patients were categorized into two groups: PRES due to primary hypertension and PRES due to secondary causes. Correlation with presenting symptoms, radiological features, and recurrence were analyzed. PRES was identified in 28 patients. Fourteen (50%) had primary hypertension. Secondary causes included immunosuppression-related (39%), preeclampsia/eclampsia (7%), and marijuana-intake-related (4%) causes. Patients presented with altered mental status (79%), headache (75%), seizure (68%), visual disturbance (39%) and hemiparesis (21%). On MRI 93% had the typical parietal-occipital involvement. The frontal lobe was affected in 64%, cerebellum in 29%, brainstem in 21%, and basal ganglia in 11%. About 36% had cortical involvement; 21% had diffusion-restricted lesions. Non-aneurysmal subarachnoid haemorrhage was found in 18% of patients and intracerebral hemorrhage in 14% of patients. No significant difference existed in presenting symptoms and the MRI distribution of vasogenic edema between the primary hypertension group and the secondary causes group. Recurrence occurred in four patients (14.3%, 95% confidence interval 4.2-33.7) and was significantly associated (p=0.05) with primary hypertension as the etiology. Intensive monitoring and treatment of hypertension is recommended for reducing morbidity. Copyright © 2012 Elsevier Ltd. All rights reserved.

MRI signal and texture features for the prediction of MCI to Alzheimer's disease progression

NASA Astrophysics Data System (ADS)

Martínez-Torteya, Antonio; Rodríguez-Rojas, Juan; Celaya-Padilla, José M.; Galván-Tejada, Jorge I.; Treviño, Victor; Tamez-Peña, José G.

2014-03-01

An early diagnosis of Alzheimer's disease (AD) confers many benefits. Several biomarkers from different information modalities have been proposed for the prediction of MCI to AD progression, where features extracted from MRI have played an important role. However, studies have focused almost exclusively in the morphological characteristics of the images. This study aims to determine whether features relating to the signal and texture of the image could add predictive power. Baseline clinical, biological and PET information, and MP-RAGE images for 62 subjects from the Alzheimer's Disease Neuroimaging Initiative were used in this study. Images were divided into 83 regions and 50 features were extracted from each one of these. A multimodal database was constructed, and a feature selection algorithm was used to obtain an accurate and small logistic regression model, which achieved a cross-validation accuracy of 0.96. These model included six features, five of them obtained from the MP-RAGE image, and one obtained from genotyping. A risk analysis divided the subjects into low-risk and high-risk groups according to a prognostic index, showing that both groups are statistically different (p-value of 2.04e-11). The results demonstrate that MRI features related to both signal and texture, add MCI to AD predictive power, and support the idea that multimodal biomarkers outperform single-modality biomarkers.

MRI-based quantification of Duchenne muscular dystrophy in a canine model

NASA Astrophysics Data System (ADS)

Wang, Jiahui; Fan, Zheng; Kornegay, Joe N.; Styner, Martin A.

2011-03-01

Duchenne muscular dystrophy (DMD) is a progressive and fatal X-linked disease caused by mutations in the DMD gene. Magnetic resonance imaging (MRI) has shown potential to provide non-invasive and objective biomarkers for monitoring disease progression and therapeutic effect in DMD. In this paper, we propose a semi-automated scheme to quantify MRI features of golden retriever muscular dystrophy (GRMD), a canine model of DMD. Our method was applied to a natural history data set and a hydrodynamic limb perfusion data set. The scheme is composed of three modules: pre-processing, muscle segmentation, and feature analysis. The pre-processing module includes: calculation of T2 maps, spatial registration of T2 weighted (T2WI) images, T2 weighted fat suppressed (T2FS) images, and T2 maps, and intensity calibration of T2WI and T2FS images. We then manually segment six pelvic limb muscles. For each of the segmented muscles, we finally automatically measure volume and intensity statistics of the T2FS images and T2 maps. For the natural history study, our results showed that four of six muscles in affected dogs had smaller volumes and all had higher mean intensities in T2 maps as compared to normal dogs. For the perfusion study, the muscle volumes and mean intensities in T2FS were increased in the post-perfusion MRI scans as compared to pre-perfusion MRI scans, as predicted. We conclude that our scheme successfully performs quantitative analysis of muscle MRI features of GRMD.

Prediction of outcome using pretreatment 18F-FDG PET/CT and MRI radiomics in locally advanced cervical cancer treated with chemoradiotherapy.

PubMed

Lucia, François; Visvikis, Dimitris; Desseroit, Marie-Charlotte; Miranda, Omar; Malhaire, Jean-Pierre; Robin, Philippe; Pradier, Olivier; Hatt, Mathieu; Schick, Ulrike

2018-05-01

The aim of this study is to determine if radiomics features from 18 fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT) and magnetic resonance imaging (MRI) images could contribute to prognoses in cervical cancer. One hundred and two patients (69 for training and 33 for testing) with locally advanced cervical cancer (LACC) receiving chemoradiotherapy (CRT) from 08/2010 to 12/2016 were enrolled in this study. 18 F-FDG PET/CT and MRI examination [T1, T2, T1C, diffusion-weighted imaging (DWI)] were performed for each patient before CRT. Primary tumor volumes were delineated with the fuzzy locally adaptive Bayesian algorithm in the PET images and with 3D Slicer™ in the MRI images. Radiomics features (intensity, shape, and texture) were extracted and their prognostic value was compared with clinical parameters for recurrence-free and locoregional control. In the training cohort, median follow-up was 3.0 years (range, 0.43-6.56 years) and relapse occurred in 36% of patients. In univariate analysis, FIGO stage (I-II vs. III-IV) and metabolic response (complete vs. non-complete) were probably associated with outcome without reaching statistical significance, contrary to several radiomics features from both PET and MRI sequences. Multivariate analysis in training test identified Grey Level Non Uniformity GLRLM in PET and Entropy GLCM in ADC maps from DWI MRI as independent prognostic factors. These had significantly higher prognostic power than clinical parameters, as evaluated in the testing cohort with accuracy of 94% for predicting recurrence and 100% for predicting lack of loco-regional control (versus ~50-60% for clinical parameters). In LACC treated with CRT, radiomics features such as EntropyGLCM and GLNUGLRLM from functional imaging DWI-MRI and PET, respectively, are independent predictors of recurrence and loco-regional control with significantly higher prognostic power than usual clinical parameters. Further research is warranted for their validation, which may justify more aggressive treatment in patients identified with high probability of recurrence.

Selective Audiovisual Semantic Integration Enabled by Feature-Selective Attention.

PubMed

Li, Yuanqing; Long, Jinyi; Huang, Biao; Yu, Tianyou; Wu, Wei; Li, Peijun; Fang, Fang; Sun, Pei

2016-01-13

An audiovisual object may contain multiple semantic features, such as the gender and emotional features of the speaker. Feature-selective attention and audiovisual semantic integration are two brain functions involved in the recognition of audiovisual objects. Humans often selectively attend to one or several features while ignoring the other features of an audiovisual object. Meanwhile, the human brain integrates semantic information from the visual and auditory modalities. However, how these two brain functions correlate with each other remains to be elucidated. In this functional magnetic resonance imaging (fMRI) study, we explored the neural mechanism by which feature-selective attention modulates audiovisual semantic integration. During the fMRI experiment, the subjects were presented with visual-only, auditory-only, or audiovisual dynamical facial stimuli and performed several feature-selective attention tasks. Our results revealed that a distribution of areas, including heteromodal areas and brain areas encoding attended features, may be involved in audiovisual semantic integration. Through feature-selective attention, the human brain may selectively integrate audiovisual semantic information from attended features by enhancing functional connectivity and thus regulating information flows from heteromodal areas to brain areas encoding the attended features.

Utility of texture analysis for quantifying hepatic fibrosis on proton density MRI.

PubMed

Yu, HeiShun; Buch, Karen; Li, Baojun; O'Brien, Michael; Soto, Jorge; Jara, Hernan; Anderson, Stephan W

2015-11-01

To evaluate the potential utility of texture analysis of proton density maps for quantifying hepatic fibrosis in a murine model of hepatic fibrosis. Following Institutional Animal Care and Use Committee (IACUC) approval, a dietary model of hepatic fibrosis was used and 15 ex vivo murine liver tissues were examined. All images were acquired using a 30 mm bore 11.7T magnetic resonance imaging (MRI) scanner with a multiecho spin-echo sequence. A texture analysis was employed extracting multiple texture features including histogram-based, gray-level co-occurrence matrix-based (GLCM), gray-level run-length-based features (GLRL), gray level gradient matrix (GLGM), and Laws' features. Texture features were correlated with histopathologic and digital image analysis of hepatic fibrosis. Histogram features demonstrated very weak to moderate correlations (r = -0.29 to 0.51) with hepatic fibrosis. GLCM features correlation and contrast demonstrated moderate-to-strong correlations (r = -0.71 and 0.59, respectively) with hepatic fibrosis. Moderate correlations were seen between hepatic fibrosis and the GLRL feature short run low gray-level emphasis (SRLGE) (r = -0. 51). GLGM features demonstrate very weak to weak correlations with hepatic fibrosis (r = -0.27 to 0.09). Moderate correlations were seen between hepatic fibrosis and Laws' features L6 and L7 (r = 0.58). This study demonstrates the utility of texture analysis applied to proton density MRI in a murine liver fibrosis model and validates the potential utility of texture-based features for the noninvasive, quantitative assessment of hepatic fibrosis. © 2015 Wiley Periodicals, Inc.

The imaging features of the meniscal roots on isotropic 3D MRI in young asymptomatic volunteers.

PubMed

Wang, Ping; Zhang, Cheng-Zhou; Zhang, Di; Liu, Quan-Yuan; Zhong, Xiao-Fei; Yin, Zhi-Jie; Wang, Bin

2018-05-01

This study aimed to describe clearly the normal imaging features of the meniscal roots on the magnetic resonance imaging (MRI) with a 3-dimensional (3D) proton density-weighted (PDW) sequence at 3T. A total of 60 knees of 31 young asymptomatic volunteers were examined using a 3D MRI. The insertion patterns, constitution patterns, and MR signals of the meniscal roots were recorded. The anterior root of the medial meniscus (ARMM), the anterior root of the lateral meniscus (ARLM), and the posterior root of the medial meniscus (PRMM) had 1 insertion site, whereas the posterior root of the lateral meniscus (PRLM) can be divided into major and minor insertion sites. The ARLM and the PRMM usually consisted of multiple fiber bundles (≥3), whereas the ARMM and the PRLM often consisted of a single fiber bundle. The ARMM and the PRLM usually appeared as hypointense, whereas the ARLM and the PRMM typically exhibited mixed signals. The meniscal roots can be complex and diverse, and certain characteristics of them were observed on 3D MRI. Understanding the normal imaging features of the meniscal roots is extremely beneficial for further diagnosis of root tears.

Parametric dictionary learning for modeling EAP and ODF in diffusion MRI.

PubMed

Merlet, Sylvain; Caruyer, Emmanuel; Deriche, Rachid

2012-01-01

In this work, we propose an original and efficient approach to exploit the ability of Compressed Sensing (CS) to recover diffusion MRI (dMRI) signals from a limited number of samples while efficiently recovering important diffusion features such as the ensemble average propagator (EAP) and the orientation distribution function (ODF). Some attempts to sparsely represent the diffusion signal have already been performed. However and contrarly to what has been presented in CS dMRI, in this work we propose and advocate the use of a well adapted learned dictionary and show that it leads to a sparser signal estimation as well as to an efficient reconstruction of very important diffusion features. We first propose to learn and design a sparse and parametric dictionary from a set of training diffusion data. Then, we propose a framework to analytically estimate in closed form two important diffusion features: the EAP and the ODF. Various experiments on synthetic, phantom and human brain data have been carried out and promising results with reduced number of atoms have been obtained on diffusion signal reconstruction, thus illustrating the added value of our method over state-of-the-art SHORE and SPF based approaches.

Deformable templates guided discriminative models for robust 3D brain MRI segmentation.

PubMed

Liu, Cheng-Yi; Iglesias, Juan Eugenio; Tu, Zhuowen

2013-10-01

Automatically segmenting anatomical structures from 3D brain MRI images is an important task in neuroimaging. One major challenge is to design and learn effective image models accounting for the large variability in anatomy and data acquisition protocols. A deformable template is a type of generative model that attempts to explicitly match an input image with a template (atlas), and thus, they are robust against global intensity changes. On the other hand, discriminative models combine local image features to capture complex image patterns. In this paper, we propose a robust brain image segmentation algorithm that fuses together deformable templates and informative features. It takes advantage of the adaptation capability of the generative model and the classification power of the discriminative models. The proposed algorithm achieves both robustness and efficiency, and can be used to segment brain MRI images with large anatomical variations. We perform an extensive experimental study on four datasets of T1-weighted brain MRI data from different sources (1,082 MRI scans in total) and observe consistent improvement over the state-of-the-art systems.

MRI and clinical features of maple syrup urine disease: preliminary results in 10 cases.

PubMed

Cheng, Ailan; Han, Lianshu; Feng, Yun; Li, Huimin; Yao, Rong; Wang, Dengbin; Jin, Biao

2017-01-01

We aimed to evaluate the magnetic resonance imaging (MRI) and clinical features of maple syrup urine disease (MSUD). This retrospective study consisted of 10 MSUD patients confirmed by genetic testing. All patients underwent brain MRI. Phenotype, genotype, and areas of brain injury on MRI were retrospectively reviewed. Six patients (60%) had the classic form of MSUD with BCKDHB mutation, three patients (30%) had the intermittent form (two with BCKDHA mutations and one with DBT mutation), and one patient (10%) had the thiamine-responsive form with DBT mutation. On diffusion-weighted imaging, nine cases presented restricted diffusion in myelinated areas, and one intermittent case with DBT mutation was normal. The classic form of MSUD involved the basal ganglia in six cases; the cerebellum, mesencephalon, pons, and supratentorial area in five cases; and the thalamus in four cases, respectively. The intermittent form involved the cerebellum, pons, and supratentorial area in two cases. The thiamine-responsive form involved the basal ganglia and supratentorial area. Our preliminary results indicate that patients with MSUD presented more commonly in classic form with BCKDHB mutation and displayed extensive brain injury on MRI.

A radiomics model from joint FDG-PET and MRI texture features for the prediction of lung metastases in soft-tissue sarcomas of the extremities

NASA Astrophysics Data System (ADS)

Vallières, M.; Freeman, C. R.; Skamene, S. R.; El Naqa, I.

2015-07-01

This study aims at developing a joint FDG-PET and MRI texture-based model for the early evaluation of lung metastasis risk in soft-tissue sarcomas (STSs). We investigate if the creation of new composite textures from the combination of FDG-PET and MR imaging information could better identify aggressive tumours. Towards this goal, a cohort of 51 patients with histologically proven STSs of the extremities was retrospectively evaluated. All patients had pre-treatment FDG-PET and MRI scans comprised of T1-weighted and T2-weighted fat-suppression sequences (T2FS). Nine non-texture features (SUV metrics and shape features) and forty-one texture features were extracted from the tumour region of separate (FDG-PET, T1 and T2FS) and fused (FDG-PET/T1 and FDG-PET/T2FS) scans. Volume fusion of the FDG-PET and MRI scans was implemented using the wavelet transform. The influence of six different extraction parameters on the predictive value of textures was investigated. The incorporation of features into multivariable models was performed using logistic regression. The multivariable modeling strategy involved imbalance-adjusted bootstrap resampling in the following four steps leading to final prediction model construction: (1) feature set reduction; (2) feature selection; (3) prediction performance estimation; and (4) computation of model coefficients. Univariate analysis showed that the isotropic voxel size at which texture features were extracted had the most impact on predictive value. In multivariable analysis, texture features extracted from fused scans significantly outperformed those from separate scans in terms of lung metastases prediction estimates. The best performance was obtained using a combination of four texture features extracted from FDG-PET/T1 and FDG-PET/T2FS scans. This model reached an area under the receiver-operating characteristic curve of 0.984 ± 0.002, a sensitivity of 0.955 ± 0.006, and a specificity of 0.926 ± 0.004 in bootstrapping evaluations. Ultimately, lung metastasis risk assessment at diagnosis of STSs could improve patient outcomes by allowing better treatment adaptation.

Automated prostate cancer localization without the need for peripheral zone extraction using multiparametric MRI.

PubMed

Liu, Xin; Yetik, Imam Samil

2011-06-01

Multiparametric magnetic resonance imaging (MRI) has been shown to have higher localization accuracy than transrectal ultrasound (TRUS) for prostate cancer. Therefore, automated cancer segmentation using multiparametric MRI is receiving a growing interest, since MRI can provide both morphological and functional images for tissue of interest. However, all automated methods to this date are applicable to a single zone of the prostate, and the peripheral zone (PZ) of the prostate needs to be extracted manually, which is a tedious and time-consuming job. In this paper, our goal is to remove the need of PZ extraction by incorporating the spatial and geometric information of prostate tumors with multiparametric MRI derived from T2-weighted MRI, diffusion-weighted imaging (DWI) and dynamic contrast enhanced MRI (DCE-MRI). In order to remove the need of PZ extraction, the authors propose a new method to incorporate the spatial information of the cancer. This is done by introducing a new feature called location map. This new feature is constructed by applying a nonlinear transformation to the spatial position coordinates of each pixel, so that the location map implicitly represents the geometric position of each pixel with respect to the prostate region. Then, this new feature is combined with multiparametric MR images to perform tumor localization. The proposed algorithm is applied to multiparametric prostate MRI data obtained from 20 patients with biopsy-confirmed prostate cancer. The proposed method which does not need the masks of PZ was found to have prostate cancer detection specificity of 0.84, sensitivity of 0.80 and dice coefficient value of 0.42. The authors have found that fusing the spatial information allows us to obtain tumor outline without the need of PZ extraction with a considerable success (better or similar performance to methods that require manual PZ extraction). Our experimental results quantitatively demonstrate the effectiveness of the proposed method, depicting that the proposed method has a slightly better or similar localization performance compared to methods which require the masks of PZ.

High-field fMRI unveils orientation columns in humans.

PubMed

Yacoub, Essa; Harel, Noam; Ugurbil, Kâmil

2008-07-29

Functional (f)MRI has revolutionized the field of human brain research. fMRI can noninvasively map the spatial architecture of brain function via localized increases in blood flow after sensory or cognitive stimulation. Recent advances in fMRI have led to enhanced sensitivity and spatial accuracy of the measured signals, indicating the possibility of detecting small neuronal ensembles that constitute fundamental computational units in the brain, such as cortical columns. Orientation columns in visual cortex are perhaps the best known example of such a functional organization in the brain. They cannot be discerned via anatomical characteristics, as with ocular dominance columns. Instead, the elucidation of their organization requires functional imaging methods. However, because of insufficient sensitivity, spatial accuracy, and image resolution of the available mapping techniques, thus far, they have not been detected in humans. Here, we demonstrate, by using high-field (7-T) fMRI, the existence and spatial features of orientation- selective columns in humans. Striking similarities were found with the known spatial features of these columns in monkeys. In addition, we found that a larger number of orientation columns are devoted to processing orientations around 90 degrees (vertical stimuli with horizontal motion), whereas relatively similar fMRI signal changes were observed across any given active column. With the current proliferation of high-field MRI systems and constant evolution of fMRI techniques, this study heralds the exciting prospect of exploring unmapped and/or unknown columnar level functional organizations in the human brain.

Estimation of trabecular bone parameters in children from multisequence MRI using texture-based regression

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

Lekadir, Karim, E-mail: karim.lekadir@upf.edu; Hoogendoorn, Corné; Armitage, Paul

Purpose: This paper presents a statistical approach for the prediction of trabecular bone parameters from low-resolution multisequence magnetic resonance imaging (MRI) in children, thus addressing the limitations of high-resolution modalities such as HR-pQCT, including the significant exposure of young patients to radiation and the limited applicability of such modalities to peripheral bones in vivo. Methods: A statistical predictive model is constructed from a database of MRI and HR-pQCT datasets, to relate the low-resolution MRI appearance in the cancellous bone to the trabecular parameters extracted from the high-resolution images. The description of the MRI appearance is achieved between subjects by usingmore » a collection of feature descriptors, which describe the texture properties inside the cancellous bone, and which are invariant to the geometry and size of the trabecular areas. The predictive model is built by fitting to the training data a nonlinear partial least square regression between the input MRI features and the output trabecular parameters. Results: Detailed validation based on a sample of 96 datasets shows correlations >0.7 between the trabecular parameters predicted from low-resolution multisequence MRI based on the proposed statistical model and the values extracted from high-resolution HRp-QCT. Conclusions: The obtained results indicate the promise of the proposed predictive technique for the estimation of trabecular parameters in children from multisequence MRI, thus reducing the need for high-resolution radiation-based scans for a fragile population that is under development and growth.« less

Extracting intrinsic functional networks with feature-based group independent component analysis.

PubMed

Calhoun, Vince D; Allen, Elena

2013-04-01

There is increasing use of functional imaging data to understand the macro-connectome of the human brain. Of particular interest is the structure and function of intrinsic networks (regions exhibiting temporally coherent activity both at rest and while a task is being performed), which account for a significant portion of the variance in functional MRI data. While networks are typically estimated based on the temporal similarity between regions (based on temporal correlation, clustering methods, or independent component analysis [ICA]), some recent work has suggested that these intrinsic networks can be extracted from the inter-subject covariation among highly distilled features, such as amplitude maps reflecting regions modulated by a task or even coordinates extracted from large meta analytic studies. In this paper our goal was to explicitly compare the networks obtained from a first-level ICA (ICA on the spatio-temporal functional magnetic resonance imaging (fMRI) data) to those from a second-level ICA (i.e., ICA on computed features rather than on the first-level fMRI data). Convergent results from simulations, task-fMRI data, and rest-fMRI data show that the second-level analysis is slightly noisier than the first-level analysis but yields strikingly similar patterns of intrinsic networks (spatial correlations as high as 0.85 for task data and 0.65 for rest data, well above the empirical null) and also preserves the relationship of these networks with other variables such as age (for example, default mode network regions tended to show decreased low frequency power for first-level analyses and decreased loading parameters for second-level analyses). In addition, the best-estimated second-level results are those which are the most strongly reflected in the input feature. In summary, the use of feature-based ICA appears to be a valid tool for extracting intrinsic networks. We believe it will become a useful and important approach in the study of the macro-connectome, particularly in the context of data fusion.

Multiclass feature selection for improved pediatric brain tumor segmentation

NASA Astrophysics Data System (ADS)

Ahmed, Shaheen; Iftekharuddin, Khan M.

2012-03-01

In our previous work, we showed that fractal-based texture features are effective in detection, segmentation and classification of posterior-fossa (PF) pediatric brain tumor in multimodality MRI. We exploited an information theoretic approach such as Kullback-Leibler Divergence (KLD) for feature selection and ranking different texture features. We further incorporated the feature selection technique with segmentation method such as Expectation Maximization (EM) for segmentation of tumor T and non tumor (NT) tissues. In this work, we extend the two class KLD technique to multiclass for effectively selecting the best features for brain tumor (T), cyst (C) and non tumor (NT). We further obtain segmentation robustness for each tissue types by computing Bay's posterior probabilities and corresponding number of pixels for each tissue segments in MRI patient images. We evaluate improved tumor segmentation robustness using different similarity metric for 5 patients in T1, T2 and FLAIR modalities.

Relationship of compartment-specific structural knee status at baseline with change in cartilage morphology: a prospective observational study using data from the osteoarthritis initiative

PubMed Central

Eckstein, Felix; Wirth, Wolfgang; Hudelmaier, Martin I; Maschek, Susanne; Hitzl, Wolfgang; Wyman, Bradley T; Nevitt, Michael; Hellio Le Graverand, Marie-Pierre; Hunter, David

2009-01-01

Introduction The aim was to investigate the relationship of cartilage loss (change in medial femorotibial cartilage thickness measured with magnetic resonance imaging (MRI)) with compartment-specific baseline radiographic findings and MRI cartilage morphometry features, and to identify which baseline features can be used for stratification of fast progressors. Methods An age and gender stratified subsample of the osteoarthritis (OA) initiative progression subcohort (79 women; 77 men; age 60.9 ± 9.9 years; body mass index (BMI) 30.3 ± 4.7) with symptomatic, radiographic OA in at least one knee was studied. Baseline fixed flexion radiographs were read centrally and adjudicated, and cartilage morphometry was performed at baseline and at one year follow-up from coronal FLASH 3 Tesla MR images of the right knee. Results Osteophyte status at baseline was not associated with medial cartilage loss. Knees with medial joint space narrowing tended to show higher rates of change than those without, but the relationship was not statistically significant. Knees with medial femoral subchondral bone sclerosis (radiography), medial denuded subchondral bone areas (MRI), and low cartilage thickness (MRI) at baseline displayed significantly higher cartilage loss than those without, both with and without adjusting for age, sex, and BMI. Participants with denuded subchondral bone showed a standardized response mean of up to -0.64 versus -0.33 for the entire subcohort. Conclusions The results indicate that radiographic and MRI cartilage morphometry features suggestive of advanced disease appear to be associated with greater cartilage loss. These features may be suited for selecting patients with a higher likelihood of fast progression in studies that attempt to demonstrate the cartilage-preserving effect of disease-modifying osteoarthritis drugs. PMID:19534783

Agreement among Magnetic Resonance Imaging/Magnetic Resonance Cholangiopancreatography (MRI-MRCP) and Endoscopic Ultrasound (EUS) in the evaluation of morphological features of Branch Duct Intraductal Papillary Mucinous Neoplasm (BD-IPMN).

PubMed

Uribarri-Gonzalez, Laura; Keane, Margaret G; Pereira, Stephen P; Iglesias-García, Julio; Dominguez-Muñoz, J Enrique; Lariño-Noia, Jose

2018-03-01

To evaluate the agreement between the imaging modalities MRI-MRCP and EUS in cystic lesions of the pancreas which were thought to be a BD-IPMN. Multicenter retrospective study included all patients between 2010 and 2015 with a suspected BD-IPMN who underwent an EUS and MRI-MRCP within 6 months or less of each other. Location, number, size, worrisome features and high-risk stigmata were evaluated. Interobserver agreement was evaluated by Kappa score. 173 patients were included (97 UHSC, 76 UCLH-RFH), mean age 65 (range 25-87 years), 66 males. When comparing both modalities there was good agreement for the location of the cyst. The median lesion size was larger by MRI-MRCP than EUS although it was not significant. With regards to worrisome features, there was moderate agreement for main PD of 5-9 mm and abrupt change (k = 0.45 and 0.52). Fair agreement was seen for the cyst wall thickening (k = 0.25). No agreement was seen between the presence of non-enhanced mural nodules or lymphadenopathy (k < 0). With regards to high-risk stigmata, poor agreement was obtained for the detection of an enhanced solid component (k = 0.12). No agreement was observed for main PD > 10 mm (k < 0). In this multicentre study of patients with a BD-IPMN under active surveillance, most disagreement between these modalities was seen in the proximal pancreas. There was generally only minimal concordance between the imaging findings of EUS and MRI-MRCP for the detection of high-risk stigmata and worrisome features. Copyright © 2018 IAP and EPC. All rights reserved.

Association of BRCA Mutation Types, Imaging Features, and Pathologic Findings in Patients With Breast Cancer With BRCA1 and BRCA2 Mutations.

PubMed

Ha, Su Min; Chae, Eun Young; Cha, Joo Hee; Kim, Hak Hee; Shin, Hee Jung; Choi, Woo Jung

2017-10-01

The purpose of this study is to retrospectively evaluate the relationships between the BRCA mutation types, imaging features, and pathologic findings of breast cancers in BRCA1 and BRCA2 mutation carriers. We identified patients with breast cancer with BRCA gene mutations from January 2000 to December 2014. After excluding patients who underwent lesion excision before MRI, 99 BRCA1 and 103 BRCA2 lesions in 187 women (mean age, 39.7 and 40.4 years, respectively) were enrolled. Mammographic, sonographic, and MRI scans were reviewed according to the BI-RADS lexicon (5th edition). Pathologic data were reviewed, including the immunohistochemistry findings. The relationships between the BRCA mutations and both imaging and pathologic findings were analyzed. The distribution of molecular subtypes of tumors significantly differed by the mutation type. BRCA1 tumors were associated with the triple-negative subtype, whereas BRCA2 tumors were associated with the luminal B subtype (p = 0.002). At MRI, breast cancers with BRCA1 mutations exhibited a circumscribed margin (p = 0.032) and rim enhancement (p = 0.013). No significant differences in mass shape or kinetic features were observed at MRI. Cancers in BRCA1 mutation carriers tended to develop in the posterior location in the breast (p = 0.034). At mammography, no significant difference in the prevalence of calcifications was observed according to the mutation type. At sonography, BRCA1 lesions were found to be associated with posterior acoustic enhancement (p < 0.0001). Breast cancers with BRCA1 mutations tend to exhibit benign morphologic features at MRI, mammography, and sonography, compared with BRCA2 mutations. Lesion location may represent another difference on imaging among various genetic phenotypes.

Ensemble support vector machine classification of dementia using structural MRI and mini-mental state examination.

PubMed

Sørensen, Lauge; Nielsen, Mads

2018-05-15

The International Challenge for Automated Prediction of MCI from MRI data offered independent, standardized comparison of machine learning algorithms for multi-class classification of normal control (NC), mild cognitive impairment (MCI), converting MCI (cMCI), and Alzheimer's disease (AD) using brain imaging and general cognition. We proposed to use an ensemble of support vector machines (SVMs) that combined bagging without replacement and feature selection. SVM is the most commonly used algorithm in multivariate classification of dementia, and it was therefore valuable to evaluate the potential benefit of ensembling this type of classifier. The ensemble SVM, using either a linear or a radial basis function (RBF) kernel, achieved multi-class classification accuracies of 55.6% and 55.0% in the challenge test set (60 NC, 60 MCI, 60 cMCI, 60 AD), resulting in a third place in the challenge. Similar feature subset sizes were obtained for both kernels, and the most frequently selected MRI features were the volumes of the two hippocampal subregions left presubiculum and right subiculum. Post-challenge analysis revealed that enforcing a minimum number of selected features and increasing the number of ensemble classifiers improved classification accuracy up to 59.1%. The ensemble SVM outperformed single SVM classifications consistently in the challenge test set. Ensemble methods using bagging and feature selection can improve the performance of the commonly applied SVM classifier in dementia classification. This resulted in competitive classification accuracies in the International Challenge for Automated Prediction of MCI from MRI data. Copyright © 2018 Elsevier B.V. All rights reserved.

Estimation of cardiac motion in cine-MRI sequences by correlation transform optical flow of monogenic features distance

NASA Astrophysics Data System (ADS)

Gao, Bin; Liu, Wanyu; Wang, Liang; Liu, Zhengjun; Croisille, Pierre; Delachartre, Philippe; Clarysse, Patrick

2016-12-01

Cine-MRI is widely used for the analysis of cardiac function in clinical routine, because of its high soft tissue contrast and relatively short acquisition time in comparison with other cardiac MRI techniques. The gray level distribution in cardiac cine-MRI is relatively homogenous within the myocardium, and can therefore make motion quantification difficult. To ensure that the motion estimation problem is well posed, more image features have to be considered. This work is inspired by a method previously developed for color image processing. The monogenic signal provides a framework to estimate the local phase, orientation, and amplitude, of an image, three features which locally characterize the 2D intensity profile. The independent monogenic features are combined into a 3D matrix for motion estimation. To improve motion estimation accuracy, we chose the zero-mean normalized cross-correlation as a matching measure, and implemented a bilateral filter for denoising and edge-preservation. The monogenic features distance is used in lieu of the color space distance in the bilateral filter. Results obtained from four realistic simulated sequences outperformed two other state of the art methods even in the presence of noise. The motion estimation errors (end point error) using our proposed method were reduced by about 20% in comparison with those obtained by the other tested methods. The new methodology was evaluated on four clinical sequences from patients presenting with cardiac motion dysfunctions and one healthy volunteer. The derived strain fields were analyzed favorably in their ability to identify myocardial regions with impaired motion.

A Novel Feature-Map Based ICA Model for Identifying the Individual, Intra/Inter-Group Brain Networks across Multiple fMRI Datasets.

PubMed

Wang, Nizhuan; Chang, Chunqi; Zeng, Weiming; Shi, Yuhu; Yan, Hongjie

2017-01-01

Independent component analysis (ICA) has been widely used in functional magnetic resonance imaging (fMRI) data analysis to evaluate functional connectivity of the brain; however, there are still some limitations on ICA simultaneously handling neuroimaging datasets with diverse acquisition parameters, e.g., different repetition time, different scanner, etc. Therefore, it is difficult for the traditional ICA framework to effectively handle ever-increasingly big neuroimaging datasets. In this research, a novel feature-map based ICA framework (FMICA) was proposed to address the aforementioned deficiencies, which aimed at exploring brain functional networks (BFNs) at different scales, e.g., the first level (individual subject level), second level (intragroup level of subjects within a certain dataset) and third level (intergroup level of subjects across different datasets), based only on the feature maps extracted from the fMRI datasets. The FMICA was presented as a hierarchical framework, which effectively made ICA and constrained ICA as a whole to identify the BFNs from the feature maps. The simulated and real experimental results demonstrated that FMICA had the excellent ability to identify the intergroup BFNs and to characterize subject-specific and group-specific difference of BFNs from the independent component feature maps, which sharply reduced the size of fMRI datasets. Compared with traditional ICAs, FMICA as a more generalized framework could efficiently and simultaneously identify the variant BFNs at the subject-specific, intragroup, intragroup-specific and intergroup levels, implying that FMICA was able to handle big neuroimaging datasets in neuroscience research.

[Different aspects of magnetic resonance imaging of muscles between dermatomyositis and polymyositis].

PubMed

Miranda, Sofia Silveira de Castro; Alvarenga, Daniel; Rodrigues, João Carlos; Shinjo, Samuel Katsuyuki

2014-01-01

Although dermatomyositis (DM) and polymyositis (PM) share many clinical features in common, they have distinct pathophysiological and histological features. It is possible that these distinctions reflect also macroscopically, for example, in muscle alterations seen in magnetic resonance images (MRI). To compare simultaneously the MRI of various muscle compartments of the thighs of adult DM and PM. The present study is a cross-sectional that included, between 2010 and 2013, 11 newly diagnosed DM and 11 PM patients (Bohan and Peter's criteria, 1975), with clinical and laboratory activity. They were valued at RM thighs, T1 and T2 with fat suppression, 1.5 T MRI scanner sequences. The mean age at the time of MRI, the time between onset of symptoms and the realization of the MRI distribution of sex and drug therapy were comparable between the two groups (p>0.050). Concerning the MRI, muscle edema was significantly found in DM, and mainly in the proximal region of the muscles. The area of fat replacement was found predominantly in PM. The partial fat replacement area occurred mainly in the medial and distal region, whereas the total fat replacement area occurred mainly in the distal muscles. There was no area of muscle fibrosis. DM and PM have different characteristics on MRI muscles, alike pathophysiological and histological distinctions. Copyright © 2014 Elsevier Editora Ltda. All rights reserved.

Early classification of Alzheimer's disease using hippocampal texture from structural MRI

NASA Astrophysics Data System (ADS)

Zhao, Kun; Ding, Yanhui; Wang, Pan; Dou, Xuejiao; Zhou, Bo; Yao, Hongxiang; An, Ningyu; Zhang, Yongxin; Zhang, Xi; Liu, Yong

2017-03-01

Convergent evidence has been collected to support that Alzheimer's disease (AD) is associated with reduction in hippocampal volume based on anatomical magnetic resonance imaging (MRI) and impaired functional connectivity based on functional MRI. Radiomics texture analysis has been previously successfully used to identify MRI biomarkers of several diseases, including AD, mild cognitive impairment and multiple sclerosis. In this study, our goal was to determine if MRI hippocampal textures, including the intensity, shape, texture and wavelet features, could be served as an MRI biomarker of AD. For this purpose, the texture marker was trained and evaluated from MRI data of 48 AD and 39 normal samples. The result highlights the presence of hippocampal texture abnormalities in AD, and the possibility that texture may serve as a neuroimaging biomarker for AD.

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.

Relationship between preoperative breast MRI and surgical treatment of non-metastatic breast cancer.

PubMed

Onega, Tracy; Weiss, Julie E; Goodrich, Martha E; Zhu, Weiwei; DeMartini, Wendy B; Kerlikowske, Karla; Ozanne, Elissa; Tosteson, Anna N A; Henderson, Louise M; Buist, Diana S M; Wernli, Karen J; Herschorn, Sally D; Hotaling, Elise; O'Donoghue, Cristina; Hubbard, Rebecca

2017-12-01

More extensive surgical treatments for early stage breast cancer are increasing. The patterns of preoperative MRI overall and by stage for this trend has not been well established. Using Breast Cancer Surveillance Consortium registry data from 2010 through 2014, we identified women with an incident non-metastatic breast cancer and determined use of preoperative MRI and initial surgical treatment (mastectomy, with or without contralateral prophylactic mastectomy (CPM), reconstruction, and breast conserving surgery ± radiation). Clinical and sociodemographic covariates were included in multivariable logistic regression models to estimate adjusted odds ratios and 95% confidence intervals. Of the 13 097 women, 2217 (16.9%) had a preoperative MRI. Among the women with MRI, results indicated 32% higher odds of unilateral mastectomy compared to breast conserving surgery and of mastectomy with CPM compared to unilateral mastectomy. Women with preoperative MRI also had 56% higher odds of reconstruction. Preoperative MRI in women with DCIS and early stage invasive breast cancer is associated with more frequent mastectomy, CPM, and reconstruction surgical treatment. Use of more extensive surgical treatment and reconstruction among women with DCIS and early stage invasive cancer whom undergo MRI warrants further investigation. © 2017 Wiley Periodicals, Inc.

A test-retest dataset for assessing long-term reliability of brain morphology and resting-state brain activity.

PubMed

Huang, Lijie; Huang, Taicheng; Zhen, Zonglei; Liu, Jia

2016-03-15

We present a test-retest dataset for evaluation of long-term reliability of measures from structural and resting-state functional magnetic resonance imaging (sMRI and rfMRI) scans. The repeated scan dataset was collected from 61 healthy adults in two sessions using highly similar imaging parameters at an interval of 103-189 days. However, as the imaging parameters were not completely identical, the reliability estimated from this dataset shall reflect the lower bounds of the true reliability of sMRI/rfMRI measures. Furthermore, in conjunction with other test-retest datasets, our dataset may help explore the impact of different imaging parameters on reliability of sMRI/rfMRI measures, which is especially critical for assessing datasets collected from multiple centers. In addition, intelligence quotient (IQ) was measured for each participant using Raven's Advanced Progressive Matrices. The data can thus be used for purposes other than assessing reliability of sMRI/rfMRI alone. For example, data from each single session could be used to associate structural and functional measures of the brain with the IQ metrics to explore brain-IQ association.

Limitations of ultrasonography for diagnosing white matter damage in preterm infants.

PubMed

Debillon, T; N'Guyen, S; Muet, A; Quere, M P; Moussaly, F; Roze, J C

2003-07-01

To compare the accuracy of ultrasonography (US) and magnetic resonance imaging (MRI) in diagnosing white matter abnormalities in preterm infants and to determine the specific indications for MRI. Prospective cohort study. A neonatal intensive care unit in France. All preterm infants (

Limitations of ultrasonography for diagnosing white matter damage in preterm infants

PubMed Central

Debillon, T; N'Guyen, S; Muet, A; Quere, M; Moussaly, F; Roze, J

2003-01-01

Objectives: To compare the accuracy of ultrasonography (US) and magnetic resonance imaging (MRI) in diagnosing white matter abnormalities in preterm infants and to determine the specific indications for MRI. Design: Prospective cohort study. Setting: A neonatal intensive care unit in France. Patients: All preterm infants (≤ 33 weeks gestation) without severe respiratory distress syndrome precluding MRI. Main outcome measures: US and MRI performed contemporaneously during the third postnatal week were analysed by an independent observer. The findings were compared with those of a term MRI scan, the results of which were taken as the final diagnosis. Statistical analysis was performed to determine which early imaging study best predicted the term MRI findings. Results: The early US and MRI findings (79 infants) correlated closely for severe lesions (cystic periventricular leucomalacia and parenchymal infarction; κ coefficient = 0.86) but not for moderate lesions (non-cystic leucomalacia and parenchymal punctate haemorrhages; κ = 0.62). Overall, early MRI findings predicted late MRI findings in 98% of patients (95% confidence interval (CI) 89.5 to 99.9) compared with only 68% for early US (95% CI 52.1 to 79.2). Conclusions: US is highly effective in detecting severe lesions of the white matter in preterm infants, but MRI seems to be necessary for the diagnosis of less severe damage. MRI performed at about the third week of life is highly predictive of the final diagnosis at term. PMID:12819157

An ICA-based method for the identification of optimal FMRI features and components using combined group-discriminative techniques

PubMed Central

Sui, Jing; Adali, Tülay; Pearlson, Godfrey D.; Calhoun, Vince D.

2013-01-01

Extraction of relevant features from multitask functional MRI (fMRI) data in order to identify potential biomarkers for disease, is an attractive goal. In this paper, we introduce a novel feature-based framework, which is sensitive and accurate in detecting group differences (e.g. controls vs. patients) by proposing three key ideas. First, we integrate two goal-directed techniques: coefficient-constrained independent component analysis (CC-ICA) and principal component analysis with reference (PCA-R), both of which improve sensitivity to group differences. Secondly, an automated artifact-removal method is developed for selecting components of interest derived from CC-ICA, with an average accuracy of 91%. Finally, we propose a strategy for optimal feature/component selection, aiming to identify optimal group-discriminative brain networks as well as the tasks within which these circuits are engaged. The group-discriminating performance is evaluated on 15 fMRI feature combinations (5 single features and 10 joint features) collected from 28 healthy control subjects and 25 schizophrenia patients. Results show that a feature from a sensorimotor task and a joint feature from a Sternberg working memory (probe) task and an auditory oddball (target) task are the top two feature combinations distinguishing groups. We identified three optimal features that best separate patients from controls, including brain networks consisting of temporal lobe, default mode and occipital lobe circuits, which when grouped together provide improved capability in classifying group membership. The proposed framework provides a general approach for selecting optimal brain networks which may serve as potential biomarkers of several brain diseases and thus has wide applicability in the neuroimaging research community. PMID:19457398

Characterization of the Growth of Deep and Subcortical White Matter Hyperintensity on MR Imaging: A Retrospective Cohort Study.

PubMed

Adachi, Michito; Sato, Takamichi

2017-07-10

In elderly patients, deep and subcortical white matter hyperintense lesions are frequently observed on MRI; however, the growth process of these lesions is unclear. The aims of this retrospective cohort study were to elucidate the growth characteristics of deep and subcortical white matter hyperintense lesions, and to insight their etiology. We enrolled 103 patients (1610 lesions) whose deep and subcortical white matter hyperintense lesions were monitored for 3 or more years by MRI examination. The area of each hyperintense lesion was measured using a tracing method in the first and last MRI examinations. The annual rate of increase in the area of each lesion was calculated, and using the Pearson product-moment correlation coefficient the correlation between the annual rate of increase in area and the interval between the first and last MRI examinations was determined. The paired t-test showed a significant increase in the mean area of all the deep and subcortical white matter hyperintense lesions between the first and last MRI examinations (P < 0.001). However, hyperintense lesions had decreased in the area or disappeared in 227 (14.1%) lesions in the last MRI examination, particularly in patients with diabetes. The mean annual rate of increase in area of all hyperintense lesions was 0.013 ± 0.021 cm 2 per year. The annual rate of increase in area and the interval between the first and last MRI examinations showed a weak negative correlation (r = -0.121; P < 0.01). Decrease in the area and the disappearance of the subcortical white matter hyperintense lesions, and a decline in the annual rate of increase in the lesion area with time suggest that the interstitial fluid accumulation associated with dysfunctional drainage around the vessels may be involved in the possible etiologies of deep and subcortical white matter hyperintense lesions.

Safety and utility of magnetic resonance imaging in patients with cardiac implantable electronic devices.

PubMed

Strom, Jordan B; Whelan, Jill B; Shen, Changyu; Zheng, Shuang Qi; Mortele, Koenraad J; Kramer, Daniel B

2017-08-01

Off-label magnetic resonance imaging (MRI) for patients with cardiac implantable electrical devices has been limited owing to concerns about safety and unclear diagnostic and prognostic utility. The purpose of this study was to define major and minor adverse events with off-label MRI scans. We prospectively evaluated patients with non-MRI-conditional cardiac implantable electrical devices referred for MRI scans under a strict clinical protocol. The primary safety outcome was incidence of major adverse events (loss of pacing, inappropriate shock or antitachycardia pacing, need for system revision, or death) or minor adverse events (inappropriate pacing, arrhythmias, power-on-reset events, heating at the generator site, or changes in device parameters at baseline or at 6 months). A total of 189 MRI scans were performed in 123 patients (63.1% [78] men; median age 70 ± 18.5 years; 56.9% [70] patients with implantable cardioverter-defibrillators; 33.3% [41] pacemaker-dependent patients) predominantly for brain or spinal conditions. A minority of scans (22.7% [43]) were performed for urgent or emergent indications. Major adverse events were rare: 1 patient with loss of pacing, no deaths, or system revisions (overall rate 0.5%; 95% confidence interval 0.01-2.91). Minor adverse events were similarly rare (overall rate 1.6%; 95% confidence interval 0.3-4.6). Nearly all studies (98.4% [186]) were interpretable, while 75.1% [142] were determined to change management according to the prespecified criteria. No clinically significant changes were observed in device parameters acutely after MRI or at 6 months as compared with baseline across all patient and device categories. Off-label MRI scans performed under a strict protocol demonstrated excellent short- and medium-term safety while providing interpretable imaging that frequently influenced clinical care. Copyright © 2017 Heart Rhythm Society. Published by Elsevier Inc. All rights reserved.

Image Quality of Cardiac Magnetic Resonance Imaging in Patients With an Implantable Cardioverter Defibrillator System Designed for the Magnetic Resonance Imaging Environment.

PubMed

Schwitter, Juerg; Gold, Michael R; Al Fagih, Ahmed; Lee, Sung; Peterson, Michael; Ciuffo, Allen; Zhang, Yan; Kristiansen, Nina; Kanal, Emanuel; Sommer, Torsten

2016-05-01

Recently, magnetic resonance (MR)-conditional implantable cardioverter defibrillator (ICD) systems have become available. However, associated cardiac MR image (MRI) quality is unknown. The goal was to evaluate the image quality performance of various cardiac MR sequences in a multicenter trial of patients implanted with an MR-conditional ICD system. The Evera-MRI trial enrolled 275 patients in 42 centers worldwide. There were 263 patients implanted with an Evera-MRI single- or dual-chamber ICD and randomized to controls (n=88) and MRI (n=175), 156 of whom underwent a protocol-required MRI (9-12 weeks post implant). Steady-state-free-precession (SSFP) and fast-gradient-echo (FGE) sequences were acquired in short-axis and horizontal long-axis orientations. Qualitative and quantitative assessment of image quality was performed by using a 7-point scale (grades 1-3: good quality, grades 6-7: nondiagnostic) and measuring ICD- and lead-related artifact size. Good to moderate image quality (grades 1-5) was obtained in 53% and 74% of SSFP and FGE acquisitions, respectively, covering the left ventricle, and in 69% and 84%, respectively, covering the right ventricle. Odds for better image quality were greater for right ventricle versus left ventricle (odds ratio, 1.8; 95% confidence interval, 1.5-2.2; P<0.0001) and greater for FGE versus SSFP (odds ratio, 3.5; 95% confidence interval, 2.5-4.8; P<0.0001). Compared with SSFP, ICD-related artifacts on FGE were smaller (141±65 versus 75±57 mm, respectively; P<0.0001). Lead artifacts were much smaller than ICD artifacts (P<0.0001). FGE yields good to moderate quality in 74% of left ventricle and 84% of right ventricle acquisitions and performs better than SSFP in patients with an MRI-conditional ICD system. In these patients, cardiac MRI can offer diagnostic information in most cases. URL: http://www.clinicaltrials.gov. Unique identifier: NCT02117414. © 2016 American Heart Association, Inc.

Safety and utility of magnetic resonance imaging in patients with cardiac implantable electronic devices

PubMed Central

Strom, Jordan B.; Whelan, Jill B.; Shen, Changyu; Zheng, Shuang Qi; Mortele, Koenraad J.; Kramer, Daniel B.

2017-01-01

BACKGROUND Off-label magnetic resonance imaging (MRI) for patients with cardiac implantable electrical devices has been limited owing to concerns about safety and unclear diagnostic and prognostic utility. OBJECTIVE The purpose of this study was to define major and minor adverse events with off-label MRI scans. METHODS We prospectively evaluated patients with non–MRI-conditional cardiac implantable electrical devices referred for MRI scans under a strict clinical protocol. The primary safety outcome was incidence of major adverse events (loss of pacing, inappropriate shock or antitachycardia pacing, need for system revision, or death) or minor adverse events (inappropriate pacing, arrhythmias, power-on-reset events, heating at the generator site, or changes in device parameters at baseline or at 6 months). RESULTS A total of 189 MRI scans were performed in 123 patients (63.1% [78] men; median age 70 ± 18.5 years; 37.0% [70] patients with implantable cardioverter-defibrillators; 21.8% [41] pacemaker-dependent patients) predominantly for brain or spinal conditions. A minority of scans (22.7% [43]) were performed for urgent or emergent indications. Major adverse events were rare: 1 patient with loss of pacing, no deaths, or system revisions (overall rate 0.5%; 95% confidence interval 0.01–2.91). Minor adverse events were similarly rare (overall rate 1.6%; 95% confidence interval 0.3–4.6). Nearly all studies (98.4% [186]) were interpretable, while 74.9% [142] were determined to change management according to the prespecified criteria. No clinically significant changes were observed in device parameters acutely after MRI or at 6 months as compared with baseline across all patient and device categories. CONCLUSION Off-label MRI scans performed under a strict protocol demonstrated excellent short- and medium-term safety while providing interpretable imaging that frequently influenced clinical care. PMID:28385671

Interobserver variability in identification of breast tumors in MRI and its implications for prognostic biomarkers and radiogenomics

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

Saha, Ashirbani, E-mail: as698@duke.edu; Grimm, La

Purpose: To assess the interobserver variability of readers when outlining breast tumors in MRI, study the reasons behind the variability, and quantify the effect of the variability on algorithmic imaging features extracted from breast MRI. Methods: Four readers annotated breast tumors from the MRI examinations of 50 patients from one institution using a bounding box to indicate a tumor. All of the annotated tumors were biopsy proven cancers. The similarity of bounding boxes was analyzed using Dice coefficients. An automatic tumor segmentation algorithm was used to segment tumors from the readers’ annotations. The segmented tumors were then compared between readersmore » using Dice coefficients as the similarity metric. Cases showing high interobserver variability (average Dice coefficient <0.8) after segmentation were analyzed by a panel of radiologists to identify the reasons causing the low level of agreement. Furthermore, an imaging feature, quantifying tumor and breast tissue enhancement dynamics, was extracted from each segmented tumor for a patient. Pearson’s correlation coefficients were computed between the features for each pair of readers to assess the effect of the annotation on the feature values. Finally, the authors quantified the extent of variation in feature values caused by each of the individual reasons for low agreement. Results: The average agreement between readers in terms of the overlap (Dice coefficient) of the bounding box was 0.60. Automatic segmentation of tumor improved the average Dice coefficient for 92% of the cases to the average value of 0.77. The mean agreement between readers expressed by the correlation coefficient for the imaging feature was 0.96. Conclusions: There is a moderate variability between readers when identifying the rectangular outline of breast tumors on MRI. This variability is alleviated by the automatic segmentation of the tumors. Furthermore, the moderate interobserver variability in terms of the bounding box does not translate into a considerable variability in terms of assessment of enhancement dynamics. The authors propose some additional ways to further reduce the interobserver variability.« less

The potential of multiparametric MRI of the breast

PubMed Central

Pinker, Katja; Helbich, Thomas H

2017-01-01

MRI is an essential tool in breast imaging, with multiple established indications. Dynamic contrast-enhanced MRI (DCE-MRI) is the backbone of any breast MRI protocol and has an excellent sensitivity and good specificity for breast cancer diagnosis. DCE-MRI provides high-resolution morphological information, as well as some functional information about neoangiogenesis as a tumour-specific feature. To overcome limitations in specificity, several other functional MRI parameters have been investigated and the application of these combined parameters is defined as multiparametric MRI (mpMRI) of the breast. MpMRI of the breast can be performed at different field strengths (1.5–7 T) and includes both established (diffusion-weighted imaging, MR spectroscopic imaging) and novel MRI parameters (sodium imaging, chemical exchange saturation transfer imaging, blood oxygen level-dependent MRI), as well as hybrid imaging with positron emission tomography (PET)/MRI and different radiotracers. Available data suggest that multiparametric imaging using different functional MRI and PET parameters can provide detailed information about the underlying oncogenic processes of cancer development and progression and can provide additional specificity. This article will review the current and emerging functional parameters for mpMRI of the breast for improved diagnostic accuracy in breast cancer. PMID:27805423

SU-F-R-42: Association of Radiomic and Metabolic Tumor Volumes in Radiation Treatment of Glioblastoma Multiforme

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

Lopez, C; Nagornaya, N; Parra, N

Purpose: High-throughput extraction of imaging and metabolomic quantitative features from MRI and MR Spectroscopy Imaging (MRSI) of Glioblastoma Multiforme (GBM) results in tens of variables per patient. In radiotherapy (RT) of GBM, the relevant metabolic tumor volumes (MTVs) are related to aberrant levels of N-acetyl Aspartate (NAA) and Choline (Cho). Corresponding Clinical Target Volumes (CTVs) for RT planning are based on Contrast Enhancing T1-weighted MRI (CE-T1w) and T2-weighted/Fluid Attenuated Inversion Recovery (FLAIR) MRI. The objective is to build a framework for investigation of associations between imaging, CTV, and MTV features better understanding of the underlying information in the CTVs andmore » dependencies between these volumes. Methods: Necrotic portions, enhancing lesion and edema were manually contoured on T1w/T2w images for 17 GBM patients. CTVs and MTVs for NAA (MTV{sub NAA}) and Cho (MTV{sub Cho}) were constructed. Tumors were scored categorically for ten semantic imaging traits by neuroradiologist. All features were investigated for redundancy. Two-way correlations between imaging and RT/MTV features were visualized as heat maps. Associations between MTV{sub NAA}, MTV{sub Cho} and imaging features were studied using Spearman correlation. Results: 39 imaging features were computed per patient. Half of the imaging traits were replaced with automatically extracted continuous variables. 21 features were extracted from MTVs/CTVs. There were a high number (43) of significant correlations of imaging with CTVs/MTV{sub NAA} while very few (10) significant correlations were with CTVs/MTV{sub Cho}. MTV{sub NAA} was found to be closely associated with MRI volumes, MTV{sub Cho} remains elusive for characterization with imaging. Conclusion: A framework for investigation of co-dependency between MRI and RT/metabolic features is established. A series of semantic imaging traits were replaced with automatically extracted continuous variables. The approach will allow for exploration of relationships between sizes and intersection of imaging features of tumors, RT volumes, metabolite concentrations and comparing those to therapy outcome, quality of life evaluation and overall survival rate. This publication was supported by Grant 10BN03 from Bankhead Coley Cancer Research Program, R01EB000822, R01EB016064, and R01CA172210 from the National Institutes of Health, and Indo-US Science & Technology Forum award #20-2009. Bhaswati Roy received financial assistance from University Grant Commission, New Delhi, India.« less

A semi-supervised Support Vector Machine model for predicting the language outcomes following cochlear implantation based on pre-implant brain fMRI imaging.

PubMed

Tan, Lirong; Holland, Scott K; Deshpande, Aniruddha K; Chen, Ye; Choo, Daniel I; Lu, Long J

2015-12-01

We developed a machine learning model to predict whether or not a cochlear implant (CI) candidate will develop effective language skills within 2 years after the CI surgery by using the pre-implant brain fMRI data from the candidate. The language performance was measured 2 years after the CI surgery by the Clinical Evaluation of Language Fundamentals-Preschool, Second Edition (CELF-P2). Based on the CELF-P2 scores, the CI recipients were designated as either effective or ineffective CI users. For feature extraction from the fMRI data, we constructed contrast maps using the general linear model, and then utilized the Bag-of-Words (BoW) approach that we previously published to convert the contrast maps into feature vectors. We trained both supervised models and semi-supervised models to classify CI users as effective or ineffective. Compared with the conventional feature extraction approach, which used each single voxel as a feature, our BoW approach gave rise to much better performance for the classification of effective versus ineffective CI users. The semi-supervised model with the feature set extracted by the BoW approach from the contrast of speech versus silence achieved a leave-one-out cross-validation AUC as high as 0.97. Recursive feature elimination unexpectedly revealed that two features were sufficient to provide highly accurate classification of effective versus ineffective CI users based on our current dataset. We have validated the hypothesis that pre-implant cortical activation patterns revealed by fMRI during infancy correlate with language performance 2 years after cochlear implantation. The two brain regions highlighted by our classifier are potential biomarkers for the prediction of CI outcomes. Our study also demonstrated the superiority of the semi-supervised model over the supervised model. It is always worthwhile to try a semi-supervised model when unlabeled data are available.

Muscle MRI in female carriers of dystrophinopathy.

PubMed

Tasca, G; Monforte, M; Iannaccone, E; Laschena, F; Ottaviani, P; Silvestri, G; Masciullo, M; Mirabella, M; Servidei, S; Ricci, E

2012-09-01

Duchenne muscular dystrophy carriers represent a rare condition that needs to be recognized because of the possible implications for prenatal diagnosis. Muscle biopsy is currently the diagnostic instrument of choice in sporadic patients. We wanted to verify whether muscle magnetic resonance imaging (MRI) could identify a pattern of involvement suggestive of this condition and whether it was similar to that reported in Duchenne and Becker muscular dystrophy. Evaluation of pelvic and lower limb MRI scans of 12 dystrophinopathy carriers was performed. We found a frequent involvement of the quadratus femoris, gluteus maximus and medius, biceps femoris long head, adductor magnus, vasti and paraspinal muscles, whilst the popliteus, iliopsoas, recti abdominis, sartorius, and gracilis were relatively spared. Asymmetry was a major feature on MRI; it could be detected significantly more often than with sole clinical examination and even in patients without weakness. The pattern we describe here is similar to that reported in Duchenne and Becker muscular dystrophy, although asymmetry represents a major distinctive feature. Muscle MRI was more sensitive than clinical examination for detecting single muscle involvement and asymmetry. Further studies are needed to verify the consistency of this pattern in larger cohorts and to assess whether muscle MRI can improve diagnostic accuracy in carriers with normal dystrophin staining on muscle biopsy. © 2012 The Author(s) European Journal of Neurology © 2012 EFNS.

Top-down modulation from inferior frontal junction to FEFs and intraparietal sulcus during short-term memory for visual features.

PubMed

Sneve, Markus H; Magnussen, Svein; Alnæs, Dag; Endestad, Tor; D'Esposito, Mark

2013-11-01

Visual STM of simple features is achieved through interactions between retinotopic visual cortex and a set of frontal and parietal regions. In the present fMRI study, we investigated effective connectivity between central nodes in this network during the different task epochs of a modified delayed orientation discrimination task. Our univariate analyses demonstrate that the inferior frontal junction (IFJ) is preferentially involved in memory encoding, whereas activity in the putative FEFs and anterior intraparietal sulcus (aIPS) remains elevated throughout periods of memory maintenance. We have earlier reported, using the same task, that areas in visual cortex sustain information about task-relevant stimulus properties during delay intervals [Sneve, M. H., Alnæs, D., Endestad, T., Greenlee, M. W., & Magnussen, S. Visual short-term memory: Activity supporting encoding and maintenance in retinotopic visual cortex. Neuroimage, 63, 166-178, 2012]. To elucidate the temporal dynamics of the IFJ-FEF-aIPS-visual cortex network during memory operations, we estimated Granger causality effects between these regions with fMRI data representing memory encoding/maintenance as well as during memory retrieval. We also investigated a set of control conditions involving active processing of stimuli not associated with a memory task and passive viewing. In line with the developing understanding of IFJ as a region critical for control processes with a possible initiating role in visual STM operations, we observed influence from IFJ to FEF and aIPS during memory encoding. Furthermore, FEF predicted activity in a set of higher-order visual areas during memory retrieval, a finding consistent with its suggested role in top-down biasing of sensory cortex.

Use of Clinical and Neuroimaging Characteristics to Distinguish Temporal Lobe Herpes Simplex Encephalitis From Its Mimics

PubMed Central

Chow, Felicia C.; Glaser, Carol A.; Sheriff, Heather; Xia, Dongxiang; Messenger, Sharon; Whitley, Richard; Venkatesan, Arun

2015-01-01

Background. We describe the spectrum of etiologies associated with temporal lobe (TL) encephalitis and identify clinical and radiologic features that distinguish herpes simplex encephalitis (HSE) from its mimics. Methods. We reviewed all adult cases of encephalitis with TL abnormalities on magnetic resonance imaging (MRI) from the California Encephalitis Project. We evaluated the association between specific clinical and MRI characteristics and HSE compared with other causes of TL encephalitis and used multivariate logistic modeling to identify radiologic predictors of HSE. Results. Of 251 cases of TL encephalitis, 43% had an infectious etiology compared with 16% with a noninfectious etiology. Of infectious etiologies, herpes simplex virus was the most commonly identified agent (n = 60), followed by tuberculosis (n = 8) and varicella zoster virus (n = 7). Of noninfectious etiologies, more than half (n = 21) were due to autoimmune disease. Patients with HSE were older (56.8 vs 50.2 years; P = .012), more likely to be white (53% vs 35%; P = .013), more likely to present acutely (88% vs 64%; P = .001) and with a fever (80% vs 49%; P < .001), and less likely to present with a rash (2% vs 15%; P = .010). In a multivariate model, bilateral TL involvement (odds ratio [OR], 0.38; 95% confidence interval [CI], .18–.79; P = .010) and lesions outside the TL, insula, or cingulate (OR, 0.37; 95% CI, .18–.74; P = .005) were associated with lower odds of HSE. Conclusions. In addition to HSE, other infectious and noninfectious etiologies should be considered in the differential diagnosis for TL encephalitis, depending on the presentation. Specific clinical and imaging features may aid in distinguishing HSE from non-HSE causes of TL encephalitis. PMID:25637586

Morphologic Features of Magnetic Resonance Imaging as a Surrogate of Capsular Contracture in Breast Cancer Patients With Implant-based Reconstructions.

PubMed

Tyagi, Neelam; Sutton, Elizabeth; Hunt, Margie; Zhang, Jing; Oh, Jung Hun; Apte, Aditya; Mechalakos, James; Wilgucki, Molly; Gelb, Emily; Mehrara, Babak; Matros, Evan; Ho, Alice

2017-02-01

Capsular contracture (CC) is a serious complication in patients receiving implant-based reconstruction for breast cancer. Currently, no objective methods are available for assessing CC. The goal of the present study was to identify image-based surrogates of CC using magnetic resonance imaging (MRI). We analyzed a retrospective data set of 50 patients who had undergone both a diagnostic MRI scan and a plastic surgeon's evaluation of the CC score (Baker's score) within a 6-month period after mastectomy and reconstructive surgery. The MRI scans were assessed for morphologic shape features of the implant and histogram features of the pectoralis muscle. The shape features, such as roundness, eccentricity, solidity, extent, and ratio length for the implant, were compared with the Baker score. For the pectoralis muscle, the muscle width and median, skewness, and kurtosis of the intensity were compared with the Baker score. Univariate analysis (UVA) using a Wilcoxon rank-sum test and multivariate analysis with the least absolute shrinkage and selection operator logistic regression was performed to determine significant differences in these features between the patient groups categorized according to their Baker's scores. UVA showed statistically significant differences between grade 1 and grade ≥2 for morphologic shape features and histogram features, except for volume and skewness. Only eccentricity, ratio length, and volume were borderline significant in differentiating grade ≤2 and grade ≥3. Features with P<.1 on UVA were used in the multivariate least absolute shrinkage and selection operator logistic regression analysis. Multivariate analysis showed a good level of predictive power for grade 1 versus grade ≥2 CC (area under the receiver operating characteristic curve 0.78, sensitivity 0.78, and specificity 0.82) and for grade ≤2 versus grade ≥3 CC (area under the receiver operating characteristic curve 0.75, sensitivity 0.75, and specificity 0.79). The morphologic shape features described on MR images were associated with the severity of CC. MRI has the potential to further improve the diagnostic ability of the Baker score in breast cancer patients who undergo implant reconstruction. Copyright © 2016 Elsevier Inc. All rights reserved.

Segmentation and texture analysis of structural biomarkers using neighborhood-clustering-based level set in MRI of the schizophrenic brain.

PubMed

Latha, Manohar; Kavitha, Ganesan

2018-02-03

Schizophrenia (SZ) is a psychiatric disorder that especially affects individuals during their adolescence. There is a need to study the subanatomical regions of SZ brain on magnetic resonance images (MRI) based on morphometry. In this work, an attempt was made to analyze alterations in structure and texture patterns in images of the SZ brain using the level-set method and Laws texture features. T1-weighted MRI of the brain from Center of Biomedical Research Excellence (COBRE) database were considered for analysis. Segmentation was carried out using the level-set method. Geometrical and Laws texture features were extracted from the segmented brain stem, corpus callosum, cerebellum, and ventricle regions to analyze pattern changes in SZ. The level-set method segmented multiple brain regions, with higher similarity and correlation values compared with an optimized method. The geometric features obtained from regions of the corpus callosum and ventricle showed significant variation (p < 0.00001) between normal and SZ brain. Laws texture feature identified a heterogeneous appearance in the brain stem, corpus callosum and ventricular regions, and features from the brain stem were correlated with Positive and Negative Syndrome Scale (PANSS) score (p < 0.005). A framework of geometric and Laws texture features obtained from brain subregions can be used as a supplement for diagnosis of psychiatric disorders.

Hepatic perivascular epithelioid cell tumor (PEComa): dynamic CT, MRI, ultrasonography, and pathologic features--analysis of 7 cases and review of the literature.

PubMed

Tan, Yan; Xiao, En-hua

2012-10-01

To evaluate the dynamic CT, MRI, ultrasonography, and pathologic features of hepatic perivascular epithelioid cell tumor (PEComa), improving the understanding and diagnosis of the tumor. A retrospective analysis of CT, MRI, ultrasonography, and pathologic features of 7 hepatic PEComas diagnosed by pathology during 1st January 2005 to 1st September 2011 in our hospital. The performance of dynamic CT, MRI, and ultrasonography revealed that lesions were regular masses with well-defined borders, the maximum diameters were 2.5-8.5 cm (mean = 4 cm), density was homogeneous, contrast-enhanced CT and MRI showed the lesions were significantly and heterogeneously enhanced on arterial phase, less enhanced on portal venous phase, and slightly hypodense on delayed phase. One patient had multiple hepatic lesions and had delayed enhancement. There were no backgrounds of hepatitis and cirrhosis, enlarged lymph nodes, or distant metastases. Pathology showed the gross appearance of the tumor was smooth. Tumor cells were round or polygonal, with clear boundaries and clear membranes, and had abundant translucent cytoplasm. Nuclei were round, with medium size. Tumor cells were epithelial-like cells and arranged in dense sheets. Immunohistochemistry showed that most of them were positive in HMB45 and MelanA, S-100, SMA, while negative in CgA, Syn, CK, CD117, CD10, and CD34. Dynamic CT, MRI, ultrasonography, and pathology of PEComa had some characteristics of benign tumor's performance. Enhanced scan showed PEComa quickly enhanced on arterial phase and enhanced less on portal venous phase. Knowing these characteristics could help to improve the understanding and diagnosis of hepatic PEComa.

Differentiation of orbital lymphoma and idiopathic orbital inflammatory pseudotumor: combined diagnostic value of conventional MRI and histogram analysis of ADC maps.

PubMed

Ren, Jiliang; Yuan, Ying; Wu, Yingwei; Tao, Xiaofeng

2018-05-02

The overlap of morphological feature and mean ADC value restricted clinical application of MRI in the differential diagnosis of orbital lymphoma and idiopathic orbital inflammatory pseudotumor (IOIP). In this paper, we aimed to retrospectively evaluate the combined diagnostic value of conventional magnetic resonance imaging (MRI) and whole-tumor histogram analysis of apparent diffusion coefficient (ADC) maps in the differentiation of the two lesions. In total, 18 patients with orbital lymphoma and 22 patients with IOIP were included, who underwent both conventional MRI and diffusion weighted imaging before treatment. Conventional MRI features and histogram parameters derived from ADC maps, including mean ADC (ADC mean ), median ADC (ADC median ), skewness, kurtosis, 10th, 25th, 75th and 90th percentiles of ADC (ADC 10 , ADC 25 , ADC 75 , ADC 90 ) were evaluated and compared between orbital lymphoma and IOIP. Multivariate logistic regression analysis was used to identify the most valuable variables for discriminating. Differential model was built upon the selected variables and receiver operating characteristic (ROC) analysis was also performed to determine the differential ability of the model. Multivariate logistic regression showed ADC 10 (P = 0.023) and involvement of orbit preseptal space (P = 0.029) were the most promising indexes in the discrimination of orbital lymphoma and IOIP. The logistic model defined by ADC 10 and involvement of orbit preseptal space was built, which achieved an AUC of 0.939, with sensitivity of 77.30% and specificity of 94.40%. Conventional MRI feature of involvement of orbit preseptal space and ADC histogram parameter of ADC 10 are valuable in differential diagnosis of orbital lymphoma and IOIP.

Detailed magnetic resonance imaging features of a case series of primary gliosarcoma.

PubMed

Sampaio, Luísa; Linhares, Paulo; Fonseca, José

2017-12-01

Objective We aimed to characterise the magnetic resonance imaging (MRI) features of a case series of primary gliosarcoma, with the inclusion of diffusion-weighted imaging and perfusion imaging with dynamic susceptibility contrast MRI. Materials and methods We conducted a retrospective study of cases of primary gliosarcoma from the Pathology Department database from January 2006 to December 2014. Clinical and demographic data were obtained. Two neuroradiologists, blinded to diagnosis, assessed tumour location, signal intensity in T1 and T2-weighted images, pattern of enhancement, diffusion-weighted imaging and dynamic susceptibility contrast MRI studies on preoperative MRI. Results Seventeen patients with primary gliosarcomas had preoperative MRI study: seven men and 10 women, with a mean age of 59 years (range 27-74). All lesions were well demarcated, supratentorial and solitary (frontal n = 5, temporal n = 4, parietal n = 3); 13 tumours abutted the dural surface (8/13 with dural enhancement); T1 and T2-weighted imaging patterns were heterogeneous and the majority of lesions (12/17) showed a rim-like enhancement pattern with focal nodularities/irregular thickness. Restricted diffusion (mean apparent diffusion coefficient values 0.64 × 10 -3 mm 2 /s) in the more solid/thick components was present in eight out of 11 patients with diffusion-weighted imaging study. Dynamic susceptibility contrast MRI study ( n = 8) consistently showed hyperperfusion in non-necrotic/cystic components on relative cerebral volume maps. Conclusions The main distinguishing features of primary gliosarcoma are supratentorial and peripheral location, well-defined boundaries and a rim-like pattern of enhancement with an irregular thick wall. Diffusion-weighted imaging and relative cerebral volume map analysis paralleled primary gliosarcoma with high-grade gliomas, thus proving helpful in differential diagnosis.

MRI detection of soleus muscle injuries in professional football players.

PubMed

Pezzotta, G; Querques, G; Pecorelli, A; Nani, R; Sironi, S

2017-11-01

To describe magnetic resonance imaging (MRI) characteristics of soleus muscle injuries in symptomatic professional football players stratified according to both the Munich consensus statement and the British Athletics Muscle Injury Classification (BAMIC), and to investigate the association between specific MRI features and the "return to play" (RTP). Professional football players with an episode of acute posterior calf pain and impaired function, subsequent to sports activity, underwent ultrasound followed by MRI examination reviewed by two different radiologists with more than 10 years of experience in the musculoskeletal system. MRI features and RTP outcome were evaluated for all types of injuries. During a 36-month period, a total of 20 professional football players were evaluated. According to the Munich consensus, 11 were type 3A, 8 were type 3B, and 1 was type 4, whereas according to the BAMIC, 11 lesions were considered grade 1, 4 grade 2, 4 grade 3, and 1 grade 4. RTP data were available for all patients (mean 3.3 ± 1.6 weeks). Both the Munich consensus and the BAMIC correlated with RTP (Spearman correlation = 0.982 and p < 0.0001 and 0.886 and p < 0.0001 respectively). Extension of edema was an independent prognostic factor for RTP in two different models of multivariate regression analysis (p = 0.044 model A; p = 0.031 model B). The Munich consensus and BAMIC grading systems are useful tools for defining the patient's prognosis and proper rehabilitation time after injury. The MRI feature that we should carefully look for is the extension of edema, as it seems to significantly affect the RTP.

Multi-channel MRI segmentation of eye structures and tumors using patient-specific features

PubMed Central

Ciller, Carlos; De Zanet, Sandro; Kamnitsas, Konstantinos; Maeder, Philippe; Glocker, Ben; Munier, Francis L.; Rueckert, Daniel; Thiran, Jean-Philippe

2017-01-01

Retinoblastoma and uveal melanoma are fast spreading eye tumors usually diagnosed by using 2D Fundus Image Photography (Fundus) and 2D Ultrasound (US). Diagnosis and treatment planning of such diseases often require additional complementary imaging to confirm the tumor extend via 3D Magnetic Resonance Imaging (MRI). In this context, having automatic segmentations to estimate the size and the distribution of the pathological tissue would be advantageous towards tumor characterization. Until now, the alternative has been the manual delineation of eye structures, a rather time consuming and error-prone task, to be conducted in multiple MRI sequences simultaneously. This situation, and the lack of tools for accurate eye MRI analysis, reduces the interest in MRI beyond the qualitative evaluation of the optic nerve invasion and the confirmation of recurrent malignancies below calcified tumors. In this manuscript, we propose a new framework for the automatic segmentation of eye structures and ocular tumors in multi-sequence MRI. Our key contribution is the introduction of a pathological eye model from which Eye Patient-Specific Features (EPSF) can be computed. These features combine intensity and shape information of pathological tissue while embedded in healthy structures of the eye. We assess our work on a dataset of pathological patient eyes by computing the Dice Similarity Coefficient (DSC) of the sclera, the cornea, the vitreous humor, the lens and the tumor. In addition, we quantitatively show the superior performance of our pathological eye model as compared to the segmentation obtained by using a healthy model (over 4% DSC) and demonstrate the relevance of our EPSF, which improve the final segmentation regardless of the classifier employed. PMID:28350816

Megalencephalic leukoencephalopathy with subcortical cysts: Characterization of disease variants.

PubMed

Hamilton, Eline M C; Tekturk, Pinar; Cialdella, Fia; van Rappard, Diane F; Wolf, Nicole I; Yalcinkaya, Cengiz; Çetinçelik, Ümran; Rajaee, Ahmad; Kariminejad, Ariana; Paprocka, Justyna; Yapici, Zuhal; Bošnjak, Vlatka Mejaški; van der Knaap, Marjo S

2018-04-17

To provide an overview of clinical and MRI characteristics of the different variants of the leukodystrophy megalencephalic leukoencephalopathy with subcortical cysts (MLC) and identify possible differentiating features. We performed an international multi-institutional, cross-sectional observational study of the clinical and MRI characteristics in patients with genetically confirmed MLC. Clinical information was obtained by questionnaires for physicians and retrospective chart review. We included 204 patients with classic MLC, 187 of whom had recessive mutations in MLC1 (MLC1 variant) and 17 in GLIALCAM (MLC2A variant) and 38 patients with remitting MLC caused by dominant GLIALCAM mutations (MLC2B variant). We observed a relatively wide variability in neurologic disability among patients with classic MLC. No clinical differences could be identified between patients with MLC1 and MLC2A. Patients with MLC2B invariably had a milder phenotype with preservation of motor function, while intellectual disability and autism were relatively frequent. Systematic MRI review revealed no MRI features that distinguish between MLC1 and MLC2A. Radiologic improvement was observed in all patients with MLC2B and also in 2 patients with MLC1. In MRIs obtained in the early disease stage, absence of signal abnormalities of the posterior limb of the internal capsule and cerebellar white matter and presence of only rarefied subcortical white matter instead of true subcortical cysts were suggestive of MLC2B. Clinical and MRI features did not distinguish between classic MLC with MLC1 or GLIALCAM mutations. Absence of signal abnormalities of the internal capsule and cerebellar white matter are MRI findings that point to the remitting phenotype. Copyright © 2018 The Author(s). Published by Wolters Kluwer Health, Inc. on behalf of the American Academy of Neurology.

State-space model with deep learning for functional dynamics estimation in resting-state fMRI.

PubMed

Suk, Heung-Il; Wee, Chong-Yaw; Lee, Seong-Whan; Shen, Dinggang

2016-04-01

Studies on resting-state functional Magnetic Resonance Imaging (rs-fMRI) have shown that different brain regions still actively interact with each other while a subject is at rest, and such functional interaction is not stationary but changes over time. In terms of a large-scale brain network, in this paper, we focus on time-varying patterns of functional networks, i.e., functional dynamics, inherent in rs-fMRI, which is one of the emerging issues along with the network modelling. Specifically, we propose a novel methodological architecture that combines deep learning and state-space modelling, and apply it to rs-fMRI based Mild Cognitive Impairment (MCI) diagnosis. We first devise a Deep Auto-Encoder (DAE) to discover hierarchical non-linear functional relations among regions, by which we transform the regional features into an embedding space, whose bases are complex functional networks. Given the embedded functional features, we then use a Hidden Markov Model (HMM) to estimate dynamic characteristics of functional networks inherent in rs-fMRI via internal states, which are unobservable but can be inferred from observations statistically. By building a generative model with an HMM, we estimate the likelihood of the input features of rs-fMRI as belonging to the corresponding status, i.e., MCI or normal healthy control, based on which we identify the clinical label of a testing subject. In order to validate the effectiveness of the proposed method, we performed experiments on two different datasets and compared with state-of-the-art methods in the literature. We also analyzed the functional networks learned by DAE, estimated the functional connectivities by decoding hidden states in HMM, and investigated the estimated functional connectivities by means of a graph-theoretic approach. Copyright © 2016 Elsevier Inc. All rights reserved.

State-space model with deep learning for functional dynamics estimation in resting-state fMRI

PubMed Central

Suk, Heung-Il; Wee, Chong-Yaw; Lee, Seong-Whan; Shen, Dinggang

2017-01-01

Studies on resting-state functional Magnetic Resonance Imaging (rs-fMRI) have shown that different brain regions still actively interact with each other while a subject is at rest, and such functional interaction is not stationary but changes over time. In terms of a large-scale brain network, in this paper, we focus on time-varying patterns of functional networks, i.e., functional dynamics, inherent in rs-fMRI, which is one of the emerging issues along with the network modelling. Specifically, we propose a novel methodological architecture that combines deep learning and state-space modelling, and apply it to rs-fMRI based Mild Cognitive Impairment (MCI) diagnosis. We first devise a Deep Auto-Encoder (DAE) to discover hierarchical non-linear functional relations among regions, by which we transform the regional features into an embedding space, whose bases are complex functional networks. Given the embedded functional features, we then use a Hidden Markov Model (HMM) to estimate dynamic characteristics of functional networks inherent in rs-fMRI via internal states, which are unobservable but can be inferred from observations statistically. By building a generative model with an HMM, we estimate the likelihood of the input features of rs-fMRI as belonging to the corresponding status, i.e., MCI or normal healthy control, based on which we identify the clinical label of a testing subject. In order to validate the effectiveness of the proposed method, we performed experiments on two different datasets and compared with state-of-the-art methods in the literature. We also analyzed the functional networks learned by DAE, estimated the functional connectivities by decoding hidden states in HMM, and investigated the estimated functional connectivities by means of a graph-theoretic approach. PMID:26774612

A Radio-genomics Approach for Identifying High Risk Estrogen Receptor-positive Breast Cancers on DCE-MRI: Preliminary Results in Predicting OncotypeDX Risk Scores

PubMed Central

Wan, Tao; Bloch, B. Nicolas; Plecha, Donna; Thompson, CheryI L.; Gilmore, Hannah; Jaffe, Carl; Harris, Lyndsay; Madabhushi, Anant

2016-01-01

To identify computer extracted imaging features for estrogen receptor (ER)-positive breast cancers on dynamic contrast en-hanced (DCE)-MRI that are correlated with the low and high OncotypeDX risk categories. We collected 96 ER-positivebreast lesions with low (<18, N = 55) and high (>30, N = 41) OncotypeDX recurrence scores. Each lesion was quantitatively charac-terize via 6 shape features, 3 pharmacokinetics, 4 enhancement kinetics, 4 intensity kinetics, 148 textural kinetics, 5 dynamic histogram of oriented gradient (DHoG), and 6 dynamic local binary pattern (DLBP) features. The extracted features were evaluated by a linear discriminant analysis (LDA) classifier in terms of their ability to distinguish low and high OncotypeDX risk categories. Classification performance was evaluated by area under the receiver operator characteristic curve (Az). The DHoG and DLBP achieved Az values of 0.84 and 0.80, respectively. The 6 top features identified via feature selection were subsequently combined with the LDA classifier to yield an Az of 0.87. The correlation analysis showed that DHoG (ρ = 0.85, P < 0.001) and DLBP (ρ = 0.83, P < 0.01) were significantly associated with the low and high risk classifications from the OncotypeDX assay. Our results indicated that computer extracted texture features of DCE-MRI were highly correlated with the high and low OncotypeDX risk categories for ER-positive cancers. PMID:26887643

Impact of magnetic resonance imaging on ventricular tachyarrhythmia sensing: Results of the Evera MRI Study.

PubMed

Gold, Michael R; Sommer, Torsten; Schwitter, Juerg; Kanal, Emanuel; Bernabei, Matthew A; Love, Charles J; Surber, Ralf; Ramza, Brian; Cerkvenik, Jeffrey; Merkely, Béla

2016-08-01

Studies have shown that magnetic resonance imaging (MRI) conditional pacemakers experience no significant effect from MRI on device function, sensing, or pacing. More recently, similar safety outcomes were demonstrated with MRI conditional defibrillators (implantable cardioverter-defibrillator [ICD]), but the impact on ventricular arrhythmias has not been assessed. The purpose of this study was to assess the effect of MRI on ICD sensing and treatment of ventricular tachyarrhythmias. The Evera MRI Study was a worldwide trial of 156 patients implanted with an ICD designed to be MRI conditional. Device-detected spontaneous and induced ventricular tachycardia/ventricular fibrillation (VT/VF) episodes occurring before and after whole body MRI were evaluated by a blinded episode review committee. Detection delay was computed as the sum of RR intervals of undersensed beats. A ≥5-second delay in detection due to undersensing was prospectively defined as clinically significant. Post-MRI, there were 22 polymorphic VT/VF episodes in 21 patients, with 16 of these patients having 17 VT/VF episodes pre-MRI. Therapy was successful for all episodes, with no failures to treat or terminate arrhythmias. The mean detection delay due to undersensing pre- and post-MRI was 0.60 ± 0.59 and 0.33 ± 0.63 seconds, respectively (P = .17). The maximum detection delay was 2.19 seconds pre-MRI and 2.87 seconds post-MRI. Of the 17 pre-MRI episodes, 14 (82%) had some detection delay as compared with 11 of 22 (50%) post-MRI episodes (P = .03); no detection delay was clinically significant. Detection and treatment of VT/VF was excellent, with no detection delays or significant impact of MRI observed. Copyright © 2016 Heart Rhythm Society. Published by Elsevier Inc. All rights reserved.

Feasibility of ferumoxytol-enhanced neonatal and young infant cardiac MRI without general anesthesia.

PubMed

Lai, Lillian M; Cheng, Joseph Y; Alley, Marcus T; Zhang, Tao; Lustig, Michael; Vasanawala, Shreyas S

2017-05-01

To assess the feasibility of ferumoxytol-enhanced anesthesia-free cardiac MRI in neonates and young infants for complex congenital heart disease (CHD). With Institutional Review Board approval, 21 consecutive neonates and young infants (1 day to 11 weeks old; median age of 3 days) who underwent a rapid two-sequence (MR angiography [MRA] and four-dimensional [4D] flow) MRI protocol with intravenous ferumoxytol without sedation (n = 17) or light sedation (n = 4) at 3 Tesla (T) (except one case at 1.5T) between June 2014 and February 2016 were retrospectively identified. Medical records were reviewed for indication, any complications, if further diagnostic imaging was performed after MRI, and surgical findings. Two radiologists scored the images in two sessions on a 5-point scale for overall image quality and delineation of various anatomical structures. Confidence interval of proportions for likelihood of requiring additional diagnostic imaging after MRI was determined. For the possibility of reducing the protocol to a single rapid sequence, Wilcoxon-rank sum test was used to assess whether 4D flow and MRA significantly differed in anatomical delineation. One of 21 patients (4.8%, 80% confidence interval 0-11%) required additional imaging, a computed tomography angiography to assess lung parenchyma and peripheral pulmonary arteries. Only 1 of 13 patients (7.7%) with operative confirmation had a minor discrepancy between radiology and operative reports (80% confidence interval 0-17%). 4D flow was significantly superior to MRA (P < 0.05) for the evaluation of systemic arteries, valves, ventricular trabeculae, and overall quality. Using Cohen's kappa coefficient, there was good interobserver agreement for the evaluation of systemic arteries by 4D flow (κ = 0.782), and systemic veins and pulmonary arteries by MRA (κ > 0.6). Overall 4D flow measurements (mean κ = 0.64-0.74) had better internal agreement compared with MRA (mean κ = 0.30-0.64). Ferumoxytol-enhanced cardiac MRI, without anesthesia, is feasible for the evaluation of complex CHD in neonates and young infants, with a low likelihood of need for additional diagnostic studies. The decreased risk by avoiding anesthesia must be balanced against the potential for adverse reactions with ferumoxytol. 2 J. MAGN. RESON. IMAGING 2017;45:1407-1418. © 2016 International Society for Magnetic Resonance in Medicine.

Test-retest reliability of evoked heat stimulation BOLD fMRI.

PubMed

Upadhyay, Jaymin; Lemme, Jordan; Anderson, Julie; Bleakman, David; Large, Thomas; Evelhoch, Jeffrey L; Hargreaves, Richard; Borsook, David; Becerra, Lino

2015-09-30

To date, the blood oxygenated-level dependent (BOLD) functional magnetic resonance imaging (fMRI) technique has enabled an objective and deeper understanding of pain processing mechanisms embedded within the human central nervous system (CNS). In order to further comprehend the benefits and limitations of BOLD fMRI in the context of pain as well as the corresponding subjective pain ratings, we evaluated the univariate response, test-retest reliability and confidence intervals (CIs) at the 95% level of both data types collected during evoked stimulation of 40°C (non-noxious), 44°C (mildly noxious) and a subject-specific temperature eliciting a 7/10 pain rating. The test-retest reliability between two scanning sessions was determined by calculating group-level interclass correlation coefficients (ICCs) and at the single-subject level. Across the three stimuli, we initially observed a graded response of increasing magnitude for both VAS (visual analog score) pain ratings and fMRI data. Test-retest reliability was observed to be highest for VAS pain ratings obtained during the 7/10 pain stimulation (ICC=0.938), while ICC values of pain fMRI data for a distribution of CNS structures ranged from 0.5 to 0.859 (p<0.05). Importantly, the upper and lower confidence interval CI bounds reported herein could be utilized in subsequent trials involving healthy volunteers to hypothesize the magnitude of effect required to overcome inherent variability of either VAS pain ratings or BOLD responses evoked during innocuous or noxious thermal stimulation. Copyright © 2015 Elsevier B.V. All rights reserved.

Multicenter prospective study of magnetic resonance imaging prior to breast-conserving surgery for breast cancer.

PubMed

Liu, Qian; Liu, Yinhua; Xu, Ling; Duan, Xuening; Li, Ting; Qin, Naishan; Kang, Hua; Jiang, Hongchuan; Yang, Deqi; Qu, Xiang; Jiang, Zefei; Yu, Chengze

2014-01-01

This multicenter prospective study aimed to assess the utility of dynamic enhanced magnetic resonance imaging (MRI) prior to breast-conserving surgery for breast cancer. The research subjects were drawn from patients with primary early resectable breast cancer treated in the breast disease centers of six three-level hospitals in Beijing from 1 January 2010 to 31 December 2012. The participants were allocated to a breast-conserving surgery group (breast-conserving group) or a total mastectomy group (total mastectomy group). Enhanced MRI was used to measure breast volume, longest diameter of tumor and tumor volume. The correlations between these measurements and those derived from histopathologic findings were assessed. The relationships between the success rate of breast-conserving surgery and MRI- and pathology-based measurement results were statistically analyzed in the breast-conserving group. The study included 461 cases in the total mastectomy group and 195 in the breast-conserving group. Allocation to these groups was based on clinical indications and patient preferences. The cut-off for concurrence between MRI- and pathology-based measurements of the longest diameter of tumor was set at 0.3 cm. In the total mastectomy group, the confidence interval for 95% concurrence of these measurements was 35.41%-44.63%. Correlation coefficients for MRI and histopathology-based measurements of breast volume, tumor volume and tumor volume/breast volume ratio were r = 0.861, 0.569, and 0.600, respectively (all P < 0.001). In the breast-conserving group, with 0.30 cm taken as the cut-off for concurrence, the 95% confidence interval for MRI and pathology-based measurements of the longest diameter of tumor was 29.98%-44.01%. The subjective and objective success rates for breast-conserving surgery were 100% and 88.54%, respectively. There were significant correlations between dynamic enhanced MRI- and histopathology-based measurements of the longest diameter of breast lesions, breast and tumor volumes, and breast volume/tumor volume ratios. Preoperative MRI examination improves the success rate of breast-conserving surgery.

Measurement of segmental lumbar spine flexion and extension using ultrasound imaging.

PubMed

Chleboun, Gary S; Amway, Matthew J; Hill, Jesse G; Root, Kara J; Murray, Hugh C; Sergeev, Alexander V

2012-10-01

Clinical measurement, technical note. To describe a technique to measure interspinous process distance using ultrasound (US) imaging, to assess the reliability of the technique, and to compare the US imaging measurements to magnetic resonance imaging (MRI) measurements in 3 different positions of the lumbar spine. Segmental spinal motion has been assessed using various imaging techniques, as well as surgically inserted pins. However, some imaging techniques are costly (MRI) and some require ionizing radiation (radiographs and fluoroscopy), and surgical procedures have limited use because of the invasive nature of the technique. Therefore, it is important to have an easily accessible and inexpensive technique for measuring lumbar segmental motion to more fully understand spine motion in vivo, to evaluate the changes that occur with various interventions, and to be able to accurately relate the changes in symptoms to changes in motion of individual vertebral segments. Six asymptomatic subjects participated. The distance between spinous processes at each lumbar segment (L1-2, L2-3, L3-4, L4-5) was measured digitally using MRI and US imaging. The interspinous distance was measured with subjects supine and the lumbar spine in 3 different positions (resting, lumbar flexion, and lumbar extension) for both MRI and US imaging. The differences in distance from neutral to extension, neutral to flexion, and extension to flexion were calculated. The measurement methods had excellent reliability for US imaging (intraclass correlation coefficient [ICC3,3] = 0.94; 95% confidence interval: 0.85, 0.97) and MRI (ICC3,3 = 0.98; 95% confidence interval: 0.95, 0.99). The distance measured was similar between US imaging and MRI (P>.05), except at L3-4 flexion-extension (P = .003). On average, the MRI measurements were 1.3 mm greater than the US imaging measurements. This study describes a new method for the measurement of lumbar spine segmental flexion and extension motion using US imaging. The US method may offer an alternative to other imaging techniques to monitor clinical outcomes because of its ease of use and the consistency of measurements compared to MRI.

High resolution magnetic resonance imaging in pathogenesis diagnosis of single lenticulostriate infarction with nonstenotic middle cerebral artery, a retrospective study.

PubMed

Sun, Li-Li; Li, Zhong-Hao; Tang, Wen-Xiong; Liu, Lei; Chang, Fei-Yan; Zhang, Xue-Bin; Ye, Wei-Jie; Lu, Shuo; Liu, Zun-Jing; Zhu, Xian-Jin

2018-04-25

It is usually difficult to identify stroke pathogenesis for single lenticulostriate infarction with nonstenotic middle cerebral artery (MCA). Our aim is to differentiate the two pathogeneses, non-branch atheromatous small vessel disease and branch atheromatous disease (BAD) by high-resolution magnetic resonance imaging (HR-MRI). Thirty-two single lenticulostriate infarction patients with nonstenotic MCA admitted to the China-Japan Friendship Hospital from December 2014 to August 2017 were enrolled for retrospective analysis. National Institutes of Health Stroke Scale (NIHSS), modified Rankin Scale (mRS), atherosclerotic risk factors, imaging features, and the characteristic of MCA vessel wall in HR-MRI were evaluated. MCA plaques were detected in 15(46.9%) patients which implied BAD and 8 of 15 (53.3%) patients had plaques location in upper dorsal side of the vessel wall. Patients with HR-MRI identified plaques had a significantly larger infarction lesion length (1.95 ± 0.86 cm versus 1.38 ± 0.55 cm; P = 0.031) and larger lesion volume (2.95 ± 3.94 cm 3 versus 0.90 ± 0.94 cm 3 ; P = 0.027) than patients without plaques. Patients with HR-MRI identified plaques had a significant higher percentage of proximal lesions than patients without plaques (P = 0.055). However, according to the location of MCA plaques, there were no significant differences in terms of imaging features, NIHSS and mRS. We demonstrated high frequency of MCA atheromatous plaques visualized in single lenticulostriate infarction patients with nonstenotic MCA by using HR-MRI. Patients with HR-MRI identified plaque presented larger infarction lesions and more proximal lesions than patients without plaque, which were consistent with imaging features of BAD. HR-MRI is an important and effective tool for identifying stroke etiology in patients with nonstenotic MCA.

The value of DCE-MRI in assessing histopathological and molecular biological features in induced rat epithelial ovarian carcinomas.

PubMed

Yuan, Su Juan; Qiao, Tian Kui; Qiang, Jin Wei; Cai, Song Qi; Li, Ruo Kun

2017-09-26

To investigate dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) for assessing histopathological and molecular biological features in induced rat epithelial ovarian carcinomas (EOCs). 7,12-dimethylbenz[A]anthracene (DMBA) was applied to induce EOCs in situ in 46 SD rats. Conventional MRI and DCE-MRI were performed to evaluate the morphology and perfusion features of the tumors, including the time-signal intensity curve (TIC), volume transfer constant (K trans ), rate constant (K ep ), extravascular extracellular space volume ratio (V e ) and initial area under the curve (IAUC). DCE-MRI parameters were correlated with histological grade, microvascular density (MVD), vascular endothelial growth factor (VEGF) and fraction of Ki67-positive cells and the serum level of cancer antigen 125 (CA125). Thirty-five of the 46 rats developed EOCs. DCE-MRI showed type III TIC more frequently than type II (29/35 vs. 6/35, p < 0.001) in EOCs. The two types of TIC of tumors had significant differences in the histological grade, MVD, expression of VEGF and Ki67, and the serum level of CA125 (all p < 0.01). K trans , K ep and IAUC values showed significant differences in different histological grades in overall and pairwise comparisons except for IAUC in grade 2 vs. grade 3 (all p < 0.01). There was no significant difference in V e values among the three grade groups (p > 0.05). K trans , K ep and IAUC values were positively correlated with MVD, VEGF and Ki67 expression (all p < 0.01). V e was not significantly correlated with MVD, VEGF expression, Ki67 expression and the CA125 level (all p > 0.05). TIC types and perfusion parameters of DCE-MRI can reflect tumor grade, angiogenesis and cell proliferation to some extent, thereby helping treatment planning and predicting prognosis.

Visual Prediction Error Spreads Across Object Features in Human Visual Cortex

PubMed Central

Summerfield, Christopher; Egner, Tobias

2016-01-01

Visual cognition is thought to rely heavily on contextual expectations. Accordingly, previous studies have revealed distinct neural signatures for expected versus unexpected stimuli in visual cortex. However, it is presently unknown how the brain combines multiple concurrent stimulus expectations such as those we have for different features of a familiar object. To understand how an unexpected object feature affects the simultaneous processing of other expected feature(s), we combined human fMRI with a task that independently manipulated expectations for color and motion features of moving-dot stimuli. Behavioral data and neural signals from visual cortex were then interrogated to adjudicate between three possible ways in which prediction error (surprise) in the processing of one feature might affect the concurrent processing of another, expected feature: (1) feature processing may be independent; (2) surprise might “spread” from the unexpected to the expected feature, rendering the entire object unexpected; or (3) pairing a surprising feature with an expected feature might promote the inference that the two features are not in fact part of the same object. To formalize these rival hypotheses, we implemented them in a simple computational model of multifeature expectations. Across a range of analyses, behavior and visual neural signals consistently supported a model that assumes a mixing of prediction error signals across features: surprise in one object feature spreads to its other feature(s), thus rendering the entire object unexpected. These results reveal neurocomputational principles of multifeature expectations and indicate that objects are the unit of selection for predictive vision. SIGNIFICANCE STATEMENT We address a key question in predictive visual cognition: how does the brain combine multiple concurrent expectations for different features of a single object such as its color and motion trajectory? By combining a behavioral protocol that independently varies expectation of (and attention to) multiple object features with computational modeling and fMRI, we demonstrate that behavior and fMRI activity patterns in visual cortex are best accounted for by a model in which prediction error in one object feature spreads to other object features. These results demonstrate how predictive vision forms object-level expectations out of multiple independent features. PMID:27810936

MRI features of extramedullary myeloma.

PubMed

Tirumani, Sree Harsha; Shinagare, Atul B; Jagannathan, Jyothi P; Krajewski, Katherine M; Munshi, Nikhil C; Ramaiya, Nikhil H

2014-04-01

The purpose of this study was to describe the MRI features of extramedullary myeloma and to evaluate the role of MRI in extramedullary myeloma. The cases of 28 patients (15 men, 13 women; mean age, 57.53 years; range, 34-83 years) with extramedullary myeloma who underwent MRI at one institution from January 2004 through December 2012 were retrospectively identified through an electronic search of an institutional radiology database. Two radiologists reviewed images from 44 MRI examinations in consensus to document the morphologic, signal-intensity, and enhancement characteristics of extramedullary myeloma. Electronic medical records were reviewed to document the indication for MRI and subsequent management of extramedullary myeloma. A total of 72 sites of extramedullary myeloma were noted, most commonly the paraspinal-epidural location (28/72, 39%). Two radiologic patterns were identified: lesions contiguous with bone (n = 44) and lesions noncontiguous with bone (n = 28). Lesions contiguous with bone were larger (p = 0.001; Student t test). Of 28 paraspinal-epidural lesions, 13 compressed the cord. Compared with skeletal muscle, most of the lesions were hypointense to isointense on T1-weighted images (67/72, 93.1%) and isointense to hyperintense on T2-weighted images (62/72, 86.1%). Lesions noncontiguous with bone were more often hypointense on T2-weighted images (8/28 vs 2/44; p = 0.006; Fisher exact test). Neurologic symptoms prompted MRI in most cases (n = 32/44). MRI was helpful in management by radiotherapy and surgery (19/28). Extramedullary myeloma can be contiguous or noncontiguous with bone. Lesions contiguous with bone are larger, often occur in a paraspinal or epidural location, and can cause cord compression. Lesions noncontiguous with bone can be T2 hypointense. MRI helps in treatment planning.

3D variational brain tumor segmentation on a clustered feature set

NASA Astrophysics Data System (ADS)

Popuri, Karteek; Cobzas, Dana; Jagersand, Martin; Shah, Sirish L.; Murtha, Albert

2009-02-01

Tumor segmentation from MRI data is a particularly challenging and time consuming task. Tumors have a large diversity in shape and appearance with intensities overlapping the normal brain tissues. In addition, an expanding tumor can also deflect and deform nearby tissue. Our work addresses these last two difficult problems. We use the available MRI modalities (T1, T1c, T2) and their texture characteristics to construct a multi-dimensional feature set. Further, we extract clusters which provide a compact representation of the essential information in these features. The main idea in this paper is to incorporate these clustered features into the 3D variational segmentation framework. In contrast to the previous variational approaches, we propose a segmentation method that evolves the contour in a supervised fashion. The segmentation boundary is driven by the learned inside and outside region voxel probabilities in the cluster space. We incorporate prior knowledge about the normal brain tissue appearance, during the estimation of these region statistics. In particular, we use a Dirichlet prior that discourages the clusters in the ventricles to be in the tumor and hence better disambiguate the tumor from brain tissue. We show the performance of our method on real MRI scans. The experimental dataset includes MRI scans, from patients with difficult instances, with tumors that are inhomogeneous in appearance, small in size and in proximity to the major structures in the brain. Our method shows good results on these test cases.

Enhancing insight in scientific problem solving by highlighting the functional features of prototypes: an fMRI study.

PubMed

Hao, Xin; Cui, Shuai; Li, Wenfu; Yang, Wenjing; Qiu, Jiang; Zhang, Qinglin

2013-10-09

Insight can be the first step toward creating a groundbreaking product. As evident in anecdotes and major inventions in history, heuristic events (heuristic prototypes) prompted inventors to acquire insight when solving problems. Bionic imitation in scientific innovation is an example of this kind of problem solving. In particular, heuristic prototypes (e.g., the lotus effect; the very high water repellence exhibited by lotus leaves) help solve insight problems (e.g., non-stick surfaces). We speculated that the biological functional feature of prototypes is a critical factor in inducing insightful scientific problem solving. In this functional magnetic resonance imaging (fMRI) study, we selected scientific innovation problems and utilized "learning prototypes-solving problems" two-phase paradigm to test the supposition. We also explored its neural mechanisms. Functional MRI data showed that the activation of the middle temporal gyrus (MTG, BA 37) and the middle occipital gyrus (MOG, BA 19) were associated with the highlighted functional feature condition. fMRI data also indicated that the MTG (BA 37) could be responsible for the semantic processing of functional features and for the formation of novel associations based on related functions. In addition, the MOG (BA 19) could be involved in the visual imagery of formation and application of function association between the heuristic prototype and problem. Our findings suggest that both semantic processing and visual imagery could be crucial components underlying scientific problem solving. © 2013 Elsevier B.V. All rights reserved.

MR imaging features and staging of neuroendocrine carcinomas of the uterine cervix with pathological correlations.

PubMed

Duan, Xiaohui; Ban, Xiaohua; Zhang, Xiang; Hu, Huijun; Li, Guozhao; Wang, Dongye; Wang, Charles Qian; Zhang, Fang; Shen, Jun

2016-12-01

To determine MR imaging features and staging accuracy of neuroendocrine carcinomas (NECs) of the uterine cervix with pathological correlations. Twenty-six patients with histologically proven NECs, 60 patients with squamous cell carcinomas (SCCs), and 30 patients with adenocarcinomas of the uterine cervix were included. The clinical data, pathological findings, and MRI findings were reviewed retrospectively. MRI features of cervical NECs, SCCs, and adenocarcinomas were compared, and MRI staging of cervical NECs was compared with the pathological staging. Cervical NECs showed a higher tendency toward a homogeneous signal intensity on T2-weighted imaging and a homogeneous enhancement pattern, as well as a lower ADC value of tumour and a higher incidence of lymphadenopathy, compared with SCCs and adenocarcinomas (P < 0.05). An ADC value cutoff of 0.90 × 10 -3  mm 2 /s was robust for differentiation between cervical NECs and other cervical cancers, with a sensitivity of 63.3 % and a specificity of 95 %. In 21 patients who underwent radical hysterectomy and lymphadenectomy, the overall accuracy of tumour staging by MR imaging was 85.7 % with reference to pathology staging. Homogeneous lesion texture and low ADC value are likely suggestive features of cervical NECs and MR imaging is reliable for the staging of cervical NECs. • Cervical NECs show a tendency of lesion homogeneity and lymphadenopathy • Low ADC values are found in cervical NECs • MRI is an accurate imaging modality for the cervical NEC staging.

Histology-derived volumetric annotation of the human hippocampal subfields in postmortem MRI.

PubMed

Adler, Daniel H; Pluta, John; Kadivar, Salmon; Craige, Caryne; Gee, James C; Avants, Brian B; Yushkevich, Paul A

2014-01-01

Recently, there has been a growing effort to analyze the morphometry of hippocampal subfields using both in vivo and postmortem magnetic resonance imaging (MRI). However, given that boundaries between subregions of the hippocampal formation (HF) are conventionally defined on the basis of microscopic features that often lack discernible signature in MRI, subfield delineation in MRI literature has largely relied on heuristic geometric rules, the validity of which with respect to the underlying anatomy is largely unknown. The development and evaluation of such rules are challenged by the limited availability of data linking MRI appearance to microscopic hippocampal anatomy, particularly in three dimensions (3D). The present paper, for the first time, demonstrates the feasibility of labeling hippocampal subfields in a high resolution volumetric MRI dataset based directly on microscopic features extracted from histology. It uses a combination of computational techniques and manual post-processing to map subfield boundaries from a stack of histology images (obtained with 200μm spacing and 5μm slice thickness; stained using the Kluver-Barrera method) onto a postmortem 9.4Tesla MRI scan of the intact, whole hippocampal formation acquired with 160μm isotropic resolution. The histology reconstruction procedure consists of sequential application of a graph-theoretic slice stacking algorithm that mitigates the effects of distorted slices, followed by iterative affine and diffeomorphic co-registration to postmortem MRI scans of approximately 1cm-thick tissue sub-blocks acquired with 200μm isotropic resolution. These 1cm blocks are subsequently co-registered to the MRI of the whole HF. Reconstruction accuracy is evaluated as the average displacement error between boundaries manually delineated in both the histology and MRI following the sequential stages of reconstruction. The methods presented and evaluated in this single-subject study can potentially be applied to multiple hippocampal tissue samples in order to construct a histologically informed MRI atlas of the hippocampal formation. © 2013 Elsevier Inc. All rights reserved.

Magnetic resonance imaging of pulmonary infection in immunocompromised children: comparison with multidetector computed tomography.

PubMed

Ozcan, H Nursun; Gormez, Ayşegul; Ozsurekci, Yasemin; Karakaya, Jale; Oguz, Berna; Unal, Sule; Cetin, Mualla; Ceyhan, Mehmet; Haliloglu, Mithat

2017-02-01

Computed tomography (CT) is commonly used to detect pulmonary infection in immunocompromised children. To compare MRI and multidetector CT findings of pulmonary abnormalities in immunocompromised children. Seventeen neutropaenic children (6 girls; ages 2-18 years) were included. Non-contrast-enhanced CT was performed with a 64-detector CT scanner. Axial and coronal non-enhanced thoracic MRI was performed using a 1.5-T scanner within 24 h of the CT examination (true fast imaging with steady-state free precession, fat-saturated T2-weighted turbo spin echo with motion correction, T2-weighted half-Fourier single-shot turbo spin echo [HASTE], fat-saturated T1-weighted spoiled gradient echo). Pulmonary abnormalities (nodules, consolidations, ground glass opacities, atelectasis, pleural effusion and lymph nodes) were evaluated and compared among MRI sequences and between MRI and CT. The relationship between MRI sequences and nodule sizes was examined by chi- square test. Of 256 CT lesions, 207 (81%, 95% confidence interval [CI] 76-85%) were detected at MRI. Of 202 CT-detected nodules, 157 (78%, 95% CI 71-83%) were seen at motion-corrected MRI. Of the 1-5-mm nodules, 69% were detected by motion-corrected T2-weighted MRI and 38% by HASTE MRI. Sensitivity of MRI (both axial fat-saturated T2-weighted turbo spin echo with variable phase encoding directions (BLADE) images and HASTE sequences) to detect pulmonary abnormalities is promising.

Breast MRI radiogenomics: Current status and research implications.

PubMed

Grimm, Lars J

2016-06-01

Breast magnetic resonance imaging (MRI) radiogenomics is an emerging area of research that has the potential to directly influence clinical practice. Clinical MRI scanners today are capable of providing excellent temporal and spatial resolution, which allows extraction of numerous imaging features via human extraction approaches or complex computer vision algorithms. Meanwhile, advances in breast cancer genetics research has resulted in the identification of promising genes associated with cancer outcomes. In addition, validated genomic signatures have been developed that allow categorization of breast cancers into distinct molecular subtypes as well as predict the risk of cancer recurrence and response to therapy. Current radiogenomics research has been directed towards exploratory analysis of individual genes, understanding tumor biology, and developing imaging surrogates to genetic analysis with the long-term goal of developing a meaningful tool for clinical care. The background of breast MRI radiogenomics research, image feature extraction techniques, approaches to radiogenomics research, and promising areas of investigation are reviewed. J. Magn. Reson. Imaging 2016;43:1269-1278. © 2015 Wiley Periodicals, Inc.

Advanced magnetic resonance imaging of neurodegenerative diseases.

PubMed

Agosta, Federica; Galantucci, Sebastiano; Filippi, Massimo

2017-01-01

Magnetic resonance imaging (MRI) is playing an increasingly important role in the study of neurodegenerative diseases, delineating the structural and functional alterations determined by these conditions. Advanced MRI techniques are of special interest for their potential to characterize the signature of each neurodegenerative condition and aid both the diagnostic process and the monitoring of disease progression. This aspect will become crucial when disease-modifying (personalized) therapies will be established. MRI techniques are very diverse and go from the visual inspection of MRI scans to more complex approaches, such as manual and automatic volume measurements, diffusion tensor MRI, and functional MRI. All these techniques allow us to investigate the different features of neurodegeneration. In this review, we summarize the most recent advances concerning the use of MRI in some of the most important neurodegenerative conditions, putting an emphasis on the advanced techniques.

Multiparametric Breast MRI of Breast Cancer

PubMed Central

Rahbar, Habib; Partridge, Savannah C.

2015-01-01

Synopsis Breast MRI has increased in popularity over the past two decades due to evidence for its high sensitivity for cancer detection. Current clinical MRI approaches rely on the use of a dynamic contrast enhanced (DCE-MRI) acquisition that facilitates morphologic and semi-quantitative kinetic assessments of breast lesions. The use of more functional and quantitative parameters, such as pharmacokinetic features from high temporal resolution DCE-MRI, apparent diffusion coefficient (ADC) and intravoxel incoherent motion (IVIM) on diffusion weighted MRI, and choline concentrations on MR spectroscopy, hold promise to broaden the utility of MRI and improve its specificity. However, due to wide variations in approach among centers for measuring these parameters and the considerable technical challenges, robust multicenter data supporting their routine use is not yet available, limiting current applications of many of these tools to research purposes. PMID:26613883

Selective Audiovisual Semantic Integration Enabled by Feature-Selective Attention

PubMed Central

Li, Yuanqing; Long, Jinyi; Huang, Biao; Yu, Tianyou; Wu, Wei; Li, Peijun; Fang, Fang; Sun, Pei

2016-01-01

An audiovisual object may contain multiple semantic features, such as the gender and emotional features of the speaker. Feature-selective attention and audiovisual semantic integration are two brain functions involved in the recognition of audiovisual objects. Humans often selectively attend to one or several features while ignoring the other features of an audiovisual object. Meanwhile, the human brain integrates semantic information from the visual and auditory modalities. However, how these two brain functions correlate with each other remains to be elucidated. In this functional magnetic resonance imaging (fMRI) study, we explored the neural mechanism by which feature-selective attention modulates audiovisual semantic integration. During the fMRI experiment, the subjects were presented with visual-only, auditory-only, or audiovisual dynamical facial stimuli and performed several feature-selective attention tasks. Our results revealed that a distribution of areas, including heteromodal areas and brain areas encoding attended features, may be involved in audiovisual semantic integration. Through feature-selective attention, the human brain may selectively integrate audiovisual semantic information from attended features by enhancing functional connectivity and thus regulating information flows from heteromodal areas to brain areas encoding the attended features. PMID:26759193

A computerized MRI biomarker quantification scheme for a canine model of Duchenne muscular dystrophy.

PubMed

Wang, Jiahui; Fan, Zheng; Vandenborne, Krista; Walter, Glenn; Shiloh-Malawsky, Yael; An, Hongyu; Kornegay, Joe N; Styner, Martin A

2013-09-01

Golden retriever muscular dystrophy (GRMD) is a widely used canine model of Duchenne muscular dystrophy (DMD). Recent studies have shown that magnetic resonance imaging (MRI) can be used to non-invasively detect consistent changes in both DMD and GRMD. In this paper, we propose a semiautomated system to quantify MRI biomarkers of GRMD. Our system was applied to a database of 45 MRI scans from 8 normal and 10 GRMD dogs in a longitudinal natural history study. We first segmented six proximal pelvic limb muscles using a semiautomated full muscle segmentation method. We then performed preprocessing, including intensity inhomogeneity correction, spatial registration of different image sequences, intensity calibration of T2-weighted and T2-weighted fat-suppressed images, and calculation of MRI biomarker maps. Finally, for each of the segmented muscles, we automatically measured MRI biomarkers of muscle volume, intensity statistics over MRI biomarker maps, and statistical image texture features. The muscle volume and the mean intensities in T2 value, fat, and water maps showed group differences between normal and GRMD dogs. For the statistical texture biomarkers, both the histogram and run-length matrix features showed obvious group differences between normal and GRMD dogs. The full muscle segmentation showed significantly less error and variability in the proposed biomarkers when compared to the standard, limited muscle range segmentation. The experimental results demonstrated that this quantification tool could reliably quantify MRI biomarkers in GRMD dogs, suggesting that it would also be useful for quantifying disease progression and measuring therapeutic effect in DMD patients.

Biomechanical factors and physical examination findings in osteoarthritis of the knee: associations with tissue abnormalities assessed by conventional radiography and high-resolution 3.0 Tesla magnetic resonance imaging.

PubMed

Knoop, Jesper; Dekker, Joost; Klein, Jan-Paul; van der Leeden, Marike; van der Esch, Martin; Reiding, Dick; Voorneman, Ramon E; Gerritsen, Martijn; Roorda, Leo D; Steultjens, Martijn P M; Lems, Willem F

2012-10-05

We aimed to explore the associations between knee osteoarthritis (OA)-related tissue abnormalities assessed by conventional radiography (CR) and by high-resolution 3.0 Tesla magnetic resonance imaging (MRI), as well as biomechanical factors and findings from physical examination in patients with knee OA. This was an explorative cross-sectional study of 105 patients with knee OA. Index knees were imaged using CR and MRI. Multiple features from CR and MRI (cartilage, osteophytes, bone marrow lesions, effusion and synovitis) were related to biomechanical factors (quadriceps and hamstrings muscle strength, proprioceptive accuracy and varus-valgus laxity) and physical examination findings (bony tenderness, crepitus, bony enlargement and palpable warmth), using multivariable regression analyses. Quadriceps weakness was associated with cartilage integrity, effusion, synovitis (all detected by MRI) and CR-detected joint space narrowing. Knee joint laxity was associated with MRI-detected cartilage integrity, CR-detected joint space narrowing and osteophyte formation. Multiple tissue abnormalities including cartilage integrity, osteophytes and effusion, but only those detected by MRI, were found to be associated with physical examination findings such as crepitus. We observed clinically relevant findings, including a significant association between quadriceps weakness and both effusion and synovitis, detected by MRI. Inflammation was detected in over one-third of the participants, emphasizing the inflammatory component of OA and a possible important role for anti-inflammatory therapies in knee OA. In general, OA-related tissue abnormalities of the knee, even those detected by MRI, were found to be discordant with biomechanical and physical examination features.

Intratumor heterogeneity of DCE-MRI reveals Ki-67 proliferation status in breast cancer

NASA Astrophysics Data System (ADS)

Cheng, Hu; Fan, Ming; Zhang, Peng; Liu, Bin; Shao, Guoliang; Li, Lihua

2018-03-01

Breast cancer is a highly heterogeneous disease both biologically and clinically, and certain pathologic parameters, i.e., Ki67 expression, are useful in predicting the prognosis of patients. The aim of the study is to identify intratumor heterogeneity of breast cancer for predicting Ki-67 proliferation status in estrogen receptor (ER)-positive breast cancer patients. A dataset of 77 patients was collected who underwent dynamic contrast enhancement magnetic resonance imaging (DCE-MRI) examination. Of these patients, 51 were high-Ki-67 expression and 26 were low-Ki-67 expression. We partitioned the breast tumor into subregions using two methods based on the values of time to peak (TTP) and peak enhancement rate (PER). Within each tumor subregion, image features were extracted including statistical and morphological features from DCE-MRI. The classification models were applied on each region separately to assess whether the classifiers based on features extracted from various subregions features could have different performance for prediction. An area under a receiver operating characteristic curve (AUC) was computed using leave-one-out cross-validation (LOOCV) method. The classifier using features related with moderate time to peak achieved best performance with AUC of 0.826 than that based on the other regions. While using multi-classifier fusion method, the AUC value was significantly (P=0.03) increased to 0.858+/-0.032 compare to classifier with AUC of 0.778 using features from the entire tumor. The results demonstrated that features reflect heterogeneity in intratumoral subregions can improve the classifier performance to predict the Ki-67 proliferation status than the classifier using features from entire tumor alone.

Long-Term Performance of Readers Trained in Grading Crohn Disease Activity Using MRI.

PubMed

Puylaert, Carl A J; Tielbeek, Jeroen A W; Bipat, Shandra; Boellaard, Thierry N; Nio, C Yung; Stoker, Jaap

2016-12-01

We aim to evaluate the long-term performance of readers who had participated in previous magnetic resonance imaging (MRI) reader training in grading Crohn disease activity. Fourteen readers (8 women; 12 radiologists, 2 residents; mean age 40; range 31-59), who had participated in a previous MRI reader training, participated in a follow-up evaluation after a mean interval of 29 months (range 25-34 months). Follow-up evaluation comprised 25 MRI cases of suspected or known Crohn disease patients with direct feedback; cases were identical to the evaluation set used in the initial reader training (of which readers were unaware). Grading accuracy, overstaging, and understaging were compared between training and follow-up using a consensus score by two experienced abdominal radiologists as the reference standard. In the follow-up evaluation, overall grading accuracy was 73% (95% confidence interval [CI]: 62%-81%), which was comparable to reader training grading accuracy (72%, 95% CI: 61%-80%) (P = .66). Overstaging decreased significantly from 19% (95% CI: 12%-27%) to 13% (95% CI: 8%-21%) between training and follow-up (P = .03), whereas understaging increased significantly from 9% (95% CI: 4%-21%) to 14% (95% CI: 7%-26%) (P < .01). Readers have consistent long-term accuracy for grading Crohn disease activity after case-based reader training with direct feedback. Copyright © 2016 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.

Classifying MCI Subtypes in Community-Dwelling Elderly Using Cross-Sectional and Longitudinal MRI-Based Biomarkers

PubMed Central

Guan, Hao; Liu, Tao; Jiang, Jiyang; Tao, Dacheng; Zhang, Jicong; Niu, Haijun; Zhu, Wanlin; Wang, Yilong; Cheng, Jian; Kochan, Nicole A.; Brodaty, Henry; Sachdev, Perminder; Wen, Wei

2017-01-01

Amnestic MCI (aMCI) and non-amnestic MCI (naMCI) are considered to differ in etiology and outcome. Accurately classifying MCI into meaningful subtypes would enable early intervention with targeted treatment. In this study, we employed structural magnetic resonance imaging (MRI) for MCI subtype classification. This was carried out in a sample of 184 community-dwelling individuals (aged 73–85 years). Cortical surface based measurements were computed from longitudinal and cross-sectional scans. By introducing a feature selection algorithm, we identified a set of discriminative features, and further investigated the temporal patterns of these features. A voting classifier was trained and evaluated via 10 iterations of cross-validation. The best classification accuracies achieved were: 77% (naMCI vs. aMCI), 81% (aMCI vs. cognitively normal (CN)) and 70% (naMCI vs. CN). The best results for differentiating aMCI from naMCI were achieved with baseline features. Hippocampus, amygdala and frontal pole were found to be most discriminative for classifying MCI subtypes. Additionally, we observed the dynamics of classification of several MRI biomarkers. Learning the dynamics of atrophy may aid in the development of better biomarkers, as it may track the progression of cognitive impairment. PMID:29085292

Pseudo CT estimation from MRI using patch-based random forest

NASA Astrophysics Data System (ADS)

Yang, Xiaofeng; Lei, Yang; Shu, Hui-Kuo; Rossi, Peter; Mao, Hui; Shim, Hyunsuk; Curran, Walter J.; Liu, Tian

2017-02-01

Recently, MR simulators gain popularity because of unnecessary radiation exposure of CT simulators being used in radiation therapy planning. We propose a method for pseudo CT estimation from MR images based on a patch-based random forest. Patient-specific anatomical features are extracted from the aligned training images and adopted as signatures for each voxel. The most robust and informative features are identified using feature selection to train the random forest. The well-trained random forest is used to predict the pseudo CT of a new patient. This prediction technique was tested with human brain images and the prediction accuracy was assessed using the original CT images. Peak signal-to-noise ratio (PSNR) and feature similarity (FSIM) indexes were used to quantify the differences between the pseudo and original CT images. The experimental results showed the proposed method could accurately generate pseudo CT images from MR images. In summary, we have developed a new pseudo CT prediction method based on patch-based random forest, demonstrated its clinical feasibility, and validated its prediction accuracy. This pseudo CT prediction technique could be a useful tool for MRI-based radiation treatment planning and attenuation correction in a PET/MRI scanner.

Dengue encephalitis with predominant cerebellar involvement: report of eight cases with MR and CT imaging features.

PubMed

Hegde, Vinay; Aziz, Zarina; Kumar, Sharath; Bhat, Maya; Prasad, Chandrajit; Gupta, A K; Netravathi, M; Saini, Jitender

2015-03-01

CNS dengue infection is a rare condition and the pattern of brain involvement has not been well described. We report the MR imaging (MRI) features in eight cases of dengue encephalitis. We retrospectively searched cases of dengue encephalitis in which imaging was performed. Eight cases (three men, five women; age range: 8-42 years) diagnosed with dengue encephalitis were included in the study. MR studies were performed on 3-T and 1.5-T MR clinical systems. Two neuroradiologists retrospectively reviewed the MR images and analysed the type of lesions, as well as their distribution and imaging features. All eight cases exhibited MRI abnormalities and the cerebellum was involved in all cases. In addition, MRI signal changes were also noted in the brainstem, thalamus, basal ganglia, internal capsule, insula, mesial temporal lobe, and cortical and cerebral white matter. Areas of susceptibility, diffusion restriction, and patchy post-contrast enhancement were the salient imaging features in our cohort of cases. A pattern of symmetrical cerebellar involvement and presence of microbleeds/haemorrhage may serve as a useful imaging marker and may help in the diagnosis of dengue encephalitis.

Sponyloarthritis features forecasting the presence of HLA-B27 or sacroiliitis on magnetic resonance imaging in patients with suspected axial spondyloarthritis: results from a cross-sectional study in the ESPeranza Cohort.

PubMed

Navarro-Compán, Victoria; de Miguel, Eugenio; van der Heijde, Désirée; Landewé, Robert; Almodóvar, Raquel; Montilla, Carlos; Beltrán, Emma; Zarco, Pedro

2015-09-23

Chronic back pain (CBP) is frequently the presenting symptom in patients with suspected axial spondyloarthritis (axSpA). Presence of sacroiliitis on magnetic-resonance-imaging (MRI) or HLA-B27 adds to diagnostic certainty. However, these costly tests cannot be applied in all patients with CBP. This study aims to investigate which SpA features increase the likelihood of a positive HLA-B27 or positive MRI of the sacroiliac-joints (MRI-SI) in patients with suspected axSpA. Data from 665 patients with CBP within the ESPeranza Programme were analysed. Diagnostic utility measures (LR+, LR-) for a positive MRI-SI or HLA-B27 were calculated for various definitions of inflammatory back pain (IBP), their separate items and for other SpA features. Pretest probabilityies of a positive result was 41% for MRI-SI and 40% for HLA-B27. For a positive MRI-SI result the most useful IBP characteristic was alternating buttock pain (LR + =2.6). Among the IBP-criteria, fulfillment of the 'ASAS criteria' (LR + =2.1) was most contributory. Interestingly, the addition of alternating buttock pain to the Calin/ASAS-IBP criteria (LR + =6.0 and 5.5, respectively) or the addition of awakening at second half of night to the Calin-IBP criteria (LR + =5.5) increased the pre-test probability of MRI-sacroiliitis from 41% to 79-80%. Dactylitis (LR + =4.1) and inflammatory bowel disease (IBD) (LR + =6.4) increased this probability to 73% and 81%, respectively. To forecast HLA-B27 positivity, awakening at the second half of the night, fulfillment of the ASAS-IBP definition and uveitis were the most useful, but only marginally predictive (LR + = 1.3, 1,6 and 2.6, respectively). If patients with suspected axial SpA have either (1) IBP according to Calin/ASAS definition plus alternating buttock pain, or (2) IBP according to Calin definition plus awakening at night, or (3) dactylitis or 4) IBD, the probability of finding a positive MRI-SI increases significantly.

MR imaging guidance for minimally invasive procedures

NASA Astrophysics Data System (ADS)

Wong, Terence Z.; Kettenbach, Joachim; Silverman, Stuart G.; Schwartz, Richard B.; Morrison, Paul R.; Kacher, Daniel F.; Jolesz, Ferenc A.

1998-04-01

Image guidance is one of the major challenges common to all minimally invasive procedures including biopsy, thermal ablation, endoscopy, and laparoscopy. This is essential for (1) identifying the target lesion, (2) planning the minimally invasive approach, and (3) monitoring the therapy as it progresses. MRI is an ideal imaging modality for this purpose, providing high soft tissue contrast and multiplanar imaging, capability with no ionizing radiation. An interventional/surgical MRI suite has been developed at Brigham and Women's Hospital which provides multiplanar imaging guidance during surgery, biopsy, and thermal ablation procedures. The 0.5T MRI system (General Electric Signa SP) features open vertical access, allowing intraoperative imaging to be performed. An integrated navigational system permits near real-time control of imaging planes, and provides interactive guidance for positioning various diagnostic and therapeutic probes. MR imaging can also be used to monitor cryotherapy as well as high temperature thermal ablation procedures sing RF, laser, microwave, or focused ultrasound. Design features of the interventional MRI system will be discussed, and techniques will be described for interactive image acquisition and tracking of interventional instruments. Applications for interactive and near-real-time imaging will be presented as well as examples of specific procedures performed using MRI guidance.

MRI and clinical features of maple syrup urine disease: preliminary results in 10 cases

PubMed Central

Cheng, Ailan; Han, Lianshu; Feng, Yun; Li, Huimin; Yao, Rong; Wang, Dengbin; Jin, Biao

2017-01-01

PURPOSE We aimed to evaluate the magnetic resonance imaging (MRI) and clinical features of maple syrup urine disease (MSUD). METHODS This retrospective study consisted of 10 MSUD patients confirmed by genetic testing. All patients underwent brain MRI. Phenotype, genotype, and areas of brain injury on MRI were retrospectively reviewed. RESULTS Six patients (60%) had the classic form of MSUD with BCKDHB mutation, three patients (30%) had the intermittent form (two with BCKDHA mutations and one with DBT mutation), and one patient (10%) had the thiamine-responsive form with DBT mutation. On diffusion-weighted imaging, nine cases presented restricted diffusion in myelinated areas, and one intermittent case with DBT mutation was normal. The classic form of MSUD involved the basal ganglia in six cases; the cerebellum, mesencephalon, pons, and supratentorial area in five cases; and the thalamus in four cases, respectively. The intermittent form involved the cerebellum, pons, and supratentorial area in two cases. The thiamine-responsive form involved the basal ganglia and supratentorial area. CONCLUSION Our preliminary results indicate that patients with MSUD presented more commonly in classic form with BCKDHB mutation and displayed extensive brain injury on MRI. PMID:28830848

Neoadjuvant radiation in primary extremity liposarcoma: correlation of MRI features with histopathology.

PubMed

Wortman, Jeremy R; Tirumani, Sree Harsha; Tirumani, Harika; Shinagare, Atul B; Jagannathan, Jyothi P; Hornick, Jason L; Ramaiya, Nikhil H

2016-05-01

To evaluate MRI features of response of primary extremity liposarcoma (LPS) to neoadjuvant radiation therapy (RT) with histopathologic correlation. In this IRB-approved study including 125 patients with extremity LPS treated with neoadjuvant RT from 2000 to 2013, MRI of the primary tumour in 18 patients (5 pleomorphic LPS, 13 myxoid LPS) before and after RT were reviewed by two radiologists by consensus. Histopathology of the surgical specimens was reviewed by a pathologist with expertise in sarcomas. In the pleomorphic LPS cohort, 3/5 tumours increased in size; 3/5 decreased in enhancing component; and 3/5 increased in peritumoral oedema, intratumoral haemorrhage, and necrosis. In the myxoid LPS cohort, 12/13 tumours decreased in size, 8/13 decreased in enhancing component, and 5/13 increased in internal fat following RT. Histopathology showed ≥50% residual tumour in 1/5 pleomorphic LPS and 2/13 myxoid LPS. Hyalinization/necrosis of ≥75% was noted in 4/5 pleomorphic LPS and 11/13 myxoid LPS. Cytodifferentiation was noted in 1/5 pleomorphic and 9/13 myxoid LPS. While pleomorphic LPS showed an increase in size, peritumoral oedema, intratumoral haemorrhage, and necrosis on MRI following neoadjuvant RT, myxoid LPS showed a decrease in size and enhancement with an increase in internal fat. • Pleomorphic LPS commonly increase in size and necrosis on MRI following RT. • Myxoid LPS commonly decrease in size and enhancement on MRI following RT. • Myxoid LPS often increase in fatty component on MRI following RT.

Resting-state functional MRI as a tool for evaluating brain hemodynamic responsiveness to external stimuli in rats.

PubMed

Paasonen, Jaakko; Salo, Raimo A; Huttunen, Joanna K; Gröhn, Olli

2017-09-01

Anesthesia is a major confounding factor in functional MRI (fMRI) experiments attributed to its effects on brain function. Recent evidence suggests that parameters obtained with resting-state fMRI (rs-fMRI) are coupled with anesthetic depth. Therefore, we investigated whether parameters obtained with rs-fMRI, such as functional connectivity (FC), are also directly related to blood-oxygen-level-dependent (BOLD) responses. A simple rs-fMRI protocol was implemented in a pharmacological fMRI study to evaluate the coupling between hemodynamic responses and FC under five anesthetics (α-chloralose, isoflurane, medetomidine, thiobutabarbital, and urethane). Temporal change in the FC was evaluated at 1-hour interval. Supplementary forepaw stimulation experiments were also conducted. Under thiobutabarbital anesthesia, FC was clearly coupled with nicotine-induced BOLD responses. Good correlation values were also obtained under isoflurane and medetomidine anesthesia. The observations in the thiobutabarbital group were supported by forepaw stimulation experiments. Additionally, the rs-fMRI protocol revealed significant temporal changes in the FC in the α-chloralose, thiobutabarbital, and urethane groups. Our results suggest that FC can be used to estimate brain hemodynamic responsiveness to stimuli and evaluate the level and temporal changes of anesthesia. Therefore, analysis of the fMRI baseline signal may be highly valuable tool for controlling the outcome of preclinical fMRI experiments. Magn Reson Med 78:1136-1146, 2017. © 2016 International Society for Magnetic Resonance in Medicine. © 2016 International Society for Magnetic Resonance in Medicine.

Primary central nervous system lymphoma and glioblastoma differentiation based on conventional magnetic resonance imaging by high-throughput SIFT features.

PubMed

Chen, Yinsheng; Li, Zeju; Wu, Guoqing; Yu, Jinhua; Wang, Yuanyuan; Lv, Xiaofei; Ju, Xue; Chen, Zhongping

2018-07-01

Due to the totally different therapeutic regimens needed for primary central nervous system lymphoma (PCNSL) and glioblastoma (GBM), accurate differentiation of the two diseases by noninvasive imaging techniques is important for clinical decision-making. Thirty cases of PCNSL and 66 cases of GBM with conventional T1-contrast magnetic resonance imaging (MRI) were analyzed in this study. Convolutional neural networks was used to segment tumor automatically. A modified scale invariant feature transform (SIFT) method was utilized to extract three-dimensional local voxel arrangement information from segmented tumors. Fisher vector was proposed to normalize the dimension of SIFT features. An improved genetic algorithm (GA) was used to extract SIFT features with PCNSL and GBM discrimination ability. The data-set was divided into a cross-validation cohort and an independent validation cohort by the ratio of 2:1. Support vector machine with the leave-one-out cross-validation based on 20 cases of PCNSL and 44 cases of GBM was employed to build and validate the differentiation model. Among 16,384 high-throughput features, 1356 features show significant differences between PCNSL and GBM with p < 0.05 and 420 features with p < 0.001. A total of 496 features were finally chosen by improved GA algorithm. The proposed method produces PCNSL vs. GBM differentiation with an area under the curve (AUC) curve of 99.1% (98.2%), accuracy 95.3% (90.6%), sensitivity 85.0% (80.0%) and specificity 100% (95.5%) on the cross-validation cohort (and independent validation cohort). Since the local voxel arrangement characterization provided by SIFT features, proposed method produced more competitive PCNSL and GBM differentiation performance by using conventional MRI than methods based on advanced MRI.

Can responses to basic non-numerical visual features explain neural numerosity responses?

PubMed

Harvey, Ben M; Dumoulin, Serge O

2017-04-01

Humans and many animals can distinguish between stimuli that differ in numerosity, the number of objects in a set. Human and macaque parietal lobes contain neurons that respond to changes in stimulus numerosity. However, basic non-numerical visual features can affect neural responses to and perception of numerosity, and visual features often co-vary with numerosity. Therefore, it is debated whether numerosity or co-varying low-level visual features underlie neural and behavioral responses to numerosity. To test the hypothesis that non-numerical visual features underlie neural numerosity responses in a human parietal numerosity map, we analyze responses to a group of numerosity stimulus configurations that have the same numerosity progression but vary considerably in their non-numerical visual features. Using ultra-high-field (7T) fMRI, we measure responses to these stimulus configurations in an area of posterior parietal cortex whose responses are believed to reflect numerosity-selective activity. We describe an fMRI analysis method to distinguish between alternative models of neural response functions, following a population receptive field (pRF) modeling approach. For each stimulus configuration, we first quantify the relationships between numerosity and several non-numerical visual features that have been proposed to underlie performance in numerosity discrimination tasks. We then determine how well responses to these non-numerical visual features predict the observed fMRI responses, and compare this to the predictions of responses to numerosity. We demonstrate that a numerosity response model predicts observed responses more accurately than models of responses to simple non-numerical visual features. As such, neural responses in cognitive processing need not reflect simpler properties of early sensory inputs. Copyright © 2017 Elsevier Inc. All rights reserved.

TU-AB-202-06: Quantitative Evaluation of Deformable Image Registration in MRI-Guided Adaptive Radiation Therapy

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

Mooney, K; Zhao, T; Green, O

Purpose: To assess the performance of the deformable image registration algorithm used for MRI-guided adaptive radiation therapy using image feature analysis. Methods: MR images were collected from five patients treated on the MRIdian (ViewRay, Inc., Oakwood Village, OH), a three head Cobalt-60 therapy machine with an 0.35 T MR system. The images were acquired immediately prior to treatment with a uniform 1.5 mm resolution. Treatment sites were as follows: head/neck, lung, breast, stomach, and bladder. Deformable image registration was performed using the ViewRay software between the first fraction MRI and the final fraction MRI, and the DICE similarity coefficient (DSC)more » for the skin contours was reported. The SIFT and Harris feature detection and matching algorithms identified point features in each image separately, then found matching features in the other image. The target registration error (TRE) was defined as the vector distance between matched features on the two image sets. Each deformation was evaluated based on comparison of average TRE and DSC. Results: Image feature analysis produced between 2000–9500 points for evaluation on the patient images. The average (± standard deviation) TRE for all patients was 3.3 mm (±3.1 mm), and the passing rate of TRE<3 mm was 60% on the images. The head/neck patient had the best average TRE (1.9 mm±2.3 mm) and the best passing rate (80%). The lung patient had the worst average TRE (4.8 mm±3.3 mm) and the worst passing rate (37.2%). DSC was not significantly correlated with either TRE (p=0.63) or passing rate (p=0.55). Conclusions: Feature matching provides a quantitative assessment of deformable image registration, with a large number of data points for analysis. The TRE of matched features can be used to evaluate the registration of many objects throughout the volume, whereas DSC mainly provides a measure of gross overlap. We have a research agreement with ViewRay Inc.« less

Detection of Focal Cortical Dysplasia Lesions in MRI Using Textural Features

NASA Astrophysics Data System (ADS)

Loyek, Christian; Woermann, Friedrich G.; Nattkemper, Tim W.

Focal cortical dysplasia (FCD) is a frequent cause of medically refractory partial epilepsy. The visual identification of FCD lesions on magnetic resonance images (MRI) is a challenging task in standard radiological analysis. Quantitative image analysis which tries to assist in the diagnosis of FCD lesions is an active field of research. In this work we investigate the potential of different texture features, in order to explore to what extent they are suitable for detecting lesional tissue. As a result we can show first promising results based on segmentation and texture classification.

Gadolinium Enhanced MR Coronary Vessel Wall Imaging at 3.0 Tesla.

PubMed

Kelle, Sebastian; Schlendorf, Kelly; Hirsch, Glenn A; Gerstenblith, Gary; Fleck, Eckart; Weiss, Robert G; Stuber, Matthias

2010-10-11

Purpose. We evaluated the influence of the time between low-dose gadolinium (Gd) contrast administration and coronary vessel wall enhancement (LGE) detected by 3T magnetic resonance imaging (MRI) in healthy subjects and patients with coronary artery disease (CAD). Materials and Methods. Four healthy subjects (4 men, mean age 29 ± 3 years and eleven CAD patients (6 women, mean age 61 ± 10 years) were studied on a commercial 3.0 Tesla (T) whole-body MR imaging system (Achieva 3.0 T; Philips, Best, The Netherlands). T1-weighted inversion-recovery coronary magnetic resonance imaging (MRI) was repeated up to 75 minutes after administration of low-dose Gadolinium (Gd) (0.1 mmol/kg Gd-DTPA). Results. LGE was seen in none of the healthy subjects, however in all of the CAD patients. In CAD patients, fifty-six of 62 (90.3%) segments showed LGE of the coronary artery vessel wall at time-interval 1 after contrast. At time-interval 2, 34 of 42 (81.0%) and at time-interval 3, 29 of 39 evaluable segments (74.4%) were enhanced. Conclusion. In this work, we demonstrate LGE of the coronary artery vessel wall using 3.0 T MRI after a single, low-dose Gd contrast injection in CAD patients but not in healthy subjects. In the majority of the evaluated coronary segments in CAD patients, LGE of the coronary vessel wall was already detectable 30-45 minutes after administration of the contrast agent.

Pattern Recognition Approaches for Breast Cancer DCE-MRI Classification: A Systematic Review.

PubMed

Fusco, Roberta; Sansone, Mario; Filice, Salvatore; Carone, Guglielmo; Amato, Daniela Maria; Sansone, Carlo; Petrillo, Antonella

2016-01-01

We performed a systematic review of several pattern analysis approaches for classifying breast lesions using dynamic, morphological, and textural features in dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). Several machine learning approaches, namely artificial neural networks (ANN), support vector machines (SVM), linear discriminant analysis (LDA), tree-based classifiers (TC), and Bayesian classifiers (BC), and features used for classification are described. The findings of a systematic review of 26 studies are presented. The sensitivity and specificity are respectively 91 and 83 % for ANN, 85 and 82 % for SVM, 96 and 85 % for LDA, 92 and 87 % for TC, and 82 and 85 % for BC. The sensitivity and specificity are respectively 82 and 74 % for dynamic features, 93 and 60 % for morphological features, 88 and 81 % for textural features, 95 and 86 % for a combination of dynamic and morphological features, and 88 and 84 % for a combination of dynamic, morphological, and other features. LDA and TC have the best performance. A combination of dynamic and morphological features gives the best performance.

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.

Using Wikipedia to learn semantic feature representations of concrete concepts in neuroimaging experiments

PubMed Central

Pereira, Francisco; Botvinick, Matthew; Detre, Greg

2012-01-01

In this paper we show that a corpus of a few thousand Wikipedia articles about concrete or visualizable concepts can be used to produce a low-dimensional semantic feature representation of those concepts. The purpose of such a representation is to serve as a model of the mental context of a subject during functional magnetic resonance imaging (fMRI) experiments. A recent study [19] showed that it was possible to predict fMRI data acquired while subjects thought about a concrete concept, given a representation of those concepts in terms of semantic features obtained with human supervision. We use topic models on our corpus to learn semantic features from text in an unsupervised manner, and show that those features can outperform those in [19] in demanding 12-way and 60-way classification tasks. We also show that these features can be used to uncover similarity relations in brain activation for different concepts which parallel those relations in behavioral data from human subjects. PMID:23243317

SU-F-R-32: Evaluation of MRI Acquisition Parameter Variations On Texture Feature Extraction Using ACR Phantom

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

Xie, Y; Wang, J; Wang, C

Purpose: To investigate the sensitivity of classic texture features to variations of MRI acquisition parameters. Methods: This study was performed on American College of Radiology (ACR) MRI Accreditation Program Phantom. MR imaging was acquired on a GE 750 3T scanner with XRM explain gradient, employing a T1-weighted images (TR/TE=500/20ms) with the following parameters as the reference standard: number of signal average (NEX) = 1, matrix size = 256×256, flip angle = 90°, slice thickness = 5mm. The effect of the acquisition parameters on texture features with and without non-uniformity correction were investigated respectively, while all the other parameters were keptmore » as reference standard. Protocol parameters were set as follows: (a). NEX = 0.5, 2 and 4; (b).Phase encoding steps = 128, 160 and 192; (c). Matrix size = 128×128, 192×192 and 512×512. 32 classic texture features were generated using the classic gray level run length matrix (GLRLM) and gray level co-occurrence matrix (GLCOM) from each image data set. Normalized range ((maximum-minimum)/mean) was calculated to determine variation among the scans with different protocol parameters. Results: For different NEX, 31 out of 32 texture features’ range are within 10%. For different phase encoding steps, 31 out of 32 texture features’ range are within 10%. For different acquisition matrix size without non-uniformity correction, 14 out of 32 texture features’ range are within 10%; for different acquisition matrix size with non-uniformity correction, 16 out of 32 texture features’ range are within 10%. Conclusion: Initial results indicated that those texture features that range within 10% are less sensitive to variations in T1-weighted MRI acquisition parameters. This might suggest that certain texture features might be more reliable to be used as potential biomarkers in MR quantitative image analysis.« less

SU-D-207B-04: Morphological Features of MRI as a Correlate of Capsular Contracture in Breast Cancer Patients with Implant-Based Reconstructions

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

Tyagi, N; Sutton, E; Hunt, M

Purpose: Capsular contracture (CC) is a serious complication in patients receiving implant-based reconstruction for breast cancer. The goal of this study was to identify image-based correlates of CC using MRI imaging in breast cancer patients who received both MRI and clinical evaluation following reconstructive surgery. Methods: We analyzed a retrospective dataset of 50 patients who had both a diagnostic MR and a plastic surgeon’s evaluations of CC score (Baker’s score) within a six month period following mastectomy and reconstructive surgery. T2w sagittal MRIs (TR/TE = 3500/102 ms, slice thickness = 4 mm) were used for morphological shape features (roundness, eccentricity,more » solidity, extent and ratio-length) and histogram features (median, skewness and kurtosis) of the implant and the pectoralis muscle overlying the implant. Implant and pectoralis muscles were segmented in 3D using Computation Environment for Radiological Research (CERR) and shape and histogram features were calculated as a function of Baker’s score. Results: Shape features such as roundness and eccentricity were statistically significant in differentiating grade 1 and grade 2 (p = 0.009; p = 0.06) as well as grade 1 and grade 3 CC (p = 0.001; p = 0.006). Solidity and extent were statistically significant in differentiating grade 1 and grade 3 CC (p = 0.04; p = 0.04). Ratio-length was statistically significant in differentiating all grades of CC except grade 2 and grade 3 that showed borderline significance (p = 0.06). The muscle thickness, median intensity and kurtosis were significant in differentiating between grade 1 and grade 3 (p = 0.02), grade 1 and grade 2 (p = 0.03) and grade 1 and grade 3 (p = 0.01) respectively. Conclusion: Morphological shape features described on MR images were associated with the severity of CC. MRI may be important in objectively evaluating outcomes in breast cancer patients who undergo implant reconstruction.« less

A Hierarchical Feature and Sample Selection Framework and Its Application for Alzheimer’s Disease Diagnosis

NASA Astrophysics Data System (ADS)

An, Le; Adeli, Ehsan; Liu, Mingxia; Zhang, Jun; Lee, Seong-Whan; Shen, Dinggang

2017-03-01

Classification is one of the most important tasks in machine learning. Due to feature redundancy or outliers in samples, using all available data for training a classifier may be suboptimal. For example, the Alzheimer’s disease (AD) is correlated with certain brain regions or single nucleotide polymorphisms (SNPs), and identification of relevant features is critical for computer-aided diagnosis. Many existing methods first select features from structural magnetic resonance imaging (MRI) or SNPs and then use those features to build the classifier. However, with the presence of many redundant features, the most discriminative features are difficult to be identified in a single step. Thus, we formulate a hierarchical feature and sample selection framework to gradually select informative features and discard ambiguous samples in multiple steps for improved classifier learning. To positively guide the data manifold preservation process, we utilize both labeled and unlabeled data during training, making our method semi-supervised. For validation, we conduct experiments on AD diagnosis by selecting mutually informative features from both MRI and SNP, and using the most discriminative samples for training. The superior classification results demonstrate the effectiveness of our approach, as compared with the rivals.

Effects of Spatial and Feature Attention on Disparity-Rendered Structure-From-Motion Stimuli in the Human Visual Cortex

PubMed Central

Ip, Ifan Betina; Bridge, Holly; Parker, Andrew J.

2014-01-01

An important advance in the study of visual attention has been the identification of a non-spatial component of attention that enhances the response to similar features or objects across the visual field. Here we test whether this non-spatial component can co-select individual features that are perceptually bound into a coherent object. We combined human psychophysics and functional magnetic resonance imaging (fMRI) to demonstrate the ability to co-select individual features from perceptually coherent objects. Our study used binocular disparity and visual motion to define disparity structure-from-motion (dSFM) stimuli. Although the spatial attention system induced strong modulations of the fMRI response in visual regions, the non-spatial system’s ability to co-select features of the dSFM stimulus was less pronounced and variable across subjects. Our results demonstrate that feature and global feature attention effects are variable across participants, suggesting that the feature attention system may be limited in its ability to automatically select features within the attended object. Careful comparison of the task design suggests that even minor differences in the perceptual task may be critical in revealing the presence of global feature attention. PMID:24936974

Use of MRI in Differentiation of Papillary Renal Cell Carcinoma Subtypes: Qualitative and Quantitative Analysis.

PubMed

Doshi, Ankur M; Ream, Justin M; Kierans, Andrea S; Bilbily, Matthew; Rusinek, Henry; Huang, William C; Chandarana, Hersh

2016-03-01

The purpose of this study was to determine whether qualitative and quantitative MRI feature analysis is useful for differentiating type 1 from type 2 papillary renal cell carcinoma (PRCC). This retrospective study included 21 type 1 and 17 type 2 PRCCs evaluated with preoperative MRI. Two radiologists independently evaluated various qualitative features, including signal intensity, heterogeneity, and margin. For the quantitative analysis, a radiology fellow and a medical student independently drew 3D volumes of interest over the entire tumor on T2-weighted HASTE images, apparent diffusion coefficient parametric maps, and nephrographic phase contrast-enhanced MR images to derive first-order texture metrics. Qualitative and quantitative features were compared between the groups. For both readers, qualitative features with greater frequency in type 2 PRCC included heterogeneous enhancement, indistinct margin, and T2 heterogeneity (all, p < 0.035). Indistinct margins and heterogeneous enhancement were independent predictors (AUC, 0.822). Quantitative analysis revealed that apparent diffusion coefficient, HASTE, and contrast-enhanced entropy were greater in type 2 PRCC (p < 0.05; AUC, 0.682-0.716). A combined quantitative and qualitative model had an AUC of 0.859. Qualitative features within the model had interreader concordance of 84-95%, and the quantitative data had intraclass coefficients of 0.873-0.961. Qualitative and quantitative features can help discriminate between type 1 and type 2 PRCC. Quantitative analysis may capture useful information that complements the qualitative appearance while benefiting from high interobserver agreement.

Breast cancer Ki67 expression preoperative discrimination by DCE-MRI radiomics features

NASA Astrophysics Data System (ADS)

Ma, Wenjuan; Ji, Yu; Qin, Zhuanping; Guo, Xinpeng; Jian, Xiqi; Liu, Peifang

2018-02-01

To investigate whether quantitative radiomics features extracted from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) are associated with Ki67 expression of breast cancer. In this institutional review board approved retrospective study, we collected 377 cases Chinese women who were diagnosed with invasive breast cancer in 2015. This cohort included 53 low-Ki67 expression (Ki67 proliferation index less than 14%) and 324 cases with high-Ki67 expression (Ki67 proliferation index more than 14%). A binary-classification of low- vs. high- Ki67 expression was performed. A set of 52 quantitative radiomics features, including morphological, gray scale statistic, and texture features, were extracted from the segmented lesion area. Three most common machine learning classification methods, including Naive Bayes, k-Nearest Neighbor and support vector machine with Gaussian kernel, were employed for the classification and the least absolute shrink age and selection operator (LASSO) method was used to select most predictive features set for the classifiers. Classification performance was evaluated by the area under receiver operating characteristic curve (AUC), accuracy, sensitivity and specificity. The model that used Naive Bayes classification method achieved the best performance than the other two methods, yielding 0.773 AUC value, 0.757 accuracy, 0.777 sensitivity and 0.769 specificity. Our study showed that quantitative radiomics imaging features of breast tumor extracted from DCE-MRI are associated with breast cancer Ki67 expression. Future larger studies are needed in order to further evaluate the findings.

Role of MRI in the early diagnosis of tubal ectopic pregnancy.

PubMed

Si, Ming-Jue; Gui, Shuang; Fan, Qin; Han, Hong-Xiu; Zhao, Qian-Qian; Li, Zhi-Xin; Zhao, Jiang-Min

2016-07-01

To determine the role of MRI in the early diagnosis of tubal ectopic pregnancy (EP). Clinical and MRI features of 27 cases of tubal pregnancy were reviewed. A thick-walled gestational sac (GS)-like structure was demonstrated lateral to the uterus in all cases. On T2-weighted images, the thick wall typically exhibited 3 discrete rings in 22 cases (81 %), among which 17 cases (63 %) displayed small vessels and 6 cases (33 %) exhibited small areas of fresh haemorrhage inside the thick wall. The contents demonstrated non-specific liquid in 26 %, papillary solid components in 56 %, and fresh blood or fluid-fluid level in 19 % of the cases. Dilatation of the affected fallopian tube associated with hematosalpinx was demonstrated in 18 cases (67 %) and marked enhancement of the tubal wall was observed in 22 cases (81 %). No correlation was found between the size of the GS and the estimated gestational age (r = 0.056). MRI plays an important role in the early diagnosis and management of tubal pregnancy. The characteristic MRI features include a GS-like structure with a "three rings" appearance on T2-weighted images, presence of solid components in the sac, dilatation of the affected fallopian tube with hematosalpinx, and tubal wall enhancement. • MR imaging has served as a problem-solving procedure in ectopic pregnancy. • MR imaging features can be criteria for early diagnosis of tubal pregnancy. • Detailed assessment of ectopic implantation is necessary for management decision-making.

Magnetic resonance imaging: a useful tool to distinguish between keratocystic odontogenic tumours and odontogenic cysts.

PubMed

Probst, F A; Probst, M; Pautke, Ch; Kaltsi, E; Otto, S; Schiel, S; Troeltzsch, M; Ehrenfeld, M; Cornelius, C P; Müller-Lisse, U G

2015-03-01

In contrast to odontogenic cysts, keratocystic odontogenic tumours often recur and require more aggressive surgical treatment, so we tried to find features that distinguished between them on magnetic resonance imaging (MRI). Without knowing the diagnosis, two radiologists reviewed intensity (low, intermediate, or high) and homogeneity (homogeneous or heterogeneous) of signals in short-tau-inversion-recovery (STIR), T1- and T2-weighted, and fat-suppressed, contrast-enhanced MRI in 20 consecutive patients with oval, radiolucent lesions of the mandible on panoramic radiography, and who were subsequently confirmed histopathologically to have either an odontogenic cyst or a keratocystic odontogenic tumour (n=10 in each group). Fisher's exact test was statistically significant at p<0.05. Delineation of a contrast-enhanced wall of a cyst with high signal intensity distinguished odontogenic cysts (9/10 and 8/10, respectively) from keratocystic odontogenic tumours (3/10, p=0.02, and 1/10, p=0.01, respectively). One radiologist found odontogenic cysts were more likely to be homogeneous on unenhanced T1-weighted images (odontogenic cysts 9/10, keratocystic odontogenic tumours 3/10, p=0.02) and one on contrast-enhanced MRI, when the cyst wall was enhanced (odontogenic cysts 7/9, keratocystic odontogenic tumours 0/3, p=0.01). There were no other significant distinguishing features on MRI. In conclusion, the signal intensity of the enhanced wall seems to be a feature on contrast-enhanced MRI that differentiates odontogenic cysts from keratocystic odontogenic tumours. Copyright © 2014 The British Association of Oral and Maxillofacial Surgeons. Published by Elsevier Ltd. All rights reserved.

Prediction of response to neoadjuvant chemotherapy in breast cancer: a radiomic study

NASA Astrophysics Data System (ADS)

Wu, Guolin; Fan, Ming; Zhang, Juan; Zheng, Bin; Li, Lihua

2017-03-01

Breast cancer is one of the most malignancies among women in worldwide. Neoadjuvant Chemotherapy (NACT) has gained interest and is increasingly used in treatment of breast cancer in recent years. Therefore, it is necessary to find a reliable non-invasive assessment and prediction method which can evaluate and predict the response of NACT. Recent studies have highlighted the use of MRI for predicting response to NACT. In addition, molecular subtype could also effectively identify patients who are likely have better prognosis in breast cancer. In this study, a radiomic analysis were performed, by extracting features from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and immunohistochemistry (IHC) to determine subtypes. A dataset with fifty-seven breast cancer patients were included, all of them received preoperative MRI examination. Among them, 47 patients had complete response (CR) or partial response (PR) and 10 had stable disease (SD) to chemotherapy based on the RECIST criterion. A total of 216 imaging features including statistical characteristics, morphology, texture and dynamic enhancement were extracted from DCE-MRI. In multivariate analysis, the proposed imaging predictors achieved an AUC of 0.923 (P = 0.0002) in leave-one-out crossvalidation. The performance of the classifier increased to 0.960, 0.950 and 0.936 when status of HER2, Luminal A and Luminal B subtypes were added into the statistic model, respectively. The results of this study demonstrated that IHC determined molecular status combined with radiomic features from DCE-MRI could be used as clinical marker that is associated with response to NACT.

Association between MRI structural features and cognitive measures in pediatric multiple sclerosis

NASA Astrophysics Data System (ADS)

Amoroso, N.; Bellotti, R.; Fanizzi, A.; Lombardi, A.; Monaco, A.; Liguori, M.; Margari, L.; Simone, M.; Viterbo, R. G.; Tangaro, S.

2017-09-01

Multiple sclerosis (MS) is an inflammatory and demyelinating disease associated with neurodegenerative processes that lead to brain structural changes. The disease affects mostly young adults, but 3-5% of cases has a pediatric onset (POMS). Magnetic Resonance Imaging (MRI) is generally used for diagnosis and follow-up in MS patients, however the most common MRI measures (e.g. new or enlarging T2-weighted lesions, T1-weighted gadolinium- enhancing lesions) have often failed as surrogate markers of MS disability and progression. MS is clinically heterogenous with symptoms that can include both physical changes (such as visual loss or walking difficulties) and cognitive impairment. 30-50% of POMS experience prominent cognitive dysfunction. In order to investigate the association between cognitive measures and brain morphometry, in this work we present a fully automated pipeline for processing and analyzing MRI brain scans. Relevant anatomical structures are segmented with FreeSurfer; besides, statistical features are computed. Thus, we describe the data referred to 12 patients with early POMS (mean age at MRI: 15.5 +/- 2.7 years) with a set of 181 structural features. The major cognitive abilities measured are verbal and visuo-spatial learning, expressive language and complex attention. Data was collected at the Department of Basic Sciences, Neurosciences and Sense Organs, University of Bari, and exploring different abilities like the verbal and visuo-spatial learning, expressive language and complex attention. Different regression models and parameter configurations are explored to assess the robustness of the results, in particular Generalized Linear Models, Bayes Regression, Random Forests, Support Vector Regression and Artificial Neural Networks are discussed.

Performance of an Automated Versus a Manual Whole-Body Magnetic Resonance Imaging Workflow.

PubMed

Stocker, Daniel; Finkenstaedt, Tim; Kuehn, Bernd; Nanz, Daniel; Klarhoefer, Markus; Guggenberger, Roman; Andreisek, Gustav; Kiefer, Berthold; Reiner, Caecilia S

2018-04-24

The aim of this study was to evaluate the performance of an automated workflow for whole-body magnetic resonance imaging (WB-MRI), which reduces user interaction compared with the manual WB-MRI workflow. This prospective study was approved by the local ethics committee. Twenty patients underwent WB-MRI for myopathy evaluation on a 3 T MRI scanner. Ten patients (7 women; age, 52 ± 13 years; body weight, 69.9 ± 13.3 kg; height, 173 ± 9.3 cm; body mass index, 23.2 ± 3.0) were examined with a prototypical automated WB-MRI workflow, which automatically segments the whole body, and 10 patients (6 women; age, 35.9 ± 12.4 years; body weight, 72 ± 21 kg; height, 169.2 ± 10.4 cm; body mass index, 24.9 ± 5.6) with a manual scan. Overall image quality (IQ; 5-point scale: 5, excellent; 1, poor) and coverage of the study volume were assessed by 2 readers for each sequence (coronal T2-weighted turbo inversion recovery magnitude [TIRM] and axial contrast-enhanced T1-weighted [ce-T1w] gradient dual-echo sequence). Interreader agreement was evaluated with intraclass correlation coefficients. Examination time, number of user interactions, and MR technicians' acceptance rating (1, highest; 10, lowest) was compared between both groups. Total examination time was significantly shorter for automated WB-MRI workflow versus manual WB-MRI workflow (30.0 ± 4.2 vs 41.5 ± 3.4 minutes, P < 0.0001) with significantly shorter planning time (2.5 ± 0.8 vs 14.0 ± 7.0 minutes, P < 0.0001). Planning took 8% of the total examination time with automated versus 34% with manual WB-MRI workflow (P < 0.0001). The number of user interactions with automated WB-MRI workflow was significantly lower compared with manual WB-MRI workflow (10.2 ± 4.4 vs 48.2 ± 17.2, P < 0.0001). Planning efforts were rated significantly lower by the MR technicians for the automated WB-MRI workflow than for the manual WB-MRI workflow (2.20 ± 0.92 vs 4.80 ± 2.39, respectively; P = 0.005). Overall IQ was similar between automated and manual WB-MRI workflow (TIRM: 4.00 ± 0.94 vs 3.45 ± 1.19, P = 0.264; ce-T1w: 4.20 ± 0.88 vs 4.55 ± .55, P = 0.423). Interreader agreement for overall IQ was excellent for TIRM and ce-T1w with an intraclass correlation coefficient of 0.95 (95% confidence interval, 0.86-0.98) and 0.88 (95% confidence interval, 0.70-0.95). Incomplete coverage of the thoracic compartment in the ce-T1w sequence occurred more often in the automated WB-MRI workflow (P = 0.008) for reader 2. No other significant differences in the study volume coverage were found. In conclusion, the automated WB-MRI scanner workflow showed a significant reduction of the examination time and the user interaction compared with the manual WB-MRI workflow. Image quality and the coverage of the study volume were comparable in both groups.

Radiogenomic analysis of hypoxia pathway reveals computerized MRI descriptors predictive of overall survival in glioblastoma

NASA Astrophysics Data System (ADS)

Beig, Niha; Patel, Jay; Prasanna, Prateek; Partovi, Sasan; Varadan, Vinay; Madabhushi, Anant; Tiwari, Pallavi

2017-03-01

Glioblastoma Multiforme (GBM) is a highly aggressive brain tumor with a median survival of 14 months. Hypoxia is a hallmark trait in GBM that is known to be associated with angiogenesis, tumor growth, and resistance to conventional therapy, thereby limiting treatment options for GBM patients. There is thus an urgent clinical need for non-invasively capturing tumor hypoxia in GBM towards identifying a subset of patients who would likely benefit from anti-angiogenic therapies (bevacizumab) in the adjuvant setting. In this study, we employed radiomic descriptors to (a) capture molecular variations of tumor hypoxia on routine MRI that are otherwise not appreciable; and (b) employ the radiomic correlates of hypoxia to discriminate patients with short-term survival (STS, overall survival (OS) < 7 months), mid-term survival (MTS) (7 months16 months). A total of 97 studies (25 STS, 36 MTS, 36 LTS) with Gadolinium T1-contrast (Gd-T1c), T2w, and FLAIR protocols with their corresponding gene expression profiles were obtained from the cancer genome atlas (TCGA) database. For each MRI study, necrotic, enhancing tumor, and edematous regions were segmented by an expert. A total of 30 radiomic descriptors (i.e. Haralick, Laws energy, Gabor) were extracted from every region across all three MRI protocols. By performing unsupervised clustering of the expression profile of hypoxia associated genes, a "low", "medium", or "high" index was defined for every study. Spearman correlation was then used to identify the most significantly correlated MRI features with the hypoxia index for every study. These features were further used to categorize each study as STS, MTS, and LTS using Kaplan-Meier (KM) analysis. Our results revealed that the most significant features (p < 0.05) were identified as Laws energy and Haralick features that capture image heterogeneity on FLAIR and Gd-T1w sequences. We also found these radiomic features to be significantly associated with survival, distinguishing MTS from LTS (p=.005) and STS from LTS (p=.0008).

Key issues in decomposing fMRI during naturalistic and continuous music experience with independent component analysis.

PubMed

Cong, Fengyu; Puoliväli, Tuomas; Alluri, Vinoo; Sipola, Tuomo; Burunat, Iballa; Toiviainen, Petri; Nandi, Asoke K; Brattico, Elvira; Ristaniemi, Tapani

2014-02-15

Independent component analysis (ICA) has been often used to decompose fMRI data mostly for the resting-state, block and event-related designs due to its outstanding advantage. For fMRI data during free-listening experiences, only a few exploratory studies applied ICA. For processing the fMRI data elicited by 512-s modern tango, a FFT based band-pass filter was used to further pre-process the fMRI data to remove sources of no interest and noise. Then, a fast model order selection method was applied to estimate the number of sources. Next, both individual ICA and group ICA were performed. Subsequently, ICA components whose temporal courses were significantly correlated with musical features were selected. Finally, for individual ICA, common components across majority of participants were found by diffusion map and spectral clustering. The extracted spatial maps (by the new ICA approach) common across most participants evidenced slightly right-lateralized activity within and surrounding the auditory cortices. Meanwhile, they were found associated with the musical features. Compared with the conventional ICA approach, more participants were found to have the common spatial maps extracted by the new ICA approach. Conventional model order selection methods underestimated the true number of sources in the conventionally pre-processed fMRI data for the individual ICA. Pre-processing the fMRI data by using a reasonable band-pass digital filter can greatly benefit the following model order selection and ICA with fMRI data by naturalistic paradigms. Diffusion map and spectral clustering are straightforward tools to find common ICA spatial maps. Copyright © 2013 Elsevier B.V. All rights reserved.

A computerized MRI biomarker quantification scheme for a canine model of Duchenne muscular dystrophy

PubMed Central

Wang, Jiahui; Fan, Zheng; Vandenborne, Krista; Walter, Glenn; Shiloh-Malawsky, Yael; An, Hongyu; Kornegay, Joe N.; Styner, Martin A.

2015-01-01

Purpose Golden retriever muscular dystrophy (GRMD) is a widely used canine model of Duchenne muscular dystrophy (DMD). Recent studies have shown that magnetic resonance imaging (MRI) can be used to non-invasively detect consistent changes in both DMD and GRMD. In this paper, we propose a semi-automated system to quantify MRI biomarkers of GRMD. Methods Our system was applied to a database of 45 MRI scans from 8 normal and 10 GRMD dogs in a longitudinal natural history study. We first segmented six proximal pelvic limb muscles using two competing schemes: 1) standard, limited muscle range segmentation and 2) semi-automatic full muscle segmentation. We then performed pre-processing, including: intensity inhomogeneity correction, spatial registration of different image sequences, intensity calibration of T2-weighted (T2w) and T2-weighted fat suppressed (T2fs) images, and calculation of MRI biomarker maps. Finally, for each of the segmented muscles, we automatically measured MRI biomarkers of muscle volume and intensity statistics over MRI biomarker maps, and statistical image texture features. Results The muscle volume and the mean intensities in T2 value, fat, and water maps showed group differences between normal and GRMD dogs. For the statistical texture biomarkers, both the histogram and run-length matrix features showed obvious group differences between normal and GRMD dogs. The full muscle segmentation shows significantly less error and variability in the proposed biomarkers when compared to the standard, limited muscle range segmentation. Conclusion The experimental results demonstrated that this quantification tool can reliably quantify MRI biomarkers in GRMD dogs, suggesting that it would also be useful for quantifying disease progression and measuring therapeutic effect in DMD patients. PMID:23299128

IDH mutation assessment of glioma using texture features of multimodal MR images

NASA Astrophysics Data System (ADS)

Zhang, Xi; Tian, Qiang; Wu, Yu-Xia; Xu, Xiao-Pan; Li, Bao-Juan; Liu, Yi-Xiong; Liu, Yang; Lu, Hong-Bing

2017-03-01

Purpose: To 1) find effective texture features from multimodal MRI that can distinguish IDH mutant and wild status, and 2) propose a radiomic strategy for preoperatively detecting IDH mutation patients with glioma. Materials and Methods: 152 patients with glioma were retrospectively included from the Cancer Genome Atlas. Corresponding T1-weighted image before- and post-contrast, T2-weighted image and fluid-attenuation inversion recovery image from the Cancer Imaging Archive were analyzed. Specific statistical tests were applied to analyze the different kind of baseline information of LrGG patients. Finally, 168 texture features were derived from multimodal MRI per patient. Then the support vector machine-based recursive feature elimination (SVM-RFE) and classification strategy was adopted to find the optimal feature subset and build the identification models for detecting the IDH mutation. Results: Among 152 patients, 92 and 60 were confirmed to be IDH-wild and mutant, respectively. Statistical analysis showed that the patients without IDH mutation was significant older than patients with IDH mutation (p<0.01), and the distribution of some histological subtypes was significant different between IDH wild and mutant groups (p<0.01). After SVM-RFE, 15 optimal features were determined for IDH mutation detection. The accuracy, sensitivity, specificity, and AUC after SVM-RFE and parameter optimization were 82.2%, 85.0%, 78.3%, and 0.841, respectively. Conclusion: This study presented a radiomic strategy for noninvasively discriminating IDH mutation of patients with glioma. It effectively incorporated kinds of texture features from multimodal MRI, and SVM-based classification strategy. Results suggested that features selected from SVM-RFE were more potential to identifying IDH mutation. The proposed radiomics strategy could facilitate the clinical decision making in patients with glioma.

Preliminary experience using dynamic MRI at 3.0 Tesla for evaluation of soft tissue tumors.

PubMed

Park, Michael Yong; Jee, Won-Hee; Kim, Sun Ki; Lee, So-Yeon; Jung, Joon-Yong

2013-01-01

We aimed to evaluate the use of dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) at 3.0 T for differentiating the benign from malignant soft tissue tumors. Also we aimed to assess whether the shorter length of DCE-MRI protocols are adequate, and to evaluate the effect of temporal resolution. Dynamic contrast-enhanced magnetic resonance imaging, at 3.0 T with a 1 second temporal resolution in 13 patients with pathologically confirmed soft tissue tumors, was analyzed. Visual assessment of time-signal curves, subtraction images, maximal relative enhancement at the first (maximal peak enhancement [Emax]/1) and second (Emax/2) minutes, Emax, steepest slope calculated by using various time intervals (5, 30, 60 seconds), and the start of dynamic enhancement were analyzed. The 13 tumors were comprised of seven benign and six malignant soft tissue neoplasms. Washout on time-signal curves was seen on three (50%) malignant tumors and one (14%) benign one. The most discriminating DCE-MRI parameter was the steepest slope calculated, by using at 5-second intervals, followed by Emax/1 and Emax/2. All of the steepest slope values occurred within 2 minutes of the dynamic study. Start of dynamic enhancement did not show a significant difference, but no malignant tumor rendered a value greater than 14 seconds. The steepest slope and early relative enhancement have the potential for differentiating benign from malignant soft tissue tumors. Short-length rather than long-length DCE-MRI protocol may be adequate for our purpose. The steepest slope parameters require a short temporal resolution, while maximal peak enhancement parameter may be more optimal for a longer temporal resolution.

Focal liver lesions segmentation and classification in nonenhanced T2-weighted MRI.

PubMed

Gatos, Ilias; Tsantis, Stavros; Karamesini, Maria; Spiliopoulos, Stavros; Karnabatidis, Dimitris; Hazle, John D; Kagadis, George C

2017-07-01

To automatically segment and classify focal liver lesions (FLLs) on nonenhanced T2-weighted magnetic resonance imaging (MRI) scans using a computer-aided diagnosis (CAD) algorithm. 71 FLLs (30 benign lesions, 19 hepatocellular carcinomas, and 22 metastases) on T2-weighted MRI scans were delineated by the proposed CAD scheme. The FLL segmentation procedure involved wavelet multiscale analysis to extract accurate edge information and mean intensity values for consecutive edges computed using horizontal and vertical analysis that were fed into the subsequent fuzzy C-means algorithm for final FLL border extraction. Texture information for each extracted lesion was derived using 42 first- and second-order textural features from grayscale value histogram, co-occurrence, and run-length matrices. Twelve morphological features were also extracted to capture any shape differentiation between classes. Feature selection was performed with stepwise multilinear regression analysis that led to a reduced feature subset. A multiclass Probabilistic Neural Network (PNN) classifier was then designed and used for lesion classification. PNN model evaluation was performed using the leave-one-out (LOO) method and receiver operating characteristic (ROC) curve analysis. The mean overlap between the automatically segmented FLLs and the manual segmentations performed by radiologists was 0.91 ± 0.12. The highest classification accuracies in the PNN model for the benign, hepatocellular carcinoma, and metastatic FLLs were 94.1%, 91.4%, and 94.1%, respectively, with sensitivity/specificity values of 90%/97.3%, 89.5%/92.2%, and 90.9%/95.6% respectively. The overall classification accuracy for the proposed system was 90.1%. Our diagnostic system using sophisticated FLL segmentation and classification algorithms is a powerful tool for routine clinical MRI-based liver evaluation and can be a supplement to contrast-enhanced MRI to prevent unnecessary invasive procedures. © 2017 American Association of Physicists in Medicine.

A feature-based approach to combine functional MRI, structural MRI and EEG brain imaging data.

PubMed

Calhoun, V; Adali, T; Liu, J

2006-01-01

The acquisition of multiple brain imaging types for a given study is a very common practice. However these data are typically examined in separate analyses, rather than in a combined model. We propose a novel methodology to perform joint independent component analysis across image modalities, including structural MRI data, functional MRI activation data and EEG data, and to visualize the results via a joint histogram visualization technique. Evaluation of which combination of fused data is most useful is determined by using the Kullback-Leibler divergence. We demonstrate our method on a data set composed of functional MRI data from two tasks, structural MRI data, and EEG data collected on patients with schizophrenia and healthy controls. We show that combining data types can improve our ability to distinguish differences between groups.

CT and MR imaging features in phosphaturic mesenchymal tumor-mixed connective tissue: A case report

PubMed Central

Shi, Zhenshan; Deng, Yiqiong; Li, Xiumei; Li, Yueming; Cao, Dairong; Coossa, Vikash Sahadeo

2018-01-01

Phosphaturic mesenchymal tumor-mixed connective tissue (PMT-MCT) is rare and usually benign and slow-growing. The majority of these tumors is associated with sporadic tumor-induced osteomalacia (TIO) or rickets, affect middle-aged individuals and are located in the extremities. Previous imaging studies often focused on seeking the causative tumors of TIO, not on the radiological features of these tumors, especially magnetic resonance imaging (MRI) features. PMT-MCT remains a largely misdiagnosed, ignored or unknown entity by most radiologists and clinicians. In the present case report, a review of the known literature of PMT-MCT was conducted and the CT and MRI findings from three patient cases were described for diagnosing the small subcutaneous tumor. Typical MRI appearances of PMT-MCT were isointense relative to the muscles on T1-weighted imaging, and markedly hyperintense on T2-weighted imaging containing variably flow voids, with markedly heterogeneous/homogenous enhancement on post contrast T1-weighted fat-suppression imaging. Short time inversion recovery was demonstrated to be the optimal sequence in localizing the tumor. PMID:29552133

CT and MR imaging features in phosphaturic mesenchymal tumor-mixed connective tissue: A case report.

PubMed

Shi, Zhenshan; Deng, Yiqiong; Li, Xiumei; Li, Yueming; Cao, Dairong; Coossa, Vikash Sahadeo

2018-04-01

Phosphaturic mesenchymal tumor-mixed connective tissue (PMT-MCT) is rare and usually benign and slow-growing. The majority of these tumors is associated with sporadic tumor-induced osteomalacia (TIO) or rickets, affect middle-aged individuals and are located in the extremities. Previous imaging studies often focused on seeking the causative tumors of TIO, not on the radiological features of these tumors, especially magnetic resonance imaging (MRI) features. PMT-MCT remains a largely misdiagnosed, ignored or unknown entity by most radiologists and clinicians. In the present case report, a review of the known literature of PMT-MCT was conducted and the CT and MRI findings from three patient cases were described for diagnosing the small subcutaneous tumor. Typical MRI appearances of PMT-MCT were isointense relative to the muscles on T1-weighted imaging, and markedly hyperintense on T2-weighted imaging containing variably flow voids, with markedly heterogeneous/homogenous enhancement on post contrast T1-weighted fat-suppression imaging. Short time inversion recovery was demonstrated to be the optimal sequence in localizing the tumor.

Neuroimaging features in subacute encephalopathy with seizures in alcoholics (SESA syndrome)

PubMed Central

Drake-Pérez, Marta; de Lucas, Enrique Marco; Lyo, John; Fernández-Torre, José L.

2017-01-01

Purpose To describe the neuroimaging findings in subacute encephalopathy with seizures in alcoholics (SESA syndrome). Methods We reviewed all cases reported previously, as well as 4 patients diagnosed in our center. We included a total of 8 patients. All subjects had clinical and EEG findings compatible with SESA syndrome and at least one MRI study that did not show other underlying condition that could be responsible for the clinical presentation. Results Initial MRI studies revealed the following features: cortical-subcortical areas of increased T2/FLAIR signal and restricted diffusion (6 patients), hyperperfusion (3 patients), atrophy (5 patients), chronic microvascular ischemic changes (4 patients). Follow-up MRI was performed in half of the patients, all showing a resolution of the hyperintense lesions, but developing focal atrophic changes in 75%. Conclusions SESA syndrome should be included among the alcohol-related encephalopathies. Its radiological features include transient cortical-subcortical T2-hyperintense areas with restricted diffusion (overlapping the typical findings in status epilepticus) observed in a patient with atrophy and chronic multifocal vascular lesions. PMID:27391464

Diagnostic imaging features of normal anal sacs in dogs and cats.

PubMed

Jung, Yechan; Jeong, Eunseok; Park, Sangjun; Jeong, Jimo; Choi, Ul Soo; Kim, Min-Su; Kim, Namsoo; Lee, Kichang

2016-09-30

This study was conducted to provide normal reference features for canine and feline anal sacs using ultrasound, low-field magnetic resonance imaging (MRI) and radiograph contrast as diagnostic imaging tools. A total of ten clinically normal beagle dogs and eight clinically normally cats were included. General radiography with contrast, ultrasonography and low-field MRI scans were performed. The visualization of anal sacs, which are located at distinct sites in dogs and cats, is possible with a contrast study on radiography. Most surfaces of the anal sacs tissue, occasionally appearing as a hyperechoic thin line, were surrounded by the hypoechoic external sphincter muscle on ultrasonography. The normal anal sac contents of dogs and cats had variable echogenicity. Signals of anal sac contents on low-field MRI varied in cats and dogs, and contrast medium using T1-weighted images enhanced the anal sac walls more obviously than that on ultrasonography. In conclusion, this study provides the normal features of anal sacs from dogs and cats on diagnostic imaging. Further studies including anal sac evaluation are expected to investigate disease conditions.

Diagnostic imaging features of normal anal sacs in dogs and cats

PubMed Central

Jung, Yechan; Jeong, Eunseok; Park, Sangjun; Jeong, Jimo; Choi, Ul Soo; Kim, Min-Su; Kim, Namsoo

2016-01-01

This study was conducted to provide normal reference features for canine and feline anal sacs using ultrasound, low-field magnetic resonance imaging (MRI) and radiograph contrast as diagnostic imaging tools. A total of ten clinically normal beagle dogs and eight clinically normally cats were included. General radiography with contrast, ultrasonography and low-field MRI scans were performed. The visualization of anal sacs, which are located at distinct sites in dogs and cats, is possible with a contrast study on radiography. Most surfaces of the anal sacs tissue, occasionally appearing as a hyperechoic thin line, were surrounded by the hypoechoic external sphincter muscle on ultrasonography. The normal anal sac contents of dogs and cats had variable echogenicity. Signals of anal sac contents on low-field MRI varied in cats and dogs, and contrast medium using T1-weighted images enhanced the anal sac walls more obviously than that on ultrasonography. In conclusion, this study provides the normal features of anal sacs from dogs and cats on diagnostic imaging. Further studies including anal sac evaluation are expected to investigate disease conditions. PMID:26645338

Advances in diffusion MRI acquisition and processing in the Human Connectome Project

PubMed Central

Sotiropoulos, Stamatios N; Jbabdi, Saad; Xu, Junqian; Andersson, Jesper L; Moeller, Steen; Auerbach, Edward J; Glasser, Matthew F; Hernandez, Moises; Sapiro, Guillermo; Jenkinson, Mark; Feinberg, David A; Yacoub, Essa; Lenglet, Christophe; Ven Essen, David C; Ugurbil, Kamil; Behrens, Timothy EJ

2013-01-01

The Human Connectome Project (HCP) is a collaborative 5-year effort to map human brain connections and their variability in healthy adults. A consortium of HCP investigators will study a population of 1200 healthy adults using multiple imaging modalities, along with extensive behavioral and genetic data. In this overview, we focus on diffusion MRI (dMRI) and the structural connectivity aspect of the project. We present recent advances in acquisition and processing that allow us to obtain very high-quality in-vivo MRI data, while enabling scanning of a very large number of subjects. These advances result from 2 years of intensive efforts in optimising many aspects of data acquisition and processing during the piloting phase of the project. The data quality and methods described here are representative of the datasets and processing pipelines that will be made freely available to the community at quarterly intervals, beginning in 2013. PMID:23702418

Conventional MRI features for predicting the clinical outcome of patients with invasive placenta

PubMed Central

Chen, Ting; Xu, Xiao-Quan; Shi, Hai-Bin; Yang, Zheng-Qiang; Zhou, Xin; Pan, Yi

2017-01-01

PURPOSE We aimed to evaluate whether morphologic magnetic resonance imaging (MRI) features could help to predict the maternal outcome after uterine artery embolization (UAE)-assisted cesarean section (CS) in patients with invasive placenta previa. METHODS We retrospectively reviewed the MRI data of 40 pregnant women who have undergone UAE-assisted cesarean section due to suspected high risk of massive hemorrhage caused by invasive placenta previa. Patients were divided into two groups based on the maternal outcome (good-outcome group: minor hemorrhage and uterus preserved; poor-outcome group: significant hemorrhage or emergency hysterectomy). Morphologic MRI features were compared between the two groups. Multivariate logistic regression analysis was used to identify the most valuable variables, and predictive value of the identified risk factor was determined. RESULTS Low signal intensity bands on T2-weighted imaging (P < 0.001), placenta percreta (P = 0.011), and placental cervical protrusion sign (P = 0.002) were more frequently observed in patients with poor outcome. Low signal intensity bands on T2-weighted imaging was the only significant predictor of poor maternal outcome in multivariate analysis (P = 0.020; odds ratio, 14.79), with 81.3% sensitivity and 84.3% specificity. CONCLUSION Low signal intensity bands on T2-weighted imaging might be a predictor of poor maternal outcome after UAE-assisted cesarean section in patients with invasive placenta previa. PMID:28345524

Disparities in staging prostate magnetic resonance imaging utilization for nonmetastatic prostate cancer patients undergoing definitive radiation therapy.

PubMed

Ajayi, Ayobami; Hwang, Wei-Ting; Vapiwala, Neha; Rosen, Mark; Chapman, Christina H; Both, Stefan; Shah, Meera; Wang, Xingmei; Agawu, Atu; Gabriel, Peter; Christodouleas, John; Tochner, Zelig; Deville, Curtiland

2016-01-01

There is growing evidence supporting incorporating multiparametric (mp) magnetic resonance imaging (MRI) scans into risk stratification, active surveillance, and treatment paradigms for prostate cancer. The purpose of our study was to determine whether demographic disparities exist in staging MRI utilization for prostate cancer patients. An institutional database of 705 nonmetastatic prostate cancer patients treated with radiation therapy from 2005 through 2013 was used to identify patients undergoing versus not undergoing pretreatment diagnostic prostate mpMRI. Uni- and multivariable logistic regression evaluated the relationship of clinical and demographic characteristics with MRI utilization. All demographic variables assessed, except the other race category, were significantly associated with MRI utilization (all P < .05), including age (odds ratio [OR], 0.92), black race (OR, 0.51), poverty (OR, 0.53), closer distance to radiation facility (OR, 1.79), and nonprivate primary insurance (OR, 0.57) on univariable analysis, while clinical stage T3 (OR, 3.37) was the only clinical characteristic. On multivariable analysis stratified by D'Amico risk group, age remained significant across all risk groups, whereas the black versus white racial (OR, 0.21; 95% confidence interval, 0.08-0.55) and nonprivate versus private insurance type (OR, 0.37; 95% confidence interval, 0.16-0.86) disparities persisted in the low-risk group. Clinical stage T3 remained associated in the high-risk group. For race specifically, the percentages of whites, blacks, and others undergoing MRI in the overall cohort and by risk group were, respectively: overall, 80% (343/427), 68% (156/231), and 85% (40/47); low risk, 86%, 56%, and 63%; intermediate risk, 79%, 72%, and 95%; and high risk, 72%, 72%, and 100%. In this urban, academic center cohort, older patients across all risk groups and black or nonprivate insurance patients in the low risk group were less likely to undergo staging prostate MRI scans. Further research should investigate these differences to ensure equitable utilization across all demographic groups considering the burden of prostate cancer disparities.

Is There a Role for MRI in Plantar Heel Pain.

PubMed

Fazal, Muhammad Ali; Tsekes, Demetris; Baloch, Irshad

2018-06-01

There is an increasing trend to investigate plantar heel pain with magnetic resonance imaging (MRI) scan though plantar fasciitis is the most common cause. The purpose of our study was to evaluate the role of MRI in patients presenting with plantar heel pain. Case notes and MRI scans of 141 patients with a clinical diagnosis of plantar fasciitis were reviewed retrospectively. There were 98 females and 43 males patients. Fourteen patients had bilateral symptoms. Average age for male patients was 51 years (range = 26-78 years), and for female patients the average age was 52 years (range = 29-76 years). A total of 121 feet had MRI features suggestive of plantar fasciitis. MRI was normal in 32 feet. There was one case of stress fracture of calcaneus and another of a heel fibroma diagnosed on MRI scan. In our study, MRI scan was normal in 20.7% of the cases; 1.3% had a diagnosis other than plantar fasciitis but no sinister pathology. We therefore conclude that MRI scan is not routinely indicated and key is careful clinical assessment. Therapeutic, Level IV: Retrospective, Case series.

Multicentre randomised controlled trial examining the cost-effectiveness of contrast-enhanced high field magnetic resonance imaging in women with primary breast cancer scheduled for wide local excision (COMICE).

PubMed

Turnbull, L W; Brown, S R; Olivier, C; Harvey, I; Brown, J; Drew, P; Hanby, A; Manca, A; Napp, V; Sculpher, M; Walker, L G; Walker, S

2010-01-01

To determine whether the addition of magnetic resonance imaging (MRI) to current patient evaluation by triple assessment would aid tumour localisation within the breast and thus reduce the reoperation rate in women with primary breast tumours who are scheduled for wide local excision (WLE), and to assess whether the addition of MRI would be cost-effective for the UK NHS. A multicentre, randomised controlled, open, parallel group trial with equal randomisation. The main design was supplemented with a qualitative study to assess patients' experiences of the treatment process and care pathway, and involved the development of a non-scheduled standardised interview (NSSI). The study took place at 45 hospitals throughout the UK. Women aged 18 years or over with biopsy-proven primary breast cancer who had undergone triple assessment, were scheduled for WLE, and were capable of providing written informed consent. Patients were randomised to receive MRI or no MR1. Randomisation was performed using minimisation, incorporating a random element. All MRI was performed at 1.5 T or 1.0 T with a dedicated bilateral breast coil. The primary end point of the trial was the reoperation rate. Secondary outcome measures included discrepancies between imaging and histopathology, and the effectiveness of using both procedures; change in clinical management after using MRI; the clinical significance of MRI-only-detected lesions; the rate of interventions; the ipsilateral tumour recurrence rate; patient quality of life (QoL); and cost-effectiveness. From a total of 1623 patients, 816 were randomised to MRI and 807 to no MRI. No differences in reoperation rates were found between the two groups of patients [MRI patients 18.75%, no MRI 19.33%, difference 0.58%, 95% confidence interval (CI) -3.24 to 4.40]. Therefore, the addition of MRI to conventional triple assessment was not found to be statistically significantly associated with a reduced reoperation rate (odds ratio = 0.96, 95% CI 0.75-1.24, p = 0.7691). The best agreement between all imaging modalities and histopathology with regard to tumour size and extent of disease was found in patients over 50 years old with ductal tumours NST and who were node negative. In the imaging arm, mastectomy was found to be pathologically avoidable for 16 (27.6%) out of 58 patients who underwent the procedure. There were no significant differences between the groups regarding the proportion of patients receiving chemotherapy, radiotherapy or additional adjuvant therapies, as well as for local recurrence-free interval rates and QoL. An acceptable NSSI was developed for use in this population of patients. Economic analysis found no difference in outcomes between the two trial arms. The addition of MRI to triple assessment did not result in a reduction in operation rates, and the use of MRI would thus consume extra resource with few or no benefits in terms of cost-effectiveness or HRQoL. However, MRI showed potential to improve tumour localisation, and preoperative biopsy of MRI-only-detected lesions is likely to minimise the incidence of inappropriate mastectomy. Current Controlled Trials ISRCTN57474502.

MRI correlates of alien leg-like phenomenon in corticobasal degeneration.

PubMed

Hu, William T; Josephs, Keith A; Ahlskog, J Eric; Shin, Cheolsu; Boeve, Bradley F; Witte, Robert J

2005-07-01

We describe the clinical and neuroradiologic correlates in two patients with the clinical picture of CBD and alien leg phenomena. The MRI brain scan in both had unique focal abnormalities in the corresponding leg area of the homunculus that may be the substrate for the alien limb features. Copyright 2005 Movement Disorder Society.

Magnetic resonance imaging (MRI) of oak trees infected with Phytophthora ramorum to determine potential avenues of infection in bark

Treesearch

Edwin R. Florance

2006-01-01

Non-destructive magnetic resonance imaging (MRI) revealed pathological anatomical features of coast live oak trees (Quercus agrifolia) that were naturally infected with Phytophthora ramorum. Fresh excised whole slices showing typical macroscopic cankers and bleeding were examined. Infected areas (i.e. cankers) were compared to...

Normal Rates of Neuroradiological Findings in Children with High Functioning Autism

ERIC Educational Resources Information Center

Vasa, Roma A.; Ranta, Marin; Huisman, Thierry A. G. M.; Pinto, Pedro S.; Tillman, Rachael M.; Mostofsky, Stewart H.

2012-01-01

Magnetic resonance imaging (MRI) has been used to analyze highly specific volumetric and morphological features of the brains of individuals with autism spectrum disorder (ASD). To date, there are few comprehensive studies examining the prevalence of neuroradiologic findings seen on routine MRI scans in children with ASD. This study examined the…

High-resolution 3-T MRI of the triangular fibrocartilage complex in the wrist: injury pattern and MR features.

PubMed

Zhan, Huili; Zhang, Huibo; Bai, Rongjie; Qian, Zhanhua; Liu, Yue; Zhang, Heng; Yin, Yuming

2017-12-01

To investigate if using high-resolution 3-T MRI can identify additional injuries of the triangular fibrocartilage complex (TFCC) beyond the Palmer classification. Eighty-six patients with surgically proven TFCC injury were included in this study. All patients underwent high-resolution 3-T MRI of the injured wrist. The MR imaging features of TFCC were analyzed according to the Palmer classification. According to the Palmer classification, 69 patients could be classified as having Palmer injuries (52 had traumatic tears and 17 had degenerative tears). There were 17 patients whose injuries could not be classified according to the Palmer classification: 13 had volar or dorsal capsular TFC detachment and 4 had a horizontal tear of the articular disk. Using high-resolution 3-T MRI, we have not only found all the TFCC injuries described in the Palmer classification, additional injury types were found in this study, including horizontal tear of the TFC and capsular TFC detachment. We propose the modified Palmer classification and add the injury types that were not included in the original Palmer classification.

MRI appearance of posterior cruciate ligament tears.

PubMed

Rodriguez, William; Vinson, Emily N; Helms, Clyde A; Toth, Alison P

2008-10-01

There is little in the radiology literature regarding the MRI appearance of a torn posterior cruciate ligament (PCL). The purpose of this study was to describe the MRI appearance of surgically proven PCL tears and to emphasize previously unreported signs. The PCL is usually injured as the result of stretching deformation; on MRI, the ligament maintains continuity as a single structure with apparent thickening. On sagittal T2-weighted images, an anteroposterior diameter of 7 mm or more is highly suggestive of a torn PCL. Increased intrasubstance signal intensity in the PCL on proton-density images with lower signal intensity on T2-weighted images is another common feature.

Differentiation of Glioblastoma and Lymphoma Using Feature Extraction and Support Vector Machine.

PubMed

Yang, Zhangjing; Feng, Piaopiao; Wen, Tian; Wan, Minghua; Hong, Xunning

2017-01-01

Differentiation of glioblastoma multiformes (GBMs) and lymphomas using multi-sequence magnetic resonance imaging (MRI) is an important task that is valuable for treatment planning. However, this task is a challenge because GBMs and lymphomas may have a similar appearance in MRI images. This similarity may lead to misclassification and could affect the treatment results. In this paper, we propose a semi-automatic method based on multi-sequence MRI to differentiate these two types of brain tumors. Our method consists of three steps: 1) the key slice is selected from 3D MRIs and region of interests (ROIs) are drawn around the tumor region; 2) different features are extracted based on prior clinical knowledge and validated using a t-test; and 3) features that are helpful for classification are used to build an original feature vector and a support vector machine is applied to perform classification. In total, 58 GBM cases and 37 lymphoma cases are used to validate our method. A leave-one-out crossvalidation strategy is adopted in our experiments. The global accuracy of our method was determined as 96.84%, which indicates that our method is effective for the differentiation of GBM and lymphoma and can be applied in clinical diagnosis. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

Differential diagnosis of ventriculomegaly and brainstem kinking on fetal MRI.

PubMed

Amir, Tali; Poretti, Andrea; Boltshauser, Eugen; Huisman, Thierry A G M

2016-01-01

Fetal ventriculomegaly is a common and frequently leading neuroimaging finding in complex brain malformations. Here we report on pre- and postnatal neuroimaging findings in three fetuses with prenatal ventriculomegaly and brainstem kinking. We aim to identify key neuroimaging features that may allow the prenatal differentiation between diseases associated with fetal ventriculomegaly and brainstem kinking. All pre- and postnatal magnetic resonance imaging (MRI) data were qualitatively evaluated for infra- and supratentorial abnormalities. Data about clinical features and genetic findings were collected from clinical histories. In all three patients, fetal MRI showed ventriculomegaly and brainstem kinking. In two patients, postnatal MRI also showed supratentorial migration abnormalities and eye abnormalities were found. In these children, the diagnosis of α-dystroglycanopathy was genetically confirmed. In the third patient, basal ganglia had an abnormal shape on MRI suggesting a tubulinopathy. The differential diagnosis of prenatal ventriculomegaly and brainstem kinking includes α-dystroglycanopathies, X-linked hydrocephalus due to mutations in L1CAM, and tubulinopathies. The prenatal differentiation between these diseases may be difficult. The presence of ocular abnormalities on prenatal neuroimaging may favor α-dystroglycanopathies, while dysplastic basal ganglia may suggest a tubulinopathy. However, in some patients the final differentiation between these diseases is possible only postnatally. Copyright © 2015 The Japanese Society of Child Neurology. Published by Elsevier B.V. All rights reserved.

MRI-visible perivascular space location is associated with Alzheimer's disease independently of amyloid burden.

PubMed

Banerjee, Gargi; Kim, Hee Jin; Fox, Zoe; Jäger, H Rolf; Wilson, Duncan; Charidimou, Andreas; Na, Han Kyu; Na, Duk L; Seo, Sang Won; Werring, David J

2017-04-01

Perivascular spaces that are visible on magnetic resonance imaging (MRI) are a neuroimaging marker of cerebral small vessel disease. Their location may relate to the type of underlying small vessel pathology: those in the white matter centrum semi-ovale have been associated with cerebral amyloid angiopathy, while those in the basal ganglia have been associated with deep perforating artery arteriolosclerosis. As cerebral amyloid angiopathy is an almost invariable pathological finding in Alzheimer's disease, we hypothesized that MRI-visible perivascular spaces in the centrum semi-ovale would be associated with a clinical diagnosis of Alzheimer's disease, whereas those in the basal ganglia would be associated with subcortical vascular cognitive impairment. We also hypothesized that MRI-visible perivascular spaces in the centrum semi-ovale would be associated with brain amyloid burden, as detected by amyloid positron emission tomography using 11C-Pittsburgh B compound (PiB-PET). Two hundred and twenty-six patients (Alzheimer's disease n = 110; subcortical vascular cognitive impairment n = 116) with standardized MRI and PiB-PET imaging were included. MRI-visible perivascular spaces were rated using a validated 4-point visual rating scale, and then categorized by severity ('none/mild', 'moderate' or 'frequent/severe'). Univariable and multivariable regression analyses were performed. Those with Alzheimer's disease-related cognitive impairment were younger, more likely to have a positive PiB-PET scan and carry at least one apolipoprotein E ɛ4 allele; those with subcortical vascular cognitive impairment were more likely to have hypertension, diabetes mellitus, hyperlipidaemia, prior stroke, lacunes, deep microbleeds, and carry the apolipoprotein E ɛ3 allele. In adjusted analyses, the severity of MRI-visible perivascular spaces in the centrum semi-ovale was independently associated with clinically diagnosed Alzheimer's disease (frequent/severe grade odds ratio 6.26, 95% confidence interval 1.66-23.58; P = 0.017, compared with none/mild grade), whereas the severity of MRI-visible perivascular spaces in the basal ganglia was associated with clinically diagnosed subcortical vascular cognitive impairment and negatively predicted Alzheimer's disease (frequent/severe grade odds ratio 0.03, 95% confidence interval 0.00-0.44; P = 0.009, compared with none/mild grade). MRI-visible perivascular space severity in either location did not predict PiB-PET. These findings provide further evidence that the anatomical distribution of MRI-visible perivascular spaces may reflect the underlying cerebral small vessel disease. Using MRI-visible perivascular space location and severity together with other imaging markers may improve the diagnostic value of neuroimaging in memory clinic populations, in particular in differentiating between clinically diagnosed Alzheimer's and subcortical vascular cognitive impairment. © The Author (2017). Published by Oxford University Press on behalf of the Guarantors of Brain. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Rare variants of the 3’-5’ DNA exonuclease TREX1 in early onset small vessel stroke

PubMed Central

McGlasson, Sarah; Rannikmäe, Kristiina; Bevan, Steven; Logan, Clare; Bicknell, Louise S.; Jury, Alexa; Jackson, Andrew P.

2017-01-01

Background: Monoallelic and biallelic mutations in the exonuclease TREX1 cause monogenic small vessel diseases (SVD). Given recent evidence for genetic and pathophysiological overlap between monogenic and polygenic forms of SVD, evaluation of TREX1 in small vessel stroke is warranted. Methods: We sequenced the TREX1 gene in an exploratory cohort of patients with lacunar stroke (Edinburgh Stroke Study, n=290 lacunar stroke cases). We subsequently performed a fully blinded case-control study of early onset MRI-confirmed small vessel stroke within the UK Young Lacunar Stroke Resource (990 cases, 939 controls). Results: No patients with canonical disease-causing mutations of TREX1 were identified in cases or controls. Analysis of an exploratory cohort identified a potential association between rare variants of TREX1 and patients with lacunar stroke. However, subsequent controlled and blinded evaluation of TREX1 in a larger and MRI-confirmed patient cohort, the UK Young Lacunar Stroke Resource, identified heterozygous rare variants in 2.1% of cases and 2.3% of controls. No association was observed with stroke risk (odds ratio = 0.90; 95% confidence interval, 0.49-1.65 p=0.74). Similarly no association was seen with rare TREX1 variants with predicted deleterious effects on enzyme function (odds ratio = 1.05; 95% confidence interval, 0.43-2.61 p=0.91). Conclusions: No patients with early-onset lacunar stroke had genetic evidence of a TREX1-associated monogenic microangiopathy. These results show no evidence of association between rare variants of TREX1 and early onset lacunar stroke. This includes rare variants that significantly affect protein and enzyme function. Routine sequencing of the TREX1 gene in patients with early onset lacunar stroke is therefore unlikely to be of diagnostic utility, in the absence of syndromic features or family history. PMID:29387804

Impact of sequential proton density fat fraction for quantification of hepatic steatosis in nonalcoholic fatty liver disease.

PubMed

Idilman, Ilkay S; Keskin, Onur; Elhan, Atilla Halil; Idilman, Ramazan; Karcaaltincaba, Musturay

2014-05-01

To determine the utility of sequential MRI-estimated proton density fat fraction (MRI-PDFF) for quantification of the longitudinal changes in liver fat content in individuals with nonalcoholic fatty liver disease (NAFLD). A total of 18 consecutive individuals (M/F: 10/8, mean age: 47.7±9.8 years) diagnosed with NAFLD, who underwent sequential PDFF calculations for the quantification of hepatic steatosis at two different time points, were included in the study. All patients underwent T1-independent volumetric multi-echo gradient-echo imaging with T2* correction and spectral fat modeling. A close correlation for quantification of hepatic steatosis between the initial MRI-PDFF and liver biopsy was observed (rs=0.758, p<0.001). The median interval between two sequential MRI-PDFF measurements was 184 days. From baseline to the end of the follow-up period, serum GGT level and homeostasis model assessment score were significantly improved (p=0.015, p=0.006, respectively), whereas BMI, serum AST, and ALT levels were slightly decreased. MRI-PDFFs were significantly improved (p=0.004). A good correlation between two sequential MRI-PDFF calculations was observed (rs=0.714, p=0.001). With linear regression analyses, only delta serum ALT levels had a significant effect on delta MRI-PDFF calculations (r2=38.6%, p=0.006). At least 5.9% improvement in MRI-PDFF is needed to achieve a normalized abnormal ALT level. The improvement of MRI-PDFF score was associated with the improvement of biochemical parameters in patients who had improvement in delta MRI-PDFF (p<0.05). MRI-PDFF can be used for the quantification of the longitudinal changes of hepatic steatosis. The changes in serum ALT levels significantly reflected changes in MRI-PDFF in patients with NAFLD.

The role of early magnetic resonance imaging in predicting survival on bevacizumab for recurrent glioblastoma: Results from a prospective clinical trial (CABARET).

PubMed

Field, Kathryn M; Phal, Pramit M; Fitt, Greg; Goh, Christine; Nowak, Anna K; Rosenthal, Mark A; Simes, John; Barnes, Elizabeth H; Sawkins, Kate; Cher, Lawrence M; Hovey, Elizabeth J; Wheeler, Helen

2017-09-15

Bevacizumab has been associated with prolonged progression-free survival for patients with recurrent glioblastoma; however, not all derive a benefit. An early indicator of efficacy or futility may allow early discontinuation for nonresponders. This study prospectively assessed the role of early magnetic resonance imaging (eMRI) and its correlation with subsequent routine magnetic resonance imaging (MRI) results and survival. Patients were part of a randomized phase 2 clinical trial (CABARET) comparing bevacizumab with bevacizumab plus carboplatin for recurrent glioblastoma. eMRI was conducted after 4 weeks in the trial (after 2 treatments with bevacizumab [10 mg/kg every 2 weeks]). The results were compared with the results of the subsequent 8-week MRI standard. For 119 of 122 patients, eMRI was available, and 111 had subsequent MRI for comparison. Thirty-six (30%) had an early radiological response, and 17 (14%) had progressive disease. The concordance between eMRI and 8-week MRI was moderate (κ = 0.56), with most providing the same result (n = 79 [71%]). There was strong evidence that progression-free survival and overall survival were predicted by the eMRI response (both P values < .001). The median survival was 8.6 months for an eMRI response, 6.6 months for stable disease, and 3.7 months for progressive disease; the hazard ratio (progressive disease vs stable disease) was 3.4 (95% confidence interval, 1.9-6.0). Landmark analyses showed that eMRI progression was a strong predictor of mortality independent of other potential baseline predictors. In this study, early progression on MRI appears to be a robust marker of a poor prognosis for patients on bevacizumab. Cancer 2017;123:3576-82. © 2017 American Cancer Society. © 2017 American Cancer 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.

CT and MRI Findings in Cerebral Aspergilloma.

PubMed

Gärtner, Friederike; Forstenpointner, Julia; Ertl-Wagner, Birgit; Hooshmand, Babak; Riedel, Christian; Jansen, Olav

2017-11-20

Purpose  Invasive aspergillosis usually affects immunocompromised patients. It carries a high risk of morbidity and mortality and usually has a nonspecific clinical presentation. Early diagnosis is essential in order to start effective treatment and improve clinical outcome. Materials and Methods  In a retrospective search of the PACS databases from two medical centers, we identified 9 patients with histologically proven cerebral aspergilloma. We systematically analyzed CT and MRI imaging findings to identify typical imaging appearances of cerebral aspergilloma. Results  CT did not show a typical appearance of the aspergillomas. In 100 % (9/9) there was a rim-attenuated diffusion restriction on MRI imaging. Multiple hypointense layers in the aspergillus wall, especially on the internal side, were detected in 100 % on T2-weighted imaging (9/9). Aspergillomas were T1-hypointense in 66 % of cases (6/9) and partly T1-hyperintense in 33 % (3/9). In 78 % (7/9) of cases, a rim-attenuated diffusion restriction was detected after contrast agent application. Conclusion  Nine cases were identified. Whereas CT features were less typical, we observed the following imaging features on MRI: A strong, rim-attenuated diffusion restriction (9/9); onion layer-like hypointense zones, in particular in the innermost part of the abscess wall on T2-weighted images (9/9). Enhancement of the lesion border was present in the majority of the cases (7/9). Key points   · There are typical MRI imaging features of aspergillomas.. · However, these findings could be affected by the immune status of the patient.. · Swift identification of aspergilloma imaging patterns is essential to allow for adequate therapeutic decision making.. Citation Format · Gärtner F, Forstenpointner J, Ertl-Wagner B et al. CT and MRI Findings in Cerebral Aspergilloma. Fortschr Röntgenstr 2017; DOI: 10.1055/s-0043-120766. © Georg Thieme Verlag KG Stuttgart · New York.

Automated segmentation of the parotid gland based on atlas registration and machine learning: a longitudinal MRI study in head-and-neck radiation therapy.

PubMed

Yang, Xiaofeng; Wu, Ning; Cheng, Guanghui; Zhou, Zhengyang; Yu, David S; Beitler, Jonathan J; Curran, Walter J; Liu, Tian

2014-12-01

To develop an automated magnetic resonance imaging (MRI) parotid segmentation method to monitor radiation-induced parotid gland changes in patients after head and neck radiation therapy (RT). The proposed method combines the atlas registration method, which captures the global variation of anatomy, with a machine learning technology, which captures the local statistical features, to automatically segment the parotid glands from the MRIs. The segmentation method consists of 3 major steps. First, an atlas (pre-RT MRI and manually contoured parotid gland mask) is built for each patient. A hybrid deformable image registration is used to map the pre-RT MRI to the post-RT MRI, and the transformation is applied to the pre-RT parotid volume. Second, the kernel support vector machine (SVM) is trained with the subject-specific atlas pair consisting of multiple features (intensity, gradient, and others) from the aligned pre-RT MRI and the transformed parotid volume. Third, the well-trained kernel SVM is used to differentiate the parotid from surrounding tissues in the post-RT MRIs by statistically matching multiple texture features. A longitudinal study of 15 patients undergoing head and neck RT was conducted: baseline MRI was acquired prior to RT, and the post-RT MRIs were acquired at 3-, 6-, and 12-month follow-up examinations. The resulting segmentations were compared with the physicians' manual contours. Successful parotid segmentation was achieved for all 15 patients (42 post-RT MRIs). The average percentage of volume differences between the automated segmentations and those of the physicians' manual contours were 7.98% for the left parotid and 8.12% for the right parotid. The average volume overlap was 91.1% ± 1.6% for the left parotid and 90.5% ± 2.4% for the right parotid. The parotid gland volume reduction at follow-up was 25% at 3 months, 27% at 6 months, and 16% at 12 months. We have validated our automated parotid segmentation algorithm in a longitudinal study. This segmentation method may be useful in future studies to address radiation-induced xerostomia in head and neck radiation therapy. Copyright © 2014 Elsevier Inc. All rights reserved.

Data-driven mapping of hypoxia-related tumor heterogeneity using DCE-MRI and OE-MRI.

PubMed

Featherstone, Adam K; O'Connor, James P B; Little, Ross A; Watson, Yvonne; Cheung, Sue; Babur, Muhammad; Williams, Kaye J; Matthews, Julian C; Parker, Geoff J M

2018-04-01

Previous work has shown that combining dynamic contrast-enhanced (DCE)-MRI and oxygen-enhanced (OE)-MRI binary enhancement maps can identify tumor hypoxia. The current work proposes a novel, data-driven method for mapping tissue oxygenation and perfusion heterogeneity, based on clustering DCE/OE-MRI data. DCE-MRI and OE-MRI were performed on nine U87 (glioblastoma) and seven Calu6 (non-small cell lung cancer) murine xenograft tumors. Area under the curve and principal component analysis features were calculated and clustered separately using Gaussian mixture modelling. Evaluation metrics were calculated to determine the optimum feature set and cluster number. Outputs were quantitatively compared with a previous non data-driven approach. The optimum method located six robustly identifiable clusters in the data, yielding tumor region maps with spatially contiguous regions in a rim-core structure, suggesting a biological basis. Mean within-cluster enhancement curves showed physiologically distinct, intuitive kinetics of enhancement. Regions of DCE/OE-MRI enhancement mismatch were located, and voxel categorization agreed well with the previous non data-driven approach (Cohen's kappa = 0.61, proportional agreement = 0.75). The proposed method locates similar regions to the previous published method of binarization of DCE/OE-MRI enhancement, but renders a finer segmentation of intra-tumoral oxygenation and perfusion. This could aid in understanding the tumor microenvironment and its heterogeneity. Magn Reson Med 79:2236-2245, 2018. © 2017 The Authors Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. © 2017 The Authors Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine.

Combination of rs-fMRI and sMRI Data to Discriminate Autism Spectrum Disorders in Young Children Using Deep Belief Network.

PubMed

Akhavan Aghdam, Maryam; Sharifi, Arash; Pedram, Mir Mohsen

2018-05-07

In recent years, the use of advanced magnetic resonance (MR) imaging methods such as functional magnetic resonance imaging (fMRI) and structural magnetic resonance imaging (sMRI) has recorded a great increase in neuropsychiatric disorders. Deep learning is a branch of machine learning that is increasingly being used for applications of medical image analysis such as computer-aided diagnosis. In a bid to classify and represent learning tasks, this study utilized one of the most powerful deep learning algorithms (deep belief network (DBN)) for the combination of data from Autism Brain Imaging Data Exchange I and II (ABIDE I and ABIDE II) datasets. The DBN was employed so as to focus on the combination of resting-state fMRI (rs-fMRI), gray matter (GM), and white matter (WM) data. This was done based on the brain regions that were defined using the automated anatomical labeling (AAL), in order to classify autism spectrum disorders (ASDs) from typical controls (TCs). Since the diagnosis of ASD is much more effective at an early age, only 185 individuals (116 ASD and 69 TC) ranging in age from 5 to 10 years were included in this analysis. In contrast, the proposed method is used to exploit the latent or abstract high-level features inside rs-fMRI and sMRI data while the old methods consider only the simple low-level features extracted from neuroimages. Moreover, combining multiple data types and increasing the depth of DBN can improve classification accuracy. In this study, the best combination comprised rs-fMRI, GM, and WM for DBN of depth 3 with 65.56% accuracy (sensitivity = 84%, specificity = 32.96%, F1 score = 74.76%) obtained via 10-fold cross-validation. This result outperforms previously presented methods on ABIDE I dataset.

Heterogeneity wavelet kinetics from DCE-MRI for classifying gene expression based breast cancer recurrence risk.

PubMed

Mahrooghy, Majid; Ashraf, Ahmed B; Daye, Dania; Mies, Carolyn; Feldman, Michael; Rosen, Mark; Kontos, Despina

2013-01-01

Breast tumors are heterogeneous lesions. Intra-tumor heterogeneity presents a major challenge for cancer diagnosis and treatment. Few studies have worked on capturing tumor heterogeneity from imaging. Most studies to date consider aggregate measures for tumor characterization. In this work we capture tumor heterogeneity by partitioning tumor pixels into subregions and extracting heterogeneity wavelet kinetic (HetWave) features from breast dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) to obtain the spatiotemporal patterns of the wavelet coefficients and contrast agent uptake from each partition. Using a genetic algorithm for feature selection, and a logistic regression classifier with leave one-out cross validation, we tested our proposed HetWave features for the task of classifying breast cancer recurrence risk. The classifier based on our features gave an ROC AUC of 0.78, outperforming previously proposed kinetic, texture, and spatial enhancement variance features which give AUCs of 0.69, 0.64, and 0.65, respectively.

New Findings, Classification and Long-Term Follow-Up Study Based on MRI Characterization of Brainstem Encephalitis Induced by Enterovirus 71

PubMed Central

Wen, Feiqiu; Huang, Wenxian; Gan, Yungen; Zeng, Weibin; Chen, Ranran; He, Yanxia; Wang, Yonker; Liu, Zaiyi; Liang, Changhong; Wong, Kelvin K. L.

2016-01-01

Background To report the diversity of MRI features of brainstem encephalitis (BE) induced by Enterovirus 71. This is supported by implementation and testing of our new classification scheme in order to improve the diagnostic level on this specific disease. Methods Neuroimaging of 91 pediatric patients who got EV71 related BE were hospitalized between March, 2010 to October, 2012, were analyzed retrospectively. All patients underwent pre- and post-contrast MRI scan. Thereafter, 31 patients were randomly called back for follow-up MRI study during December 2013 to August 2014. The MRI signal patterns of BE primary lesion were analyzed and classified according to MR signal alteration at various disease stages. Findings in fatal and non-fatal cases were compared, and according to the MRI scan time point during the course of this disease, the patients’ conditions were classified as 1) acute stage, 2) convalescence stage, 3) post mortem stage, and 4) long term follow-up study. Results 103 patients were identified. 11 patients did not undergo MRI, as they died within 48 hours. One patient died on 14th day without MR imaging. 2 patients had postmortem MRI. Medical records and imaging were reviewed in the 91 patients, aged 4 months to 12 years, and two cadavers who have had MRI scan. At acute stage: the most frequent pattern (40 patients) was foci of prolonged T1 and T2 signal, with (15) or without (25) contrast enhancement. We observed a novel pattern in 4 patients having foci of low signal intensity on T2WI, with contrast enhancement. Another pattern in 10 patients having foci of contrast enhancement without abnormalities in T1WI or T2WI weighted images. Based on 2 cases, the entire medulla and pons had prolonged T1 and T2 signal, and 2 of our postmortem cases demonstrated the same pattern. At convalescence stage, the pattern observed in 4 patients was foci of prolonged T1 and T2 signal without contrast enhancement. Follow-up MR study of 31 cases showed normal in 26 cases, and demonstrated foci of prolonged T1 and T2 signal with hyper-intensity on FLAIR in 3 cases, or of prolonged T1 and T2 signal with hypo-intensity on FLAIR in 2 cases. Most importantly, MR findings of each case were thoroughly investigated and classified according to phases and MRI signal alteration. Conclusions This study has provided enhanced and useful information for the MRI features of BE induced by EV71, apart from common practice established by previous reports. In addition, a classification scheme that summarizes all types of features based on the MRI signal at the four different stages of the disease would be helpful to improve the diagnostic level. PMID:27798639

New Findings, Classification and Long-Term Follow-Up Study Based on MRI Characterization of Brainstem Encephalitis Induced by Enterovirus 71.

PubMed

Zeng, Hongwu; Wen, Feiqiu; Huang, Wenxian; Gan, Yungen; Zeng, Weibin; Chen, Ranran; He, Yanxia; Wang, Yonker; Liu, Zaiyi; Liang, Changhong; Wong, Kelvin K L

2016-01-01

To report the diversity of MRI features of brainstem encephalitis (BE) induced by Enterovirus 71. This is supported by implementation and testing of our new classification scheme in order to improve the diagnostic level on this specific disease. Neuroimaging of 91 pediatric patients who got EV71 related BE were hospitalized between March, 2010 to October, 2012, were analyzed retrospectively. All patients underwent pre- and post-contrast MRI scan. Thereafter, 31 patients were randomly called back for follow-up MRI study during December 2013 to August 2014. The MRI signal patterns of BE primary lesion were analyzed and classified according to MR signal alteration at various disease stages. Findings in fatal and non-fatal cases were compared, and according to the MRI scan time point during the course of this disease, the patients' conditions were classified as 1) acute stage, 2) convalescence stage, 3) post mortem stage, and 4) long term follow-up study. 103 patients were identified. 11 patients did not undergo MRI, as they died within 48 hours. One patient died on 14th day without MR imaging. 2 patients had postmortem MRI. Medical records and imaging were reviewed in the 91 patients, aged 4 months to 12 years, and two cadavers who have had MRI scan. At acute stage: the most frequent pattern (40 patients) was foci of prolonged T1 and T2 signal, with (15) or without (25) contrast enhancement. We observed a novel pattern in 4 patients having foci of low signal intensity on T2WI, with contrast enhancement. Another pattern in 10 patients having foci of contrast enhancement without abnormalities in T1WI or T2WI weighted images. Based on 2 cases, the entire medulla and pons had prolonged T1 and T2 signal, and 2 of our postmortem cases demonstrated the same pattern. At convalescence stage, the pattern observed in 4 patients was foci of prolonged T1 and T2 signal without contrast enhancement. Follow-up MR study of 31 cases showed normal in 26 cases, and demonstrated foci of prolonged T1 and T2 signal with hyper-intensity on FLAIR in 3 cases, or of prolonged T1 and T2 signal with hypo-intensity on FLAIR in 2 cases. Most importantly, MR findings of each case were thoroughly investigated and classified according to phases and MRI signal alteration. This study has provided enhanced and useful information for the MRI features of BE induced by EV71, apart from common practice established by previous reports. In addition, a classification scheme that summarizes all types of features based on the MRI signal at the four different stages of the disease would be helpful to improve the diagnostic level.

Cross-sectional anatomy, computed tomography and magnetic resonance imaging of the head of common dolphin (Delphinus delphis) and striped dolphin (Stenella coeruleoalba).

PubMed

Alonso-Farré, J M; Gonzalo-Orden, M; Barreiro-Vázquez, J D; Barreiro-Lois, A; André, M; Morell, M; Llarena-Reino, M; Monreal-Pawlowsky, T; Degollada, E

2015-02-01

Computed tomography (CT) and low-field magnetic resonance imaging (MRI) were used to scan seven by-caught dolphin cadavers, belonging to two species: four common dolphins (Delphinus delphis) and three striped dolphins (Stenella coeruleoalba). CT and MRI were obtained with the animals in ventral recumbency. After the imaging procedures, six dolphins were frozen at -20°C and sliced in the same position they were examined. Not only CT and MRI scans, but also cross sections of the heads were obtained in three body planes: transverse (slices of 1 cm thickness) in three dolphins, sagittal (5 cm thickness) in two dolphins and dorsal (5 cm thickness) in two dolphins. Relevant anatomical structures were identified and labelled on each cross section, obtaining a comprehensive bi-dimensional topographical anatomy guide of the main features of the common and the striped dolphin head. Furthermore, the anatomical cross sections were compared with their corresponding CT and MRI images, allowing an imaging identification of most of the anatomical features. CT scans produced an excellent definition of the bony and air-filled structures, while MRI allowed us to successfully identify most of the soft tissue structures in the dolphin's head. This paper provides a detailed anatomical description of the head structures of common and striped dolphins and compares anatomical cross sections with CT and MRI scans, becoming a reference guide for the interpretation of imaging studies. © 2014 Blackwell Verlag GmbH.

Co-registration of In-Vivo Human MRI Brain Images to Postmortem Histological Microscopic Images

PubMed Central

Singh, M.; Rajagopalan, A.; Kim, T.-S.; Hwang, D.; Chui, H.; Zhang, X.-L.; Lee, A.-Y.; Zarow, C.

2009-01-01

Certain features such as small vascular lesions seen in human MRI are detected reliably only in postmortem histological samples by microscopic imaging. Co-registration of these microscopically detected features to their corresponding locations in the in-vivo images would be of great benefit to understanding the MRI signatures of specific diseases. Using non-linear Polynomial transformation, we report a method to co-register in-vivo MRIs to microscopic images of histological samples drawn off the postmortem brain. The approach utilizes digital photographs of postmortem slices as an intermediate reference to co-register the MRIs to microscopy. The overall procedure is challenging due to gross structural deformations in the postmortem brain during extraction and subsequent distortions in the histological preparations. Hemispheres of the brain were co-registered separately to mitigate these effects. Approaches relying on matching single-slices, multiple-slices and entire volumes in conjunction with different similarity measures suggested that using four slices at a time in combination with two sequential measures, Pearson correlation coefficient followed by mutual information, produced the best MRI-postmortem co-registration according to a voxel mismatch count. The accuracy of the overall registration was evaluated by measuring the 3D Euclidean distance between the locations of microscopically identified lesions on postmortem slices and their MRI-postmortem co-registered locations. The results show a mean 3D displacement of 5.1 ± 2.0 mm between the in-vivo MRI and microscopically determined locations for 21 vascular lesions in 11 subjects. PMID:19169415

Predicting clinical decline in progressive agrammatic aphasia and apraxia of speech.

PubMed

Whitwell, Jennifer L; Weigand, Stephen D; Duffy, Joseph R; Clark, Heather M; Strand, Edythe A; Machulda, Mary M; Spychalla, Anthony J; Senjem, Matthew L; Jack, Clifford R; Josephs, Keith A

2017-11-28

To determine whether baseline clinical and MRI features predict rate of clinical decline in patients with progressive apraxia of speech (AOS). Thirty-four patients with progressive AOS, with AOS either in isolation or in the presence of agrammatic aphasia, were followed up longitudinally for up to 4 visits, with clinical testing and MRI at each visit. Linear mixed-effects regression models including all visits (n = 94) were used to assess baseline clinical and MRI variables that predict rate of worsening of aphasia, motor speech, parkinsonism, and behavior. Clinical predictors included baseline severity and AOS type. MRI predictors included baseline frontal, premotor, motor, and striatal gray matter volumes. More severe parkinsonism at baseline was associated with faster rate of decline in parkinsonism. Patients with predominant sound distortions (AOS type 1) showed faster rates of decline in aphasia and motor speech, while patients with segmented speech (AOS type 2) showed faster rates of decline in parkinsonism. On MRI, we observed trends for fastest rates of decline in aphasia in patients with relatively small left, but preserved right, Broca area and precentral cortex. Bilateral reductions in lateral premotor cortex were associated with faster rates of decline of behavior. No associations were observed between volumes and decline in motor speech or parkinsonism. Rate of decline of each of the 4 clinical features assessed was associated with different baseline clinical and regional MRI predictors. Our findings could help improve prognostic estimates for these patients. © 2017 American Academy of Neurology.

Improved brain tumor segmentation by utilizing tumor growth model in longitudinal brain MRI

NASA Astrophysics Data System (ADS)

Pei, Linmin; Reza, Syed M. S.; Li, Wei; Davatzikos, Christos; Iftekharuddin, Khan M.

2017-03-01

In this work, we propose a novel method to improve texture based tumor segmentation by fusing cell density patterns that are generated from tumor growth modeling. To model tumor growth, we solve the reaction-diffusion equation by using Lattice-Boltzmann method (LBM). Computational tumor growth modeling obtains the cell density distribution that potentially indicates the predicted tissue locations in the brain over time. The density patterns is then considered as novel features along with other texture (such as fractal, and multifractal Brownian motion (mBm)), and intensity features in MRI for improved brain tumor segmentation. We evaluate the proposed method with about one hundred longitudinal MRI scans from five patients obtained from public BRATS 2015 data set, validated by the ground truth. The result shows significant improvement of complete tumor segmentation using ANOVA analysis for five patients in longitudinal MR images.

Improved brain tumor segmentation by utilizing tumor growth model in longitudinal brain MRI.

PubMed

Pei, Linmin; Reza, Syed M S; Li, Wei; Davatzikos, Christos; Iftekharuddin, Khan M

2017-02-11

In this work, we propose a novel method to improve texture based tumor segmentation by fusing cell density patterns that are generated from tumor growth modeling. In order to model tumor growth, we solve the reaction-diffusion equation by using Lattice-Boltzmann method (LBM). Computational tumor growth modeling obtains the cell density distribution that potentially indicates the predicted tissue locations in the brain over time. The density patterns is then considered as novel features along with other texture (such as fractal, and multifractal Brownian motion (mBm)), and intensity features in MRI for improved brain tumor segmentation. We evaluate the proposed method with about one hundred longitudinal MRI scans from five patients obtained from public BRATS 2015 data set, validated by the ground truth. The result shows significant improvement of complete tumor segmentation using ANOVA analysis for five patients in longitudinal MR images.

The MRI features of placental adhesion disorder—a pictorial review

PubMed Central

Teixidor Vinas, Mireia; Whitby, Elspeth

2016-01-01

Placental adhesion disorder (PAD) comprises placenta accreta, increta and percreta lesions; these are classified according to the depth of uterine invasion. Although PAD is considered a rare condition, its incidence has increased 10-fold in the last 50 years. Ultrasound is the primary imaging modality for the assessment of the placenta and in the majority of cases, it is sufficient for diagnosis; however, when ultrasound findings are suspicious or inconclusive, MRI is recommended as an adjunct imaging technique. Numerous MRI features of PAD have been described, including dark intraplacental bands, disorganized intraplacental vascularity and abnormal uterine bulging. This pictorial review describes and illustrates these characteristics and discusses their implications in planning delivery. In addition, we present a series of “pitfall” cases to aid the interpreting radiologist and discuss management of PAD. PAD is a clinical and diagnostic challenge that is encountered with increasing frequency, requiring a cohesive multidisciplinary approach to its management. PMID:27355318

Designing Image Operators for MRI-PET Image Fusion of the Brain

NASA Astrophysics Data System (ADS)

Márquez, Jorge; Gastélum, Alfonso; Padilla, Miguel A.

2006-09-01

Our goal is to obtain images combining in a useful and precise way the information from 3D volumes of medical imaging sets. We address two modalities combining anatomy (Magnetic Resonance Imaging or MRI) and functional information (Positron Emission Tomography or PET). Commercial imaging software offers image fusion tools based on fixed blending or color-channel combination of two modalities, and color Look-Up Tables (LUTs), without considering the anatomical and functional character of the image features. We used a sensible approach for image fusion taking advantage mainly from the HSL (Hue, Saturation and Luminosity) color space, in order to enhance the fusion results. We further tested operators for gradient and contour extraction to enhance anatomical details, plus other spatial-domain filters for functional features corresponding to wide point-spread-function responses in PET images. A set of image-fusion operators was formulated and tested on PET and MRI acquisitions.

Complementary aspects of diffusion imaging and fMRI; I: structure and function.

PubMed

Mulkern, Robert V; Davis, Peter E; Haker, Steven J; Estepar, Raul San Jose; Panych, Lawrence P; Maier, Stephan E; Rivkin, Michael J

2006-05-01

Studying the intersection of brain structure and function is an important aspect of modern neuroscience. The development of magnetic resonance imaging (MRI) over the last 25 years has provided new and powerful tools for the study of brain structure and function. Two tools in particular, diffusion imaging and functional MRI (fMRI), are playing increasingly important roles in elucidating the complementary aspects of brain structure and function. In this work, we review basic technical features of diffusion imaging and fMRI for studying the integrity of white matter structural components and for determining the location and extent of cortical activation in gray matter, respectively. We then review a growing body of literature in which the complementary aspects of diffusion imaging and fMRI, applied as separate examinations but analyzed in tandem, have been exploited to enhance our knowledge of brain structure and function.

Reproducibility of EEG-fMRI results in a patient with fixation-off sensitivity.

PubMed

Formaggio, Emanuela; Storti, Silvia Francesca; Galazzo, Ilaria Boscolo; Bongiovanni, Luigi Giuseppe; Cerini, Roberto; Fiaschi, Antonio; Manganotti, Paolo

2014-07-01

Blood oxygenation level-dependent (BOLD) activation associated with interictal epileptiform discharges in a patient with fixation-off sensitivity (FOS) was studied using a combined electroencephalography-functional magnetic resonance imaging (EEG-fMRI) technique. An automatic approach for combined EEG-fMRI analysis and a subject-specific hemodynamic response function was used to improve general linear model analysis of the fMRI data. The EEG showed the typical features of FOS, with continuous epileptiform discharges during elimination of central vision by eye opening and closing and fixation; modification of this pattern was clearly visible and recognizable. During all 3 recording sessions EEG-fMRI activations indicated a BOLD signal decrease related to epileptiform activity in the parietal areas. This study can further our understanding of this EEG phenomenon and can provide some insight into the reliability of the EEG-fMRI technique in localizing the irritative zone.

Normalization of similarity-based individual brain networks from gray matter MRI and its association with neurodevelopment in infants with intrauterine growth restriction.

PubMed

Batalle, Dafnis; Muñoz-Moreno, Emma; Figueras, Francesc; Bargallo, Nuria; Eixarch, Elisenda; Gratacos, Eduard

2013-12-01

Obtaining individual biomarkers for the prediction of altered neurological outcome is a challenge of modern medicine and neuroscience. Connectomics based on magnetic resonance imaging (MRI) stands as a good candidate to exhaustively extract information from MRI by integrating the information obtained in a few network features that can be used as individual biomarkers of neurological outcome. However, this approach typically requires the use of diffusion and/or functional MRI to extract individual brain networks, which require high acquisition times and present an extreme sensitivity to motion artifacts, critical problems when scanning fetuses and infants. Extraction of individual networks based on morphological similarity from gray matter is a new approach that benefits from the power of graph theory analysis to describe gray matter morphology as a large-scale morphological network from a typical clinical anatomic acquisition such as T1-weighted MRI. In the present paper we propose a methodology to normalize these large-scale morphological networks to a brain network with standardized size based on a parcellation scheme. The proposed methodology was applied to reconstruct individual brain networks of 63 one-year-old infants, 41 infants with intrauterine growth restriction (IUGR) and 22 controls, showing altered network features in the IUGR group, and their association with neurodevelopmental outcome at two years of age by means of ordinal regression analysis of the network features obtained with Bayley Scale for Infant and Toddler Development, third edition. Although it must be more widely assessed, this methodology stands as a good candidate for the development of biomarkers for altered neurodevelopment in the pediatric population. © 2013 Elsevier Inc. All rights reserved.

Molecular classification of patients with grade II/III glioma using quantitative MRI characteristics.

PubMed

Bahrami, Naeim; Hartman, Stephen J; Chang, Yu-Hsuan; Delfanti, Rachel; White, Nathan S; Karunamuni, Roshan; Seibert, Tyler M; Dale, Anders M; Hattangadi-Gluth, Jona A; Piccioni, David; Farid, Nikdokht; McDonald, Carrie R

2018-06-02

Molecular markers of WHO grade II/III glioma are known to have important prognostic and predictive implications and may be associated with unique imaging phenotypes. The purpose of this study is to determine whether three clinically relevant molecular markers identified in gliomas-IDH, 1p/19q, and MGMT status-show distinct quantitative MRI characteristics on FLAIR imaging. Sixty-one patients with grade II/III gliomas who had molecular data and MRI available prior to radiation were included. Quantitative MRI features were extracted that measured tissue heterogeneity (homogeneity and pixel correlation) and FLAIR border distinctiveness (edge contrast; EC). T-tests were conducted to determine whether patients with different genotypes differ across the features. Logistic regression with LASSO regularization was used to determine the optimal combination of MRI and clinical features for predicting molecular subtypes. Patients with IDH wildtype tumors showed greater signal heterogeneity (p = 0.001) and lower EC (p = 0.008) within the FLAIR region compared to IDH mutant tumors. Among patients with IDH mutant tumors, 1p/19q co-deleted tumors had greater signal heterogeneity (p = 0.002) and lower EC (p = 0.005) compared to 1p/19q intact tumors. MGMT methylated tumors showed lower EC (p = 0.03) compared to the unmethylated group. The combination of FLAIR border distinctness, heterogeneity, and pixel correlation optimally classified tumors by IDH status. Quantitative imaging characteristics of FLAIR heterogeneity and border pattern in grade II/III gliomas may provide unique information for determining molecular status at time of initial diagnostic imaging, which may then guide subsequent surgical and medical management.

Magnetic resonance imaging features of Great Danes with and without clinical signs of cervical spondylomyelopathy

PubMed Central

Martin-Vaquero, Paula; da Costa, Ronaldo C.

2014-01-01

Objective To characterize and compare the MRI morphological features of the cervical vertebral column of Great Danes with and without clinical signs of cervical spondylomyelopathy (CSM). Design Prospective cohort study. Animals 30 Great Danes (15 clinically normal and 15 CSM-affected). Procedures All dogs underwent MRI of the cervical vertebral column (C2–3 through T1–2). Features evaluated included sites of subarachnoid space compression, spinal cord compression, or both; degree, cause, and direction of compression; MRI signal changes of the spinal cord; articular process (facet) joint characteristics; internal vertebral venous plexus visibility; and presence of extradural synovial cysts as well as presence and degree of intervertebral disk degeneration and foraminal stenosis. Results Clinically normal and CSM-affected dogs had 11 and 61 compressive sites, respectively, detected with MRI. All CSM-affected dogs had ≥ 1 site of spinal cord compression. No signal changes were observed in spinal cords of normal dogs, whereas 14 sites of hyperintensity were found in 9 CSM-affected dogs. Foraminal stenosis was present in 11 clinically normal and all CSM-affected dogs. The number of stenotic foraminal sites was significantly greater in the CSM-affected group, and severe stenosis appeared to be more common in this group than in the clinically normal group. Significant differences were identified between clinically normal and CSM-affected dogs with regard to amount of synovial fluid evident, regularity of articular surfaces, degree of articular process joint proliferation, and internal vertebral venous plexus visibility. Conclusions and Clinical Relevance Abnormalities were detected with MRI in several clinically normal Great Danes. Severe spinal cord compression, number of stenotic foraminal sites, and signal changes within the spinal cord distinguished CSM-affected from clinically normal Great Danes. PMID:25075822

Magnetic resonance imaging features of Great Danes with and without clinical signs of cervical spondylomyelopathy.

PubMed

Martin-Vaquero, Paula; da Costa, Ronaldo C

2014-08-15

To characterize and compare the MRI morphological features of the cervical vertebral column of Great Danes with and without clinical signs of cervical spondylomyelopathy (CSM). Prospective cohort study. 30 Great Danes (15 clinically normal and 15 CSM-affected). All dogs underwent MRI of the cervical vertebral column (C2-3 through T1-2). Features evaluated included sites of subarachnoid space compression, spinal cord compression, or both; degree, cause, and direction of compression; MRI signal changes of the spinal cord; articular process (facet) joint characteristics; internal vertebral venous plexus visibility; and presence of extradural synovial cysts as well as presence and degree of intervertebral disk degeneration and foraminal stenosis. Clinically normal and CSM-affected dogs had 11 and 61 compressive sites, respectively, detected with MRI. All CSM-affected dogs had ≥ 1 site of spinal cord compression. No signal changes were observed in spinal cords of normal dogs, whereas 14 sites of hyperintensity were found in 9 CSM-affected dogs. Foraminal stenosis was present in 11 clinically normal and all CSM-affected dogs. The number of stenotic foraminal sites was significantly greater in the CSM-affected group, and severe stenosis appeared to be more common in this group than in the clinically normal group. Significant differences were identified between clinically normal and CSM-affected dogs with regard to amount of synovial fluid evident, regularity of articular surfaces, degree of articular process joint proliferation, and internal vertebral venous plexus visibility. Abnormalities were detected with MRI in several clinically normal Great Danes. Severe spinal cord compression, number of stenotic foraminal sites, and signal changes within the spinal cord distinguished CSM-affected from clinically normal Great Danes.

Methodologies for semiquantitative evaluation of hip osteoarthritis by magnetic resonance imaging: approaches based on the whole organ and focused on active lesions.

PubMed

Jaremko, Jacob L; Lambert, Robert G W; Zubler, Veronika; Weber, Ulrich; Loeuille, Damien; Roemer, Frank W; Cibere, Jolanda; Pianta, Marcus; Gracey, David; Conaghan, Philip; Ostergaard, Mikkel; Maksymowych, Walter P

2014-02-01

As a wider variety of therapeutic options for osteoarthritis (OA) becomes available, there is an increasing need to objectively evaluate disease severity on magnetic resonance imaging (MRI). This is more technically challenging at the hip than at the knee, and as a result, few systematic scoring systems exist. The OMERACT (Outcome Measures in Rheumatology) filter of truth, discrimination, and feasibility can be used to validate image-based scoring systems. Our objective was (1) to review the imaging features relevant to the assessment of severity and progression of hip OA; and (2) to review currently used methods to grade these features in existing hip OA scoring systems. A systematic literature review was conducted. MEDLINE keyword search was performed for features of arthropathy (such as hip + bone marrow edema or lesion, synovitis, cyst, effusion, cartilage, etc.) and scoring system (hip + OA + MRI + score or grade), with a secondary manual search for additional references in the retrieved publications. Findings relevant to the severity of hip OA include imaging markers associated with inflammation (bone marrow lesion, synovitis, effusion), structural damage (cartilage loss, osteophytes, subchondral cysts, labral tears), and predisposing geometric factors (hip dysplasia, femoral-acetabular impingement). Two approaches to the semiquantitative assessment of hip OA are represented by Hip OA MRI Scoring System (HOAMS), a comprehensive whole organ assessment of nearly all findings, and the Hip Inflammation MRI Scoring System (HIMRISS), which selectively scores only active lesions (bone marrow lesion, synovitis/effusion). Validation is presently confined to limited assessment of reliability. Two methods for semiquantitative assessment of hip OA on MRI have been described and validation according to the OMERACT Filter is limited to evaluation of reliability.

Which patellofemoral joint imaging features are associated with patellofemoral pain? Systematic review and meta-analysis.

PubMed

Drew, B T; Redmond, A C; Smith, T O; Penny, F; Conaghan, P G

2016-02-01

To review the association between patellofemoral joint (PFJ) imaging features and patellofemoral pain (PFP). A systematic review of the literature from AMED, CiNAHL, Cochrane Central Register of Controlled Trials (CENTRAL), MEDLINE, PEDro, EMBASE and SPORTDiscus was undertaken from their inception to September 2014. Studies were eligible if they used magnetic resonance imaging (MRI), computed tomography (CT), ultrasound (US) or X-ray (XR) to compare PFJ features between a PFP group and an asymptomatic control group in people <45 years of age. A pooled meta-analysis was conducted and data was interpreted using a best evidence synthesis. Forty studies (all moderate to high quality) describing 1043 people with PFP and 839 controls were included. Two features were deemed to have a large standardised mean difference (SMD) based on meta-analysis: an increased MRI bisect offset at 0° knee flexion under load (0.99; 95% CI: 0.49, 1.49) and an increased CT congruence angle at 15° knee flexion, both under load (1.40 95% CI: 0.04, 2.76) and without load (1.24; 95% CI: 0.37, 2.12). A medium SMD was identified for MRI patella tilt and patellofemoral contact area. Limited evidence was found to support the association of other imaging features with PFP. A sensitivity analysis showed an increase in the SMD for patella bisect offset at 0° knee flexion (1.91; 95% CI: 1.31, 2.52) and patella tilt at 0° knee flexion (0.99; 95% CI: 0.47, 1.52) under full weight bearing. Certain PFJ imaging features were associated with PFP. Future interventional strategies may be targeted at these features. CRD 42014009503. Copyright © 2015 The Authors. Published by Elsevier Ltd.. All rights reserved.

4D tumor centroid tracking using orthogonal 2D dynamic MRI: Implications for radiotherapy planning

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

Tryggestad, Erik; Flammang, Aaron; Shea, Steven M.

2013-09-15

Purpose: Current pretreatment, 4D imaging techniques are suboptimal in that they sample breathing motion over a very limited “snapshot” in time. Heretofore, long-duration, 4D motion characterization for radiotherapy planning, margin optimization, and validation have been impractical for safety reasons, requiring invasive markers imaged under x-ray fluoroscopy. To characterize 3D tumor motion and associated variability over durations more consistent with treatments, the authors have developed a practical dynamic MRI (dMRI) technique employing two orthogonal planes acquired in a continuous, interleaved fashion.Methods: 2D balanced steady-state free precession MRI was acquired continuously over 9–14 min at approximately 4 Hz in three healthy volunteersmore » using a commercial 1.5 T system; alternating orthogonal imaging planes (sagittal, coronal, sagittal, etc.) were employed. The 2D in-plane pixel resolution was 2 × 2 mm{sup 2} with a 5 mm slice profile. Simultaneous with image acquisition, the authors monitored a 1D surrogate respiratory signal using a device available with the MRI system. 2D template matching-based anatomic feature registration, or tracking, was performed independently in each orientation. 4D feature tracking at the raw frame rate was derived using spline interpolation.Results: Tracking vascular features in the lung for two volunteers and pancreatic features in one volunteer, the authors have successfully demonstrated this method. Registration error, defined here as the difference between the sagittal and coronal tracking result in the SI direction, ranged from 0.7 to 1.6 mm (1σ) which was less than the acquired image resolution. Although the healthy volunteers were instructed to relax and breathe normally, significantly variable respiration was observed. To demonstrate potential applications of this technique, the authors subsequently explored the intrafraction stability of hypothetical tumoral internal target volumes and 3D spatial probability distribution functions. The surrogate respiratory information allowed the authors to show how this technique can be used to study correlations between internal and external (surrogate) information over these prolonged durations. However, compared against the gold standard of the time stamps in the dMRI frames, the temporal synchronization of the surrogate 1D respiratory information was shown to be likely unreliable.Conclusions: The authors have established viability of a novel and practical pretreatment, 4D tumor centroid tracking method employing a commercially available dynamic MRI sequence. Further developments from the vendor are likely needed to provide a reliably synchronized surrogate 1D respiratory signal, which will likely broaden the utility of this method in the pretreatment radiotherapy planning context.« less

TU-CD-BRB-09: Prediction of Chemo-Radiation Outcome for Rectal Cancer Based On Radiomics of Tumor Clinical Characteristics and Multi-Parametric MRI

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

Nie, K; Yue, N; Shi, L

2015-06-15

Purpose: To evaluate the tumor clinical characteristics and quantitative multi-parametric MR imaging features for prediction of response to chemo-radiation treatment (CRT) in locally advanced rectal cancer (LARC). Methods: Forty-three consecutive patients (59.7±6.9 years, from 09/2013 – 06/2014) receiving neoadjuvant CRT followed by surgery were enrolled. All underwent MRI including anatomical T1/T2, Dynamic Contrast Enhanced (DCE)-MRI and Diffusion-Weighted MRI (DWI) prior to the treatment. A total of 151 quantitative features, including morphology/Gray Level Co-occurrence Matrix (GLCM) texture from T1/T2, enhancement kinetics and the voxelized distribution from DCE-MRI, apparent diffusion coefficient (ADC) from DWI, along with clinical information (carcinoembryonic antigen CEA level,more » TNM staging etc.), were extracted for each patient. Response groups were separated based on down-staging, good response and pathological complete response (pCR) status. Logistic regression analysis (LRA) was used to select the best predictors to classify different groups and the predictive performance were calculated using receiver operating characteristic (ROC) analysis. Results: Individual imaging category or clinical charateristics might yield certain level of power in assessing the response. However, the combined model outperformed than any category alone in prediction. With selected features as Volume, GLCM AutoCorrelation (T2), MaxEnhancementProbability (DCE-MRI), and MeanADC (DWI), the down-staging prediciton accuracy (area under the ROC curve, AUC) could be 0.95, better than individual tumor metrics with AUC from 0.53–0.85. While for the pCR prediction, the best set included CEA (clinical charateristics), Homogeneity (DCE-MRI) and MeanADC (DWI) with an AUC of 0.89, more favorable compared to conventional tumor metrics with an AUC ranging from 0.511–0.79. Conclusion: Through a systematic analysis of multi-parametric MR imaging features, we are able to build models with improved predictive value over conventional imaging or clinical metrics. This is encouraging, suggesting the wealth of imaging radiomics should be further explored to help tailor the treatment into the era of personalized medicine. This work is supported by the National Science Foundation of China (NSFC Grant No. 81201091), National High Technology Research and Development Program of China (863 program, Grant No. 2015AA020917), and Fund Project for Excellent Abroad Scholar Personnel in Science and Technology.« less

Voxel-based automated detection of focal cortical dysplasia lesions using diffusion tensor imaging and T2-weighted MRI data.

PubMed

Wang, Yanming; Zhou, Yawen; Wang, Huijuan; Cui, Jin; Nguchu, Benedictor Alexander; Zhang, Xufei; Qiu, Bensheng; Wang, Xiaoxiao; Zhu, Mingwang

2018-05-21

The aim of this study was to automatically detect focal cortical dysplasia (FCD) lesions in patients with extratemporal lobe epilepsy by relying on diffusion tensor imaging (DTI) and T2-weighted magnetic resonance imaging (MRI) data. We implemented an automated classifier using voxel-based multimodal features to identify gray and white matter abnormalities of FCD in patient cohorts. In addition to the commonly used T2-weighted image intensity feature, DTI-based features were also utilized. A Gaussian processes for machine learning (GPML) classifier was tested on 12 patients with FCD (8 with histologically confirmed FCD) scanned at 1.5 T and cross-validated using a leave-one-out strategy. Moreover, we compared the multimodal GPML paradigm's performance with that of single modal GPML and classical support vector machine (SVM). Our results demonstrated that the GPML performance on DTI-based features (mean AUC = 0.63) matches with the GPML performance on T2-weighted image intensity feature (mean AUC = 0.64). More promisingly, GPML yielded significantly improved performance (mean AUC = 0.76) when applying DTI-based features to multimodal paradigm. Based on the results, it can also be clearly stated that the proposed GPML strategy performed better and is robust to unbalanced dataset contrary to SVM that performed poorly (AUC = 0.69). Therefore, the GPML paradigm using multimodal MRI data containing DTI modality has promising result towards detection of the FCD lesions and provides an effective direction for future researches. Copyright © 2018 Elsevier Inc. All rights reserved.

WE-G-BRD-08: Motion Analysis for Rectal Cancer: Implications for Adaptive Radiotherapy On the MR-Linac

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

Kleijnen, J; Asselen, B van; Burbach, M

2015-06-15

Purpose: Purpose of this study is to find the optimal trade-off between adaptation interval and margin reduction and to define the implications of motion for rectal cancer boost radiotherapy on a MR-linac. Methods: Daily MRI scans were acquired of 16 patients, diagnosed with rectal cancer, prior to each radiotherapy fraction in one week (N=76). Each scan session consisted of T2-weighted and three 2D sagittal cine-MRI, at begin (t=0 min), middle (t=9:30 min) and end (t=18:00 min) of scan session, for 1 minute at 2 Hz temporal resolution. Tumor and clinical target volume (CTV) were delineated on each T2-weighted scan andmore » transferred to each cine-MRI. The start frame of the begin scan was used as reference and registered to frames at time-points 15, 30 and 60 seconds, 9:30 and 18:00 minutes and 1, 2, 3 and 4 days later. Per time-point, motion of delineated voxels was evaluated using the deformation vector fields of the registrations and the 95th percentile distance (dist95%) was calculated as measure of motion. Per time-point, the distance that includes 90% of all cases was taken as estimate of required planning target volume (PTV)-margin. Results: Highest motion reduction is observed going from 9:30 minutes to 60 seconds. We observe a reduction in margin estimates from 10.6 to 2.7 mm and 16.1 to 4.6 mm for tumor and CTV, respectively, when adapting every 60 seconds compared to not adapting treatment. A 75% and 71% reduction, respectively. Further reduction in adaptation time-interval yields only marginal motion reduction. For adaptation intervals longer than 18:00 minutes only small motion reductions are observed. Conclusion: The optimal adaptation interval for adaptive rectal cancer (boost) treatments on a MR-linac is 60 seconds. This results in substantial smaller PTV-margin estimates. Adaptation intervals of 18:00 minutes and higher, show little improvement in motion reduction.« less

The Physics and Mathematics of MRI

NASA Astrophysics Data System (ADS)

Ansorge, Richard; Graves, Martin

2016-10-01

Magnetic Resonance Imaging is a very important clinical imaging tool. It combines different fields of physics and engineering in a uniquely complex way. MRI is also surprisingly versatile, `pulse sequences' can be designed to yield many different types of contrast. This versatility is unique to MRI. This short book gives both an in depth account of the methods used for the operation and construction of modern MRI systems and also the principles of sequence design and many examples of applications. An important additional feature of this book is the detailed discussion of the mathematical principles used in building optimal MRI systems and for sequence design. The mathematical discussion is very suitable for undergraduates attending medical physics courses. It is also more complete than usually found in alternative books for physical scientists or more clinically orientated works.

Predicting individual brain functional connectivity using a Bayesian hierarchical model.

PubMed

Dai, Tian; Guo, Ying

2017-02-15

Network-oriented analysis of functional magnetic resonance imaging (fMRI), especially resting-state fMRI, has revealed important association between abnormal connectivity and brain disorders such as schizophrenia, major depression and Alzheimer's disease. Imaging-based brain connectivity measures have become a useful tool for investigating the pathophysiology, progression and treatment response of psychiatric disorders and neurodegenerative diseases. Recent studies have started to explore the possibility of using functional neuroimaging to help predict disease progression and guide treatment selection for individual patients. These studies provide the impetus to develop statistical methodology that would help provide predictive information on disease progression-related or treatment-related changes in neural connectivity. To this end, we propose a prediction method based on Bayesian hierarchical model that uses individual's baseline fMRI scans, coupled with relevant subject characteristics, to predict the individual's future functional connectivity. A key advantage of the proposed method is that it can improve the accuracy of individualized prediction of connectivity by combining information from both group-level connectivity patterns that are common to subjects with similar characteristics as well as individual-level connectivity features that are particular to the specific subject. Furthermore, our method also offers statistical inference tools such as predictive intervals that help quantify the uncertainty or variability of the predicted outcomes. The proposed prediction method could be a useful approach to predict the changes in individual patient's brain connectivity with the progression of a disease. It can also be used to predict a patient's post-treatment brain connectivity after a specified treatment regimen. Another utility of the proposed method is that it can be applied to test-retest imaging data to develop a more reliable estimator for individual functional connectivity. We show there exists a nice connection between our proposed estimator and a recently developed shrinkage estimator of connectivity measures in the neuroimaging community. We develop an expectation-maximization (EM) algorithm for estimation of the proposed Bayesian hierarchical model. Simulations studies are performed to evaluate the accuracy of our proposed prediction methods. We illustrate the application of the methods with two data examples: the longitudinal resting-state fMRI from ADNI2 study and the test-retest fMRI data from Kirby21 study. In both the simulation studies and the fMRI data applications, we demonstrate that the proposed methods provide more accurate prediction and more reliable estimation of individual functional connectivity as compared with alternative methods. Copyright © 2017 Elsevier Inc. All rights reserved.

Dynamic contrast enhanced MRI of the placenta: A tool for prenatal diagnosis of placenta accreta?

PubMed

Millischer, A E; Deloison, B; Silvera, S; Ville, Y; Boddaert, N; Balvay, D; Siauve, N; Cuenod, C A; Tsatsaris, V; Sentilhes, L; Salomon, L J

2017-05-01

Ultrasound (US) is the primary imaging modality for the diagnosis of placenta accreta, but it is not sufficiently accurate. MRI morphologic criteria have recently emerged as a useful tool in this setting, but their analysis is too subjective. Recent studies suggest that gadolinium enhancement may help to distinguish between the stretched myometrium and placenta within a scar area. However, objective MRI criteria are still required for prenatal diagnosis of placenta accreta. The purpose of this study was to assess the diagnostic value of dynamic contrast gadolinium enhancement (DCE) MRI patterns for placenta accreta. MR images were acquired with a 1.5-T unit at 30-35 weeks of gestation in women with a history of Caesarian section, a low-lying anterior placenta, and US features compatible with placenta accreta. Sagittal, axial and coronal SSFP (Steady State Free Precession) sequences were acquired before injection. Then, contrast-enhanced dynamic T1-weighted images were acquired through the entire cross-sectional area of the placenta. Images were obtained sequentially at 10- to 14-s intervals for 2 min, beginning simultaneously with the bolus injection. Functional analysis was performed retrospectively, and tissular relative enhancement parameters were extracted from the recorded images. The suspected area of accreta (SAA) was placed in the region of the previous scar, and a control area (CA) of similar size was placed on the same image plane, as far as possible from the SAA. Semi-quantitative analysis of DCE-MR images was based on the kinetic enhancement curves in these two regions of interest (ROI). Three tissular relative enhancement parameters were compared according to the pregnancy outcomes, namely time to peak, maximal signal intensity, and area under the enhancement curve. We studied 9 women (43%) with accreta and 12 women (57%) with a normal placenta. All three tissular relative enhancement parameters differed significantly between the two groups (p < 10 -3 ). The use of dynamic contrast-enhanced MRI at 30-35 weeks of gestation in women with a high risk of placenta accreta allows the extraction of tissular enhancement parameters that differ significantly between placenta accreta and normal placenta. It therefore provides objective parameters on which to base the diagnosis and patient management. Copyright © 2017. Published by Elsevier Ltd.

Decoding the individual finger movements from single-trial functional magnetic resonance imaging recordings of human brain activity.

PubMed

Shen, Guohua; Zhang, Jing; Wang, Mengxing; Lei, Du; Yang, Guang; Zhang, Shanmin; Du, Xiaoxia

2014-06-01

Multivariate pattern classification analysis (MVPA) has been applied to functional magnetic resonance imaging (fMRI) data to decode brain states from spatially distributed activation patterns. Decoding upper limb movements from non-invasively recorded human brain activation is crucial for implementing a brain-machine interface that directly harnesses an individual's thoughts to control external devices or computers. The aim of this study was to decode the individual finger movements from fMRI single-trial data. Thirteen healthy human subjects participated in a visually cued delayed finger movement task, and only one slight button press was performed in each trial. Using MVPA, the decoding accuracy (DA) was computed separately for the different motor-related regions of interest. For the construction of feature vectors, the feature vectors from two successive volumes in the image series for a trial were concatenated. With these spatial-temporal feature vectors, we obtained a 63.1% average DA (84.7% for the best subject) for the contralateral primary somatosensory cortex and a 46.0% average DA (71.0% for the best subject) for the contralateral primary motor cortex; both of these values were significantly above the chance level (20%). In addition, we implemented searchlight MVPA to search for informative regions in an unbiased manner across the whole brain. Furthermore, by applying searchlight MVPA to each volume of a trial, we visually demonstrated the information for decoding, both spatially and temporally. The results suggest that the non-invasive fMRI technique may provide informative features for decoding individual finger movements and the potential of developing an fMRI-based brain-machine interface for finger movement. © 2014 Federation of European Neuroscience Societies and John Wiley & Sons Ltd.

SPIRAL-SPRITE: a rapid single point MRI technique for application to porous media.

PubMed

Szomolanyi, P; Goodyear, D; Balcom, B; Matheson, D

2001-01-01

This study presents the application of a new, rapid, single point MRI technique which samples k space with spiral trajectories. The general principles of the technique are outlined along with application to porous concrete samples, solid pharmaceutical tablets and gas phase imaging. Each sample was chosen to highlight specific features of the method.

Performance comparison of deep learning and segmentation-based radiomic methods in the task of distinguishing benign and malignant breast lesions on DCE-MRI

NASA Astrophysics Data System (ADS)

Antropova, Natasha; Huynh, Benjamin; Giger, Maryellen

2017-03-01

Intuitive segmentation-based CADx/radiomic features, calculated from the lesion segmentations of dynamic contrast-enhanced magnetic resonance images (DCE-MRIs) have been utilized in the task of distinguishing between malignant and benign lesions. Additionally, transfer learning with pre-trained deep convolutional neural networks (CNNs) allows for an alternative method of radiomics extraction, where the features are derived directly from the image data. However, the comparison of computer-extracted segmentation-based and CNN features in MRI breast lesion characterization has not yet been conducted. In our study, we used a DCE-MRI database of 640 breast cases - 191 benign and 449 malignant. Thirty-eight segmentation-based features were extracted automatically using our quantitative radiomics workstation. Also, 2D ROIs were selected around each lesion on the DCE-MRIs and directly input into a pre-trained CNN AlexNet, yielding CNN features. Each method was investigated separately and in combination in terms of performance in the task of distinguishing between benign and malignant lesions. Area under the ROC curve (AUC) served as the figure of merit. Both methods yielded promising classification performance with round-robin cross-validated AUC values of 0.88 (se =0.01) and 0.76 (se=0.02) for segmentationbased and deep learning methods, respectively. Combination of the two methods enhanced the performance in malignancy assessment resulting in an AUC value of 0.91 (se=0.01), a statistically significant improvement over the performance of the CNN method alone.

Differential diagnosis of neurodegenerative diseases using structural MRI data

PubMed Central

Koikkalainen, Juha; Rhodius-Meester, Hanneke; Tolonen, Antti; Barkhof, Frederik; Tijms, Betty; Lemstra, Afina W.; Tong, Tong; Guerrero, Ricardo; Schuh, Andreas; Ledig, Christian; Rueckert, Daniel; Soininen, Hilkka; Remes, Anne M.; Waldemar, Gunhild; Hasselbalch, Steen; Mecocci, Patrizia; van der Flier, Wiesje; Lötjönen, Jyrki

2016-01-01

Different neurodegenerative diseases can cause memory disorders and other cognitive impairments. The early detection and the stratification of patients according to the underlying disease are essential for an efficient approach to this healthcare challenge. This emphasizes the importance of differential diagnostics. Most studies compare patients and controls, or Alzheimer's disease with one other type of dementia. Such a bilateral comparison does not resemble clinical practice, where a clinician is faced with a number of different possible types of dementia. Here we studied which features in structural magnetic resonance imaging (MRI) scans could best distinguish four types of dementia, Alzheimer's disease, frontotemporal dementia, vascular dementia, and dementia with Lewy bodies, and control subjects. We extracted an extensive set of features quantifying volumetric and morphometric characteristics from T1 images, and vascular characteristics from FLAIR images. Classification was performed using a multi-class classifier based on Disease State Index methodology. The classifier provided continuous probability indices for each disease to support clinical decision making. A dataset of 504 individuals was used for evaluation. The cross-validated classification accuracy was 70.6% and balanced accuracy was 69.1% for the five disease groups using only automatically determined MRI features. Vascular dementia patients could be detected with high sensitivity (96%) using features from FLAIR images. Controls (sensitivity 82%) and Alzheimer's disease patients (sensitivity 74%) could be accurately classified using T1-based features, whereas the most difficult group was the dementia with Lewy bodies (sensitivity 32%). These results were notable better than the classification accuracies obtained with visual MRI ratings (accuracy 44.6%, balanced accuracy 51.6%). Different quantification methods provided complementary information, and consequently, the best results were obtained by utilizing several quantification methods. The results prove that automatic quantification methods and computerized decision support methods are feasible for clinical practice and provide comprehensive information that may help clinicians in the diagnosis making. PMID:27104138

Six-minute magnetic resonance imaging protocol for evaluation of acute ischemic stroke: pushing the boundaries.

PubMed

Nael, Kambiz; Khan, Rihan; Choudhary, Gagandeep; Meshksar, Arash; Villablanca, Pablo; Tay, Jennifer; Drake, Kendra; Coull, Bruce M; Kidwell, Chelsea S

2014-07-01

If magnetic resonance imaging (MRI) is to compete with computed tomography for evaluation of patients with acute ischemic stroke, there is a need for further improvements in acquisition speed. Inclusion criteria for this prospective, single institutional study were symptoms of acute ischemic stroke within 24 hours onset, National Institutes of Health Stroke Scale ≥3, and absence of MRI contraindications. A combination of echo-planar imaging (EPI) and a parallel acquisition technique were used on a 3T magnetic resonance (MR) scanner to accelerate the acquisition time. Image analysis was performed independently by 2 neuroradiologists. A total of 62 patients met inclusion criteria. A repeat MRI scan was performed in 22 patients resulting in a total of 84 MRIs available for analysis. Diagnostic image quality was achieved in 100% of diffusion-weighted imaging, 100% EPI-fluid attenuation inversion recovery imaging, 98% EPI-gradient recalled echo, 90% neck MR angiography and 96% of brain MR angiography, and 94% of dynamic susceptibility contrast perfusion scans with interobserver agreements (k) ranging from 0.64 to 0.84. Fifty-nine patients (95%) had acute infarction. There was good interobserver agreement for EPI-fluid attenuation inversion recovery imaging findings (k=0.78; 95% confidence interval, 0.66-0.87) and for detection of mismatch classification using dynamic susceptibility contrast-Tmax (k=0.92; 95% confidence interval, 0.87-0.94). Thirteen acute intracranial hemorrhages were detected on EPI-gradient recalled echo by both observers. A total of 68 and 72 segmental arterial stenoses were detected on contrast-enhanced MR angiography of the neck and brain with k=0.93, 95% confidence interval, 0.84 to 0.96 and 0.87, 95% confidence interval, 0.80 to 0.90, respectively. A 6-minute multimodal MR protocol with good diagnostic quality is feasible for the evaluation of patients with acute ischemic stroke and can result in significant reduction in scan time rivaling that of the multimodal computed tomographic protocol. © 2014 American Heart Association, Inc.

Atrial fibrillation and cognitive decline-the role of subclinical cerebral infarcts: the atherosclerosis risk in communities study.

PubMed

Chen, Lin Y; Lopez, Faye L; Gottesman, Rebecca F; Huxley, Rachel R; Agarwal, Sunil K; Loehr, Laura; Mosley, Thomas; Alonso, Alvaro

2014-09-01

The mechanism underlying the association of atrial fibrillation (AF) with cognitive decline in stroke-free individuals is unclear. We examined the association of incident AF with cognitive decline in stroke-free individuals, stratified by subclinical cerebral infarcts (SCIs) on brain MRI scans. We analyzed data from 935 stroke-free participants (mean age±SD, 61.5±4.3 years; 62% women; and 51% black) from 1993 to 1995 through 2004 to 2006 in the Atherosclerosis Risk in Communities Study, a biracial community-based prospective cohort study. Cognitive testing (including the digit symbol substitution and the word fluency tests) was performed in 1993 to 1995, 1996 to 1998, and 2004 to 2006 and brain MRI scans in 1993 to 1995 and 2004 to 2006. During follow-up, there were 48 incident AF events. Incident AF was associated with greater annual average rate of decline in digit symbol substitution (-0.77; 95% confidence interval, -1.55 to 0.01; P=0.054) and word fluency (-0.80; 95% confidence interval, -1.60 to -0.01; P=0.048). Among participants without SCIs on brain MRI scans, incident AF was not associated with cognitive decline. In contrast, incident AF was associated with greater annual average rate of decline in word fluency (-2.65; 95% confidence interval, -4.26 to -1.03; P=0.002) among participants with prevalent SCIs in 1993 to 1995. Among participants who developed SCIs during follow-up, incident AF was associated with a greater annual average rate of decline in digit symbol substitution (-1.51; 95% confidence interval, -3.02 to -0.01; P=0.049). The association of incident AF with cognitive decline in stroke-free individuals can be explained by the presence or development of SCIs, raising the possibility of anticoagulation as a strategy to prevent cognitive decline in AF. © 2014 American Heart Association, Inc.

Genes involved in prostate cancer progression determine MRI visibility

PubMed Central

Li, Ping; You, Sungyong; Nguyen, Christopher; Wang, Yanping; Kim, Jayoung; Sirohi, Deepika; Ziembiec, Asha; Luthringer, Daniel; Lin, Shih-Chieh; Daskivich, Timothy; Wu, Jonathan; Freeman, Michael R; Saouaf, Rola; Li, Debiao; Kim, Hyung L.

2018-01-01

MRI is used to image prostate cancer and target tumors for biopsy or therapeutic ablation. The objective was to understand the biology of tumors not visible on MRI that may go undiagnosed and untreated. Methods: Prostate cancers visible or invisible on multiparametric MRI were macrodissected and examined by RNAseq. Differentially expressed genes (DEGs) based on MRI visibility status were cross-referenced with publicly available gene expression databases to identify genes associated with disease progression. Genes with potential roles in determining MRI visibility and disease progression were knocked down in murine prostate cancer xenografts, and imaged by MRI. Results: RNAseq identified 1,654 DEGs based on MRI visibility status. Comparison of DEGs based on MRI visibility and tumor characteristics revealed that Gleason score (dissimilarity test, p<0.0001) and tumor size (dissimilarity test, p<0.039) did not completely determine MRI visibility. Genes in previously reported prognostic signatures significantly correlated with MRI visibility suggesting that MRI visibility was prognostic. Cross-referencing DEGs with external datasets identified four genes (PHYHD1, CENPF, ALDH2, GDF15) that predict MRI visibility, progression free survival and metastatic deposits. Genetic modification of a human prostate cancer cell line to induce miR-101 and suppress CENPF decreased cell migration and invasion. As prostate cancer xenografts in mice, these cells had decreased visibility on diffusion weighted MRI and decreased perfusion, which correlated with immunostaining showing decreased cell density and proliferation. Conclusions: Genes involved in prostate cancer prognosis and metastasis determine MRI visibility, indicating that MRI visibility has prognostic significance. MRI visibility was associated with genetic features linked to poor prognosis. PMID:29556354

Quantification of liver fat with respiratory-gated quantitative chemical shift encoded MRI.

PubMed

Motosugi, Utaroh; Hernando, Diego; Bannas, Peter; Holmes, James H; Wang, Kang; Shimakawa, Ann; Iwadate, Yuji; Taviani, Valentina; Rehm, Jennifer L; Reeder, Scott B

2015-11-01

To evaluate free-breathing chemical shift-encoded (CSE) magnetic resonance imaging (MRI) for quantification of hepatic proton density fat-fraction (PDFF). A secondary purpose was to evaluate hepatic R2* values measured using free-breathing quantitative CSE-MRI. Fifty patients (mean age, 56 years) were prospectively recruited and underwent the following four acquisitions to measure PDFF and R2*; 1) conventional breath-hold CSE-MRI (BH-CSE); 2) respiratory-gated CSE-MRI using respiratory bellows (BL-CSE); 3) respiratory-gated CSE-MRI using navigator echoes (NV-CSE); and 4) single voxel MR spectroscopy (MRS) as the reference standard for PDFF. Image quality was evaluated by two radiologists. MRI-PDFF measured from the three CSE-MRI methods were compared with MRS-PDFF using linear regression. The PDFF and R2* values were compared using two one-sided t-test to evaluate statistical equivalence. There was no significant difference in the image quality scores among the three CSE-MRI methods for either PDFF (P = 1.000) or R2* maps (P = 0.359-1.000). Correlation coefficients (95% confidence interval [CI]) for the PDFF comparisons were 0.98 (0.96-0.99) for BH-, 0.99 (0.97-0.99) for BL-, and 0.99 (0.98-0.99) for NV-CSE. The statistical equivalence test revealed that the mean difference in PDFF and R2* between any two of the three CSE-MRI methods was less than ±1 percentage point (pp) and ±5 s(-1) , respectively (P < 0.046). Respiratory-gated CSE-MRI with respiratory bellows or navigator echo are feasible methods to quantify liver PDFF and R2* and are as valid as the standard breath-hold technique. © 2015 Wiley Periodicals, Inc.

Annual Screening Strategies in BRCA1 and BRCA2 Gene Mutation Carriers: A Comparative Effectiveness Analysis

PubMed Central

Lowry, Kathryn P.; Lee, Janie M.; Kong, Chung Y.; McMahon, Pamela M.; Gilmore, Michael E.; Cott Chubiz, Jessica E.; Pisano, Etta D.; Gatsonis, Constantine; Ryan, Paula D.; Ozanne, Elissa M.; Gazelle, G. Scott

2011-01-01

Background While breast cancer screening with mammography and MRI is recommended for BRCA mutation carriers, there is no current consensus on the optimal screening regimen. Methods We used a computer simulation model to compare six annual screening strategies [film mammography (FM), digital mammography (DM), FM and magnetic resonance imaging (MRI) or DM and MRI contemporaneously, and alternating FM/MRI or DM/MRI at six-month intervals] beginning at ages 25, 30, 35, and 40, and two strategies of annual MRI with delayed alternating DM/FM to clinical surveillance alone. Strategies were evaluated without and with mammography-induced breast cancer risk, using two models of excess relative risk. Input parameters were obtained from the medical literature, publicly available databases, and calibration. Results Without radiation risk effects, alternating DM/MRI starting at age 25 provided the highest life expectancy (BRCA1: 72.52 years, BRCA2: 77.63 years). When radiation risk was included, a small proportion of diagnosed cancers were attributable to radiation exposure (BRCA1: <2%, BRCA2: <4%). With radiation risk, alternating DM/MRI at age 25 or annual MRI at age 25/delayed alternating DM at age 30 were most effective, depending on the radiation risk model used. Alternating DM/MRI starting at age 25 also had the highest number of false-positive screens/person (BRCA1: 4.5, BRCA2: 8.1). Conclusions Annual MRI at 25/delayed alternating DM at age 30 is likely the most effective screening strategy in BRCA mutation carriers. Screening benefits, associated risks and personal acceptance of false-positive results, should be considered in choosing the optimal screening strategy for individual women. PMID:21935911

Short- and long-term reliability of language fMRI.

PubMed

Nettekoven, Charlotte; Reck, Nicola; Goldbrunner, Roland; Grefkes, Christian; Weiß Lucas, Carolin

2018-08-01

When using functional magnetic resonance imaging (fMRI) for mapping important language functions, a high test-retest reliability is mandatory, both in basic scientific research and for clinical applications. We, therefore, systematically tested the short- and long-term reliability of fMRI in a group of healthy subjects using a picture naming task and a sparse-sampling fMRI protocol. We hypothesized that test-retest reliability might be higher for (i) speech-related motor areas than for other language areas and for (ii) the short as compared to the long intersession interval. 16 right-handed subjects (mean age: 29 years) participated in three sessions separated by 2-6 (session 1 and 2, short-term) and 21-34 days (session 1 and 3, long-term). Subjects were asked to perform the same overt picture naming task in each fMRI session (50 black-white images per session). Reliability was tested using the following measures: (i) Euclidean distances (ED) between local activation maxima and Centers of Gravity (CoGs), (ii) overlap volumes and (iii) voxel-wise intraclass correlation coefficients (ICCs). Analyses were performed for three regions of interest which were chosen based on whole-brain group data: primary motor cortex (M1), superior temporal gyrus (STG) and inferior frontal gyrus (IFG). Our results revealed that the activation centers were highly reliable, independent of the time interval, ROI or hemisphere with significantly smaller ED for the local activation maxima (6.45 ± 1.36 mm) as compared to the CoGs (8.03 ± 2.01 mm). In contrast, the extent of activation revealed rather low reliability values with overlaps ranging from 24% (IFG) to 56% (STG). Here, the left hemisphere showed significantly higher overlap volumes than the right hemisphere. Although mean ICCs ranged between poor (ICC<0.5) and moderate (ICC 0.5-0.74) reliability, highly reliable voxels (ICC>0.75) were found for all ROIs. Voxel-wise reliability of the different ROIs was influenced by the intersession interval. Taken together, we could show that, despite of considerable ROI-dependent variations of the extent of activation over time, highly reliable centers of activation can be identified using an overt picture naming paradigm. Copyright © 2018 Elsevier Inc. All rights reserved.

Diagnostic performance of 3D standing CT imaging for detection of knee osteoarthritis features.

PubMed

Segal, Neil A; Nevitt, Michael C; Lynch, John A; Niu, Jingbo; Torner, James C; Guermazi, Ali

2015-07-01

To determine the diagnostic performance of standing computerized tomography (SCT) of the knee for osteophytes and subchondral cysts compared with fixed-flexion radiography, using MRI as the reference standard. Twenty participants were recruited from the Multicenter Osteoarthritis Study. Participants' knees were imaged with SCT while standing in a knee-positioning frame, and with postero-anterior fixed-flexion radiography and 1T MRI. Medial and lateral marginal osteophytes and subchondral cysts were scored on bilateral radiographs and coronal SCT images using the OARSI grading system and on coronal MRI using Whole Organ MRI Scoring. Imaging modalities were read separately with images in random order. Sensitivity, specificity and accuracy for the detection of lesions were calculated and differences between modalities were tested using McNemar's test. Participants' mean age was 66.8 years, body mass index was 29.6 kg/m(2) and 50% were women. Of the 160 surfaces (medial and lateral femur and tibia for 40 knees), MRI revealed 84 osteophytes and 10 subchondral cysts. In comparison with osteophytes and subchondral cysts detected by MRI, SCT was significantly more sensitive (93 and 100%; p < 0.004) and accurate (95 and 99%; p < 0.001 for osteophytes) than plain radiographs (sensitivity 60 and 10% and accuracy 79 and 94%, respectively). For osteophytes, differences in sensitivity and accuracy were greatest at the medial femur (p = 0.002). In comparison with MRI, SCT imaging was more sensitive and accurate for detection of osteophytes and subchondral cysts than conventional fixed-flexion radiography. Additional study is warranted to assess diagnostic performance of SCT measures of joint space width, progression of OA features and the patellofemoral joint.

Multi-institutional validation of a novel textural analysis tool for preoperative stratification of suspected thyroid tumors on diffusion-weighted MRI.

PubMed

Brown, Anna M; Nagala, Sidhartha; McLean, Mary A; Lu, Yonggang; Scoffings, Daniel; Apte, Aditya; Gonen, Mithat; Stambuk, Hilda E; Shaha, Ashok R; Tuttle, R Michael; Deasy, Joseph O; Priest, Andrew N; Jani, Piyush; Shukla-Dave, Amita; Griffiths, John

2016-04-01

Ultrasound-guided fine needle aspirate cytology fails to diagnose many malignant thyroid nodules; consequently, patients may undergo diagnostic lobectomy. This study assessed whether textural analysis (TA) could noninvasively stratify thyroid nodules accurately using diffusion-weighted MRI (DW-MRI). This multi-institutional study examined 3T DW-MRI images obtained with spin echo echo planar imaging sequences. The training data set included 26 patients from Cambridge, United Kingdom, and the test data set included 18 thyroid cancer patients from Memorial Sloan Kettering Cancer Center (New York, New York, USA). Apparent diffusion coefficients (ADCs) were compared over regions of interest (ROIs) defined on thyroid nodules. TA, linear discriminant analysis (LDA), and feature reduction were performed using the 21 MaZda-generated texture parameters that best distinguished benign and malignant ROIs. Training data set mean ADC values were significantly different for benign and malignant nodules (P = 0.02) with a sensitivity and specificity of 70% and 63%, respectively, and a receiver operator characteristic (ROC) area under the curve (AUC) of 0.73. The LDA model of the top 21 textural features correctly classified 89/94 DW-MRI ROIs with 92% sensitivity, 96% specificity, and an AUC of 0.97. This algorithm correctly classified 16/18 (89%) patients in the independently obtained test set of thyroid DW-MRI scans. TA classifies thyroid nodules with high sensitivity and specificity on multi-institutional DW-MRI data sets. This method requires further validation in a larger prospective study. Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance. © 2015 The Authors. Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine.

The cheating liver: imaging of focal steatosis and fatty sparing.

PubMed

Dioguardi Burgio, Marco; Bruno, Onorina; Agnello, Francesco; Torrisi, Chiara; Vernuccio, Federica; Cabibbo, Giuseppe; Soresi, Maurizio; Petta, Salvatore; Calamia, Mauro; Papia, Giovanni; Gambino, Angelo; Ricceri, Viola; Midiri, Massimo; Lagalla, Roberto; Brancatelli, Giuseppe

2016-06-01

Focal steatosis and fatty sparing are a frequent finding in liver imaging, and can mimic solid lesions. Liver regional variations in the degree of fat accumulation can be related to vascular anomalies, metabolic disorders, use of certain drugs or coexistence of hepatic masses. CT and MRI are the modalities of choice for the noninvasive diagnosis of hepatic steatosis. Knowledge of CT and MRI appearance of focal steatosis and fatty sparing is crucial for an accurate diagnosis, and to rule-out other pathologic processes. This paper will review the CT and MRI techniques for the diagnosis of hepatic steatosis and the CT and MRI features of common and uncommon causes of focal steatosis and fatty sparing.

Fuzzy feature selection based on interval type-2 fuzzy sets

NASA Astrophysics Data System (ADS)

Cherif, Sahar; Baklouti, Nesrine; Alimi, Adel; Snasel, Vaclav

2017-03-01

When dealing with real world data; noise, complexity, dimensionality, uncertainty and irrelevance can lead to low performance and insignificant judgment. Fuzzy logic is a powerful tool for controlling conflicting attributes which can have similar effects and close meanings. In this paper, an interval type-2 fuzzy feature selection is presented as a new approach for removing irrelevant features and reducing complexity. We demonstrate how can Feature Selection be joined with Interval Type-2 Fuzzy Logic for keeping significant features and hence reducing time complexity. The proposed method is compared with some other approaches. The results show that the number of attributes is proportionally small.

Structural MRI-based detection of Alzheimer's disease using feature ranking and classification error.

PubMed

Beheshti, Iman; Demirel, Hasan; Farokhian, Farnaz; Yang, Chunlan; Matsuda, Hiroshi

2016-12-01

This paper presents an automatic computer-aided diagnosis (CAD) system based on feature ranking for detection of Alzheimer's disease (AD) using structural magnetic resonance imaging (sMRI) data. The proposed CAD system is composed of four systematic stages. First, global and local differences in the gray matter (GM) of AD patients compared to the GM of healthy controls (HCs) are analyzed using a voxel-based morphometry technique. The aim is to identify significant local differences in the volume of GM as volumes of interests (VOIs). Second, the voxel intensity values of the VOIs are extracted as raw features. Third, the raw features are ranked using a seven-feature ranking method, namely, statistical dependency (SD), mutual information (MI), information gain (IG), Pearson's correlation coefficient (PCC), t-test score (TS), Fisher's criterion (FC), and the Gini index (GI). The features with higher scores are more discriminative. To determine the number of top features, the estimated classification error based on training set made up of the AD and HC groups is calculated, with the vector size that minimized this error selected as the top discriminative feature. Fourth, the classification is performed using a support vector machine (SVM). In addition, a data fusion approach among feature ranking methods is introduced to improve the classification performance. The proposed method is evaluated using a data-set from ADNI (130 AD and 130 HC) with 10-fold cross-validation. The classification accuracy of the proposed automatic system for the diagnosis of AD is up to 92.48% using the sMRI data. An automatic CAD system for the classification of AD based on feature-ranking method and classification errors is proposed. In this regard, seven-feature ranking methods (i.e., SD, MI, IG, PCC, TS, FC, and GI) are evaluated. The optimal size of top discriminative features is determined by the classification error estimation in the training phase. The experimental results indicate that the performance of the proposed system is comparative to that of state-of-the-art classification models. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

MRI-Guided Selection of Patients for Acute Ischemic Stroke Treatment

PubMed Central

Leigh, Richard; Krakauer, John W.

2014-01-01

Purpose of review To summarize what is known about the use of MRI in acute stroke treatments (predominantly thrombolysis), to examine the assumptions and theories behind the interpretation of MR images of acute stroke and how they are used to select patients for therapies, and to suggest directions for future research. Recent findings Recent studies have been contradictory about the usefulness of MRI in selecting patients for treatment. New MRI models for selecting patients have emerged that focus not only on the ischemic penumbra but also the core infarct. Fixed time-window selection parameters are being replaced by individualized MRI features. New ways to interpret traditional MRI sequences are emerging. Summary Although the efficacy of acute stroke treatment is time dependent, the use of fixed time-windows does not account for individual differences in infarct evolution, which could be detected with MRI. While MRI shows promise for identifying patients who should be treated, as well as exclude patients who should not be treated, definitive evidence is still lacking. Future research should focus on validating the use of MRI to select patients for IV therapies in extended time windows. PMID:24978637

Control-group feature normalization for multivariate pattern analysis of structural MRI data using the support vector machine.

PubMed

Linn, Kristin A; Gaonkar, Bilwaj; Satterthwaite, Theodore D; Doshi, Jimit; Davatzikos, Christos; Shinohara, Russell T

2016-05-15

Normalization of feature vector values is a common practice in machine learning. Generally, each feature value is standardized to the unit hypercube or by normalizing to zero mean and unit variance. Classification decisions based on support vector machines (SVMs) or by other methods are sensitive to the specific normalization used on the features. In the context of multivariate pattern analysis using neuroimaging data, standardization effectively up- and down-weights features based on their individual variability. Since the standard approach uses the entire data set to guide the normalization, it utilizes the total variability of these features. This total variation is inevitably dependent on the amount of marginal separation between groups. Thus, such a normalization may attenuate the separability of the data in high dimensional space. In this work we propose an alternate approach that uses an estimate of the control-group standard deviation to normalize features before training. We study our proposed approach in the context of group classification using structural MRI data. We show that control-based normalization leads to better reproducibility of estimated multivariate disease patterns and improves the classifier performance in many cases. Copyright © 2016 Elsevier Inc. All rights reserved.

Complementary fMRI and EEG evidence for more efficient neural processing of rhythmic vs. unpredictably timed sounds

PubMed Central

van Atteveldt, Nienke; Musacchia, Gabriella; Zion-Golumbic, Elana; Sehatpour, Pejman; Javitt, Daniel C.; Schroeder, Charles

2015-01-01

The brain’s fascinating ability to adapt its internal neural dynamics to the temporal structure of the sensory environment is becoming increasingly clear. It is thought to be metabolically beneficial to align ongoing oscillatory activity to the relevant inputs in a predictable stream, so that they will enter at optimal processing phases of the spontaneously occurring rhythmic excitability fluctuations. However, some contexts have a more predictable temporal structure than others. Here, we tested the hypothesis that the processing of rhythmic sounds is more efficient than the processing of irregularly timed sounds. To do this, we simultaneously measured functional magnetic resonance imaging (fMRI) and electro-encephalograms (EEG) while participants detected oddball target sounds in alternating blocks of rhythmic (e.g., with equal inter-stimulus intervals) or random (e.g., with randomly varied inter-stimulus intervals) tone sequences. Behaviorally, participants detected target sounds faster and more accurately when embedded in rhythmic streams. The fMRI response in the auditory cortex was stronger during random compared to random tone sequence processing. Simultaneously recorded N1 responses showed larger peak amplitudes and longer latencies for tones in the random (vs. the rhythmic) streams. These results reveal complementary evidence for more efficient neural and perceptual processing during temporally predictable sensory contexts. PMID:26579044

Improving Functional MRI Registration Using Whole-Brain Functional Correlation Tensors.

PubMed

Zhou, Yujia; Yap, Pew-Thian; Zhang, Han; Zhang, Lichi; Feng, Qianjin; Shen, Dinggang

2017-09-01

Population studies of brain function with resting-state functional magnetic resonance imaging (rs-fMRI) largely rely on the accurate inter-subject registration of functional areas. This is typically achieved through registration of the corresponding T1-weighted MR images with more structural details. However, accumulating evidence has suggested that such strategy cannot well-align functional regions which are not necessarily confined by the anatomical boundaries defined by the T1-weighted MR images. To mitigate this problem, various registration algorithms based directly on rs-fMRI data have been developed, most of which have utilized functional connectivity (FC) as features for registration. However, most of the FC-based registration methods usually extract the functional features only from the thin and highly curved cortical grey matter (GM), posing a great challenge in accurately estimating the whole-brain deformation field. In this paper, we demonstrate that the additional useful functional features can be extracted from brain regions beyond the GM, particularly, white-matter (WM) based on rs-fMRI, for improving the overall functional registration. Specifically, we quantify the local anisotropic correlation patterns of the blood oxygenation level-dependent (BOLD) signals, modeled by functional correlation tensors (FCTs), in both GM and WM. Functional registration is then performed based on multiple components of the whole-brain FCTs using a multichannel Large Deformation Diffeomorphic Metric Mapping (mLDDMM) algorithm. Experimental results show that our proposed method achieves superior functional registration performance, compared with other conventional registration methods.

Clinical and Imaging Findings in Childhood Posterior Reversible Encephalopathy Syndrome

PubMed Central

GUNGOR, Serdal; KILIC, Betul; TABEL, Yilmaz; SELIMOGLU, Ayse; OZGEN, Unsal; YILMAZ, Sezai

2018-01-01

Objective Posterior reversible encephalopathy syndrome (PRES) is characterized by typical radiologic findings in the posterior regions of the cerebral hemispheres and cerebellum. The symptoms include headache, nausea, vomiting, visual disturbances, focal neurologic deficits, and seizures. The aim of this study is to evaluate the clinical and radiological features of PRES in children and to emphasize the recognition of atypical features. Materials & Methods We retrospectively examined 23 children with PRES from Mar 2010-Apr 2015 in Inonu University Turgut Ozal Medical Center in Turkey. We compared the clinical features and cranial MRI findings between underlying diseases of PRES. Results The most common precipitating factors were hypertension (78.2%) and medications, namely immunosuppressive and antineoplastic agents (60.8%). Manifestations included mental changes (100%), seizures (95.6%), headache (60.8%), and visual disturbances (21.7%) of mean 3.6 (range 1-10) days' duration. Cranial magnetic resonance imaging (MRI) showed bilateral occipital lesions in all patients, associated in 82.6% with less typical distribution of lesions in frontal, temporal or parietal lobes, cerebellum, corpus callosum, basal ganglia, thalamus, and brain stem. Frontal involvement was predominant, observed in 56.5% of patients. Clinical recovery was followed by radiologic resolution in all patients. Conclusion PRES is often unsuspected by the clinician, thus radiologists may be the first to suggest this diagnosis on an MRI obtained for seizures or encephalopathy. Atypical MRI finding is seen quite often. Rapid diagnosis and treatment are required to avoid a devastating outcome. PMID:29379559

Spatially aggregated multiclass pattern classification in functional MRI using optimally selected functional brain areas.

PubMed

Zheng, Weili; Ackley, Elena S; Martínez-Ramón, Manel; Posse, Stefan

2013-02-01

In previous works, boosting aggregation of classifier outputs from discrete brain areas has been demonstrated to reduce dimensionality and improve the robustness and accuracy of functional magnetic resonance imaging (fMRI) classification. However, dimensionality reduction and classification of mixed activation patterns of multiple classes remain challenging. In the present study, the goals were (a) to reduce dimensionality by combining feature reduction at the voxel level and backward elimination of optimally aggregated classifiers at the region level, (b) to compare region selection for spatially aggregated classification using boosting and partial least squares regression methods and (c) to resolve mixed activation patterns using probabilistic prediction of individual tasks. Brain activation maps from interleaved visual, motor, auditory and cognitive tasks were segmented into 144 functional regions. Feature selection reduced the number of feature voxels by more than 50%, leaving 95 regions. The two aggregation approaches further reduced the number of regions to 30, resulting in more than 75% reduction of classification time and misclassification rates of less than 3%. Boosting and partial least squares (PLS) were compared to select the most discriminative and the most task correlated regions, respectively. Successful task prediction in mixed activation patterns was feasible within the first block of task activation in real-time fMRI experiments. This methodology is suitable for sparsifying activation patterns in real-time fMRI and for neurofeedback from distributed networks of brain activation. Copyright © 2013 Elsevier Inc. All rights reserved.

Temporal Dermoid Cyst with Unusual Imaging Appearance: Case Report.

PubMed

Abderrahmen, Khansa; Bouhoula, Asma; Aouidj, Lasaad; Jemel, Hafedh

2016-01-01

Intracranial dermoid cysts are benign, slow growing tumors derived from ectopic inclusions of epithelial cells during closure of neural tube. These lesions, accounting for less than 1% of intracranial tumors, have characteristic computed tomography (CT) and magnetic resonance imaging (MRI) appearances that generally permits preoperative diagnosis. However, the radiologic features are uncommon and the cyst can be easily misdiagnosed with other tumors in rare cases. Herein, we report a case of a left temporoparietal dermoid cyst in a 48-year-old woman that was peroperatively and histopathologically proven but not advocated on CT and MRI. Clinical, radiological and histopathological features of a dermoid cyst are reviewed.

Multi-Sectional Views Textural Based SVM for MS Lesion Segmentation in Multi-Channels MRIs

PubMed Central

Abdullah, Bassem A; Younis, Akmal A; John, Nigel M

2012-01-01

In this paper, a new technique is proposed for automatic segmentation of multiple sclerosis (MS) lesions from brain magnetic resonance imaging (MRI) data. The technique uses a trained support vector machine (SVM) to discriminate between the blocks in regions of MS lesions and the blocks in non-MS lesion regions mainly based on the textural features with aid of the other features. The classification is done on each of the axial, sagittal and coronal sectional brain view independently and the resultant segmentations are aggregated to provide more accurate output segmentation. The main contribution of the proposed technique described in this paper is the use of textural features to detect MS lesions in a fully automated approach that does not rely on manually delineating the MS lesions. In addition, the technique introduces the concept of the multi-sectional view segmentation to produce verified segmentation. The proposed textural-based SVM technique was evaluated using three simulated datasets and more than fifty real MRI datasets. The results were compared with state of the art methods. The obtained results indicate that the proposed method would be viable for use in clinical practice for the detection of MS lesions in MRI. PMID:22741026

Decoding of visual activity patterns from fMRI responses using multivariate pattern analyses and convolutional neural network.

PubMed

Zafar, Raheel; Kamel, Nidal; Naufal, Mohamad; Malik, Aamir Saeed; Dass, Sarat C; Ahmad, Rana Fayyaz; Abdullah, Jafri M; Reza, Faruque

2017-01-01

Decoding of human brain activity has always been a primary goal in neuroscience especially with functional magnetic resonance imaging (fMRI) data. In recent years, Convolutional neural network (CNN) has become a popular method for the extraction of features due to its higher accuracy, however it needs a lot of computation and training data. In this study, an algorithm is developed using Multivariate pattern analysis (MVPA) and modified CNN to decode the behavior of brain for different images with limited data set. Selection of significant features is an important part of fMRI data analysis, since it reduces the computational burden and improves the prediction performance; significant features are selected using t-test. MVPA uses machine learning algorithms to classify different brain states and helps in prediction during the task. General linear model (GLM) is used to find the unknown parameters of every individual voxel and the classification is done using multi-class support vector machine (SVM). MVPA-CNN based proposed algorithm is compared with region of interest (ROI) based method and MVPA based estimated values. The proposed method showed better overall accuracy (68.6%) compared to ROI (61.88%) and estimation values (64.17%).

Fast and effective characterization of 3D region of interest in medical image data

NASA Astrophysics Data System (ADS)

Kontos, Despina; Megalooikonomou, Vasileios

2004-05-01

We propose a framework for detecting, characterizing and classifying spatial Regions of Interest (ROIs) in medical images, such as tumors and lesions in MRI or activation regions in fMRI. A necessary step prior to classification is efficient extraction of discriminative features. For this purpose, we apply a characterization technique especially designed for spatial ROIs. The main idea of this technique is to extract a k-dimensional feature vector using concentric spheres in 3D (or circles in 2D) radiating out of the ROI's center of mass. These vectors form characterization signatures that can be used to represent the initial ROIs. We focus on classifying fMRI ROIs obtained from a study that explores neuroanatomical correlates of semantic processing in Alzheimer's disease (AD). We detect a ROI highly associated with AD and apply the feature extraction technique with different experimental settings. We seek to distinguish control from patient samples. We study how classification can be performed using the extracted signatures as well as how different experimental parameters affect classification accuracy. The obtained classification accuracy ranged from 82% to 87% (based on the selected ROI) suggesting that the proposed classification framework can be potentially useful in supporting medical decision-making.

Age-specific MRI brain and head templates for healthy adults from 20 through 89 years of age

PubMed Central

Fillmore, Paul T.; Phillips-Meek, Michelle C.; Richards, John E.

2015-01-01

This study created and tested a database of adult, age-specific MRI brain and head templates. The participants included healthy adults from 20 through 89 years of age. The templates were done in five-year, 10-year, and multi-year intervals from 20 through 89 years, and consist of average T1W for the head and brain, and segmenting priors for gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF). It was found that age-appropriate templates provided less biased tissue classification estimates than age-inappropriate reference data and reference data based on young adult templates. This database is available for use by other investigators and clinicians for their MRI studies, as well as other types of neuroimaging and electrophysiological research.1 PMID:25904864

Effect of electromagnetic field accompanying the magnetic resonance imaging on human heart rate variability - a pilot study.

PubMed

Derkacz, Arkadiusz; Gawrys, Jakub; Gawrys, Karolina; Podgorski, Maciej; Magott-Derkacz, Agnieszka; Poreba, Rafał; Doroszko, Adrian

2018-06-01

The effect of electromagnetic field on cardiovascular system in the literature is defined in ambiguous way. The aim of this study was to evaluate the effect of electromagnetic field on the heart rate variability (HRV) during the examination with magnetic resonance. Forty-two patients underwent Holter ECG heart monitoring for 30 minutes twice: immediately before and after the examination with magnetic resonance imaging (MRI). HRV was analysed by assessing a few selected time and spectral parameters. Is has been shown that standard deviation of NN intervals (SDNN) and very low frequency rates increased, whereas the low frequency:high frequency parameter significantly decreased following the MRI examination. These results show that MRI may affect the HRV most likely by changing the sympathetic-parasympathetic balance.

Mapping MRI/MRS Parameters with Genetic Over-expression Profiles In Human Prostate Cancer: Demonstrating the Potential

PubMed Central

Lenkinski, Robert E.; Bloch, B. Nicholas; Liu, Fangbing; Frangioni, John V.; Perner, Sven; Rubin, Mark A.; Genega, Elizabeth; Rofsky, Neil M.; Gaston, Sandra M.

2009-01-01

Magnetic resonance imaging (MRI) and MR spectroscopy can probe a variety of physiological (e.g. blood vessel permeability) and metabolic characteristics of prostate cancer. However, little is known about the changes in gene expression that underlie the spectral and imaging features observed in prostate cancer. Tumor induced changes in vascular permeability and angiogenesis are thought to contribute to patterns of dynamic contrast enhanced (DCE) MRI images of prostate cancer even though the genetic basis of tumor vasculogenesis is complex and the specific mechanisms underlying these DCEMRI features have not yet been determined. In order to identify the changes in gene expression that correspond to MRS and DCEMRI patterns in human prostate cancers, we have utilized tissue print micropeel techniques to generate “whole mount” molecular maps of radical prostatectomy specimens that correspond to pre-surgical MRI/MRS studies. These molecular maps include RNA expression profiles from both Affymetrix GeneChip microarrays and quantitative reverse transcriptase PCR (qrt-PCR) analysis, as well as immunohistochemical studies. Using these methods on patients with prostate cancer, we found robust over-expression of choline kinase a in the majority of primary tumors. We also observed overexpression of neuropeptide Y (NPY), a newly identified angiogenic factor, in a subset of DCEMRI positive prostate cancers. These studies set the stage for establishing MRI/MRS parameters as validated biomarkers for human prostate cancer. PMID:18752015

phMRI: methodological considerations for mitigating potential confounding factors

PubMed Central

Bourke, Julius H.; Wall, Matthew B.

2015-01-01

Pharmacological Magnetic Resonance Imaging (phMRI) is a variant of conventional MRI that adds pharmacological manipulations in order to study the effects of drugs, or uses pharmacological probes to investigate basic or applied (e.g., clinical) neuroscience questions. Issues that may confound the interpretation of results from various types of phMRI studies are briefly discussed, and a set of methodological strategies that can mitigate these problems are described. These include strategies that can be employed at every stage of investigation, from study design to interpretation of resulting data, and additional techniques suited for use with clinical populations are also featured. Pharmacological MRI is a challenging area of research that has both significant advantages and formidable difficulties, however with due consideration and use of these strategies many of the key obstacles can be overcome. PMID:25999812

Magnetic resonance imaging of placenta accreta

PubMed Central

Varghese, Binoj; Singh, Navdeep; George, Regi A.N; Gilvaz, Sareena

2013-01-01

Placenta accreta (PA) is a severe pregnancy complication which occurs when the chorionic villi (CV) invade the myometrium abnormally. Optimal management requires accurate prenatal diagnosis. Ultrasonography (USG) and magnetic resonance imaging (MRI) are the modalities for prenatal diagnosis of PA, although USG remains the primary investigation of choice. MRI is a complementary technique and reserved for further characterization when USG is inconclusive or incomplete. Breath-hold T2-weighted half-Fourier rapid acquisition with relaxation enhancement (RARE) and balanced steady-state free precession imaging in the three orthogonal planes is the key MRI technique. Markedly heterogeneous placenta, thick intraplacental dark bands on half-Fourier acquisition single-shot turbo spin-echo (HASTE), and disorganized abnormal intraplacental vascularity are the cardinal MRI features of PA. MRI is less reliable in differentiating between different degrees of placental invasion, especially between accreta vera and increta. PMID:24604945

Functional MRI registration with tissue-specific patch-based functional correlation tensors.

PubMed

Zhou, Yujia; Zhang, Han; Zhang, Lichi; Cao, Xiaohuan; Yang, Ru; Feng, Qianjin; Yap, Pew-Thian; Shen, Dinggang

2018-06-01

Population studies of brain function with resting-state functional magnetic resonance imaging (rs-fMRI) rely on accurate intersubject registration of functional areas. This is typically achieved through registration using high-resolution structural images with more spatial details and better tissue contrast. However, accumulating evidence has suggested that such strategy cannot align functional regions well because functional areas are not necessarily consistent with anatomical structures. To alleviate this problem, a number of registration algorithms based directly on rs-fMRI data have been developed, most of which utilize functional connectivity (FC) features for registration. However, most of these methods usually extract functional features only from the thin and highly curved cortical grey matter (GM), posing great challenges to accurate estimation of whole-brain deformation fields. In this article, we demonstrate that additional useful functional features can also be extracted from the whole brain, not restricted to the GM, particularly the white-matter (WM), for improving the overall functional registration. Specifically, we quantify local anisotropic correlation patterns of the blood oxygenation level-dependent (BOLD) signals using tissue-specific patch-based functional correlation tensors (ts-PFCTs) in both GM and WM. Functional registration is then performed by integrating the features from different tissues using the multi-channel large deformation diffeomorphic metric mapping (mLDDMM) algorithm. Experimental results show that our method achieves superior functional registration performance, compared with conventional registration methods. © 2018 Wiley Periodicals, Inc.

Sentence Syntax and Content in the Human Temporal Lobe: An fMRI Adaptation Study in Auditory and Visual Modalities

ERIC Educational Resources Information Center

Devauchelle, Anne-Dominique; Oppenheim, Catherine; Rizzi, Luigi; Dehaene, Stanislas; Pallier, Christophe

2009-01-01

Priming effects have been well documented in behavioral psycholinguistics experiments: The processing of a word or a sentence is typically facilitated when it shares lexico-semantic or syntactic features with a previously encountered stimulus. Here, we used fMRI priming to investigate which brain areas show adaptation to the repetition of a…

Magnetic resonance imaging and computed tomography characteristics of renal cell carcinoma associated with Xp11.2 translocation/TFE3 gene fusion.

PubMed

Wang, Wei; Ding, Jianhui; Li, Yuan; Wang, Chaofu; Zhou, Liangping; Zhu, Hui; Peng, Weijun

2014-01-01

To characterize Xp11.2 translocation renal cell carcinoma (RCC) using magnetic resonance imaging (MRI) and computed tomography (CT). This study retrospectively collected the MRI and CT data of twelve patients with Xp11.2 translocation RCC confirmed by pathology. Nine cases underwent dynamic contrast-enhanced MRI (DCE-MRI) and 6 cases underwent CT, of which 3 cases underwent MRI and CT simultaneously. The MRI and CT findings were analyzed in regard to tumor position, size, hemorrhagic, cystic or necrotic components, calcification, tumor density, signal intensity and enhancement features. The age of the 12 patients ranged from 13 to 46 years (mean age: 23 years). T2WI revealed heterogeneous intensity, hyper-intensity, and slight hypo-intensity in 6 cases, 2 cases, and 1 case, respectively. On DCE-MR images, mild, moderate, and marked rim enhancement of the tumor in the corticomedullary phase (CMP) were observed in 1, 6, and 2 cases, respectively. The tumor parenchyma showed iso-attenuation (n = 4) or slight hyper-attenuation (n = 1) compared to the normal renal cortex on non-contrast CT images. Imaging findings were suggestive of hemorrhage (n = 4) or necrosis (n = 8) in the tumors, and there was evidence of calcification in 8 cases by CT (n = 3) and pathology (n = 8). On dynamic contrast-enhanced CT images, 3 cases and 1 case manifested moderate and strong CMP enhancement, respectively. Nine tumors by MRI and 4 tumors by CT showed prolonged enhancement. Three neoplasms presented at stage I, 2 at stage II, 3 at stage III, and 4 at stage IV according the 2010 AJCC staging criteria. XP11.2 translocation RCC should be considered when a child or young adult patient presents with a renal tumor with heterogeneous features such as hemorrhage, necrosis, cystic changes, and calcification on CT and MRI and/or is accompanied by metastatic evidence.

Magnetic Resonance Imaging and Computed Tomography Characteristics of Renal Cell Carcinoma Associated with Xp11.2 Translocation/TFE3 Gene Fusion

PubMed Central

Li, Yuan; Wang, Chaofu; Zhou, Liangping; Zhu, Hui; Peng, Weijun

2014-01-01

Purpose To characterize Xp11.2 translocation renal cell carcinoma (RCC) using magnetic resonance imaging (MRI) and computed tomography (CT). Methods This study retrospectively collected the MRI and CT data of twelve patients with Xp11.2 translocation RCC confirmed by pathology. Nine cases underwent dynamic contrast-enhanced MRI (DCE-MRI) and 6 cases underwent CT, of which 3 cases underwent MRI and CT simultaneously. The MRI and CT findings were analyzed in regard to tumor position, size, hemorrhagic, cystic or necrotic components, calcification, tumor density, signal intensity and enhancement features. Results The age of the 12 patients ranged from 13 to 46 years (mean age: 23 years). T2WI revealed heterogeneous intensity, hyper-intensity, and slight hypo-intensity in 6 cases, 2 cases, and 1 case, respectively. On DCE-MR images, mild, moderate, and marked rim enhancement of the tumor in the corticomedullary phase (CMP) were observed in 1, 6, and 2 cases, respectively. The tumor parenchyma showed iso-attenuation (n = 4) or slight hyper-attenuation (n = 1) compared to the normal renal cortex on non-contrast CT images. Imaging findings were suggestive of hemorrhage (n = 4) or necrosis (n = 8) in the tumors, and there was evidence of calcification in 8 cases by CT (n = 3) and pathology (n = 8). On dynamic contrast-enhanced CT images, 3 cases and 1 case manifested moderate and strong CMP enhancement, respectively. Nine tumors by MRI and 4 tumors by CT showed prolonged enhancement. Three neoplasms presented at stage I, 2 at stage II, 3 at stage III, and 4 at stage IV according the 2010 AJCC staging criteria. Conclusions XP11.2 translocation RCC should be considered when a child or young adult patient presents with a renal tumor with heterogeneous features such as hemorrhage, necrosis, cystic changes, and calcification on CT and MRI and/or is accompanied by metastatic evidence. PMID:24926688

4H Leukodystrophy: A Brain Magnetic Resonance Imaging Scoring System.

PubMed

Vrij-van den Bos, Suzanne; Hol, Janna A; La Piana, Roberta; Harting, Inga; Vanderver, Adeline; Barkhof, Frederik; Cayami, Ferdy; van Wieringen, Wessel N; Pouwels, Petra J W; van der Knaap, Marjo S; Bernard, Geneviève; Wolf, Nicole I

2017-06-01

4H (hypomyelination, hypodontia and hypogonadotropic hypogonadism) leukodystrophy (4H) is an autosomal recessive hypomyelinating white matter (WM) disorder with neurologic, dental, and endocrine abnormalities. The aim of this study was to develop and validate a magnetic resonance imaging (MRI) scoring system for 4H. A scoring system (0-54) was developed to quantify hypomyelination and atrophy of different brain regions. Pons diameter and bicaudate ratio were included as measures of cerebral and brainstem atrophy, and reference values were determined using controls. Five independent raters completed the scoring system in 40 brain MRI scans collected from 36 patients with genetically proven 4H. Interrater reliability (IRR) and correlations between MRI scores, age, gross motor function, gender, and mutated gene were assessed. IRR for total MRI severity was found to be excellent (intraclass correlation coefficient: 0.87; 95% confidence interval: 0.80-0.92) but varied between different items with some (e.g., myelination of the cerebellar WM) showing poor IRR. Atrophy increased with age in contrast to hypomyelination scores. MRI scores (global, hypomyelination, and atrophy scores) significantly correlated with clinical handicap ( p  < 0.01 for all three items) and differed between the different genotypes. Our 4H MRI scoring system reliably quantifies hypomyelination and atrophy in patients with 4H, and MRI scores reflect clinical disease severity. Georg Thieme Verlag KG Stuttgart · New York.

Optimized breast MRI functional tumor volume as a biomarker of recurrence-free survival following neoadjuvant chemotherapy.

PubMed

Jafri, Nazia F; Newitt, David C; Kornak, John; Esserman, Laura J; Joe, Bonnie N; Hylton, Nola M

2014-08-01

To evaluate optimal contrast kinetics thresholds for measuring functional tumor volume (FTV) by breast magnetic resonance imaging (MRI) for assessment of recurrence-free survival (RFS). In this Institutional Review Board (IRB)-approved retrospective study of 64 patients (ages 29-72, median age of 48.6) undergoing neoadjuvant chemotherapy (NACT) for breast cancer, all patients underwent pre-MRI1 and postchemotherapy MRI4 of the breast. Tumor was defined as voxels meeting thresholds for early percent enhancement (PEthresh) and early-to-late signal enhancement ratio (SERthresh); and FTV (PEthresh, SERthresh) by summing all voxels meeting threshold criteria and minimum connectivity requirements. Ranges of PEthresh from 50% to 220% and SERthresh from 0.0 to 2.0 were evaluated. A Cox proportional hazard model determined associations between change in FTV over treatment and RFS at different PE and SER thresholds. The plot of hazard ratios for change in FTV from MRI1 to MRI4 showed a broad peak with the maximum hazard ratio and highest significance occurring at PE threshold of 70% and SER threshold of 1.0 (hazard ratio = 8.71, 95% confidence interval 2.86-25.5, P < 0.00015), indicating optimal model fit. Enhancement thresholds affect the ability of MRI tumor volume to predict RFS. The value is robust over a wide range of thresholds, supporting the use of FTV as a biomarker. © 2013 Wiley Periodicals, Inc.

Quantifying bone marrow edema in the rheumatoid cervical spine using magnetic resonance imaging.

PubMed

Suppiah, Ravi; Doyle, Anthony; Rai, Raylynne; Dalbeth, Nicola; Lobo, Maria; Braun, Jürgen; McQueen, Fiona M

2010-08-01

To determine the reliability and feasibility of a new magnetic resonance imaging (MRI) score to quantify bone marrow edema (BME), synovitis, and erosions in the cervical spine of patients with rheumatoid arthritis (RA); and to investigate the correlations among neck pain, clinical markers of RA disease activity, and MRI features of disease activity in the cervical spine. Thirty patients with RA (50% with neck pain) and a Disease Activity Score 28-joint count > 3.2 had an MRI scan of their cervical spine. STIR, VIBE, and T1-weighted postcontrast sequences were used to quantify BME. MRI scans were scored for total BME, synovitis, and erosions using a new scoring method developed by the authors and assessed for reliability and feasibility. Associations between neck pain and clinical markers of disease activity were investigated. BME was present in 14/30 patients; 9/14 (64%) had atlantoaxial BME, 10/14 (71%) had subaxial BME, and 5/14 (36%) had both. Interobserver reliability for total cervical BME score was moderate [intraclass correlation coefficient (ICC) = 0.51]. ICC improved to 0.67 if only the vertebral bodies and dens were considered. There was no correlation between neck pain or clinical measures of RA disease activity and the presence of any MRI features including BME, synovitis, or erosions. Current RA disease activity scores do not identify activity in the cervical spine. An MRI score that quantifies BME, synovitis, and erosions in the cervical spine may provide useful information regarding inflammation and damage. This could alert clinicians to the presence of significant pathology and influence management.

Motion‐related artifacts in structural brain images revealed with independent estimates of in‐scanner head motion

PubMed Central

Savalia, Neil K.; Agres, Phillip F.; Chan, Micaela Y.; Feczko, Eric J.; Kennedy, Kristen M.

2016-01-01

Abstract Motion‐contaminated T1‐weighted (T1w) magnetic resonance imaging (MRI) results in misestimates of brain structure. Because conventional T1w scans are not collected with direct measures of head motion, a practical alternative is needed to identify potential motion‐induced bias in measures of brain anatomy. Head movements during functional MRI (fMRI) scanning of 266 healthy adults (20–89 years) were analyzed to reveal stable features of in‐scanner head motion. The magnitude of head motion increased with age and exhibited within‐participant stability across different fMRI scans. fMRI head motion was then related to measurements of both quality control (QC) and brain anatomy derived from a T1w structural image from the same scan session. A procedure was adopted to “flag” individuals exhibiting excessive head movement during fMRI or poor T1w quality rating. The flagging procedure reliably reduced the influence of head motion on estimates of gray matter thickness across the cortical surface. Moreover, T1w images from flagged participants exhibited reduced estimates of gray matter thickness and volume in comparison to age‐ and gender‐matched samples, resulting in inflated effect sizes in the relationships between regional anatomical measures and age. Gray matter thickness differences were noted in numerous regions previously reported to undergo prominent atrophy with age. Recommendations are provided for mitigating this potential confound, and highlight how the procedure may lead to more accurate measurement and comparison of anatomical features. Hum Brain Mapp 38:472–492, 2017. © 2016 Wiley Periodicals, Inc. PMID:27634551

Defining the learning curve for multiparametric magnetic resonance imaging (MRI) of the prostate using MRI-transrectal ultrasonography (TRUS) fusion-guided transperineal prostate biopsies as a validation tool.

PubMed

Gaziev, Gabriele; Wadhwa, Karan; Barrett, Tristan; Koo, Brendan C; Gallagher, Ferdia A; Serrao, Eva; Frey, Julia; Seidenader, Jonas; Carmona, Lina; Warren, Anne; Gnanapragasam, Vincent; Doble, Andrew; Kastner, Christof

2016-01-01

To determine the accuracy of multiparametric magnetic resonance imaging (mpMRI) during the learning curve of radiologists using MRI targeted, transrectal ultrasonography (TRUS) guided transperineal fusion biopsy (MTTP) for validation. Prospective data on 340 men who underwent mpMRI (T2-weighted and diffusion-weighted MRI) followed by MTTP prostate biopsy, was collected according to Ginsburg Study Group and Standards for Reporting of Diagnostic Accuracy standards. MRI data were reported by two experienced radiologists and scored on a Likert scale. Biopsies were performed by consultant urologists not 'blinded' to the MRI result and men had both targeted and systematic sector biopsies, which were reviewed by a dedicated uropathologist. The cohorts were divided into groups representing five consecutive time intervals in the study. Sensitivity and specificity of positive MRI reports, prostate cancer detection by positive MRI, distribution of significant Gleason score and negative MRI with false negative for prostate cancer were calculated. Data were sequentially analysed and the learning curve was determined by comparing the first and last group. We detected a positive mpMRI in 64 patients from Group A (91%) and 52 patients from Group E (74%). The prostate cancer detection rate on mpMRI increased from 42% (27/64) in Group A to 81% (42/52) in Group E (P < 0.001). The prostate cancer detection rate by targeted biopsy increased from 27% (17/64) in Group A to 63% (33/52) in Group E (P < 0.001). The negative predictive value of MRI for significant cancer (>Gleason 3+3) was 88.9% in Group E compared with 66.6% in Group A. We demonstrate an improvement in detection of prostate cancer for MRI reporting over time, suggesting a learning curve for the technique. With an improved negative predictive value for significant cancer, decision for biopsy should be based on patient/surgeon factors and risk attributes alongside the MRI findings. © 2014 The Authors BJU International © 2014 BJU International Published by John Wiley & Sons Ltd.

Fronto-Temporal Connectivity Predicts ECT Outcome in Major Depression.

PubMed

Leaver, Amber M; Wade, Benjamin; Vasavada, Megha; Hellemann, Gerhard; Joshi, Shantanu H; Espinoza, Randall; Narr, Katherine L

2018-01-01

Electroconvulsive therapy (ECT) is arguably the most effective available treatment for severe depression. Recent studies have used MRI data to predict clinical outcome to ECT and other antidepressant therapies. One challenge facing such studies is selecting from among the many available metrics, which characterize complementary and sometimes non-overlapping aspects of brain function and connectomics. Here, we assessed the ability of aggregated, functional MRI metrics of basal brain activity and connectivity to predict antidepressant response to ECT using machine learning. A radial support vector machine was trained using arterial spin labeling (ASL) and blood-oxygen-level-dependent (BOLD) functional magnetic resonance imaging (fMRI) metrics from n = 46 (26 female, mean age 42) depressed patients prior to ECT (majority right-unilateral stimulation). Image preprocessing was applied using standard procedures, and metrics included cerebral blood flow in ASL, and regional homogeneity, fractional amplitude of low-frequency modulations, and graph theory metrics (strength, local efficiency, and clustering) in BOLD data. A 5-repeated 5-fold cross-validation procedure with nested feature-selection validated model performance. Linear regressions were applied post hoc to aid interpretation of discriminative features. The range of balanced accuracy in models performing statistically above chance was 58-68%. Here, prediction of non-responders was slightly higher than for responders (maximum performance 74 and 64%, respectively). Several features were consistently selected across cross-validation folds, mostly within frontal and temporal regions. Among these were connectivity strength among: a fronto-parietal network [including left dorsolateral prefrontal cortex (DLPFC)], motor and temporal networks (near ECT electrodes), and/or subgenual anterior cingulate cortex (sgACC). Our data indicate that pattern classification of multimodal fMRI metrics can successfully predict ECT outcome, particularly for individuals who will not respond to treatment. Notably, connectivity with networks highly relevant to ECT and depression were consistently selected as important predictive features. These included the left DLPFC and the sgACC, which are both targets of other neurostimulation therapies for depression, as well as connectivity between motor and right temporal cortices near electrode sites. Future studies that probe additional functional and structural MRI metrics and other patient characteristics may further improve the predictive power of these and similar models.

MRI change metrics of facioscapulohumeral muscular dystrophy: Stir and T1.

PubMed

Ferguson, Mark R; Poliachik, Sandra L; Budech, Christopher B; Gove, Nancy E; Carter, Gregory T; Wang, Leo H; Miller, Daniel G; Shaw, Dennis W W; Friedman, Seth D

2018-06-01

MRI evaluation in facioscapulohumeral muscular dystrophy (FSHD) demonstrates fatty replacement and inflammation/edema in muscle. Our previous work demonstrated short T1 inversion recovery (STIR)-hyperintense (STIR+) signal in muscle 2 years before fatty replacement. We evaluated leg muscle STIR changes and fatty replacement within 14 months. FSHD subjects received 2 MRI scans of thigh and calf over a 6.9- to 13.8-month interval. Quality of life measures were collected. One Radiologist rated muscle changes on a semi-quantitative scale. Fifteen subjects completed longitudinal imaging. Four STIR + muscles and 3 STIR-normal (STIR-) muscles were rated as progressing to fatty tissue over the study period. STIR + muscles with confluent regions of fat at baseline increased more in fat, while STIR- muscles had increases in septal-fat over the study period. These changes may reflect two phases of FSHD, demonstrating MRI sensitivity is weighted toward gross pathological phases of the disease. Muscle Nerve 57: 905-912, 2018. © 2017 Wiley Periodicals, Inc.

Longitudinal 2-point dixon muscle magnetic resonance imaging in becker muscular dystrophy.

PubMed

Bonati, Ulrike; Schmid, Maurice; Hafner, Patricia; Haas, Tanja; Bieri, Oliver; Gloor, Monika; Fischmann, Arne; Fischer, Dirk

2015-06-01

Quantitative MRI techniques detect disease progression in myopathies more sensitively than muscle function measures or conventional MRI. To date, only conventional MRI data using visual rating scales are available for measurement of disease progression in Becker muscular dystrophy (BMD). In 3 patients with BMD (mean age 36.8 years), the mean fat fraction (MFF) of the thigh muscles was assessed by MRI at baseline and at 1-year follow-up using a 2-point Dixon approach (2PD). The motor function measurement scale (MFM) was used for clinical assessment. The mean MFF of all muscles at baseline was 61.6% (SD 7.6). It increased by 3.7% to 65.3% (SD 4.7) at follow-up. The severity of muscle involvement varied between various muscle groups. As in other myopathies, 2PD can quantify fatty muscle degeneration in BMD and can detect disease progression in a small sample size and at relatively short imaging intervals. © 2015 Wiley Periodicals, Inc.

Tracking neural coding of perceptual and semantic features of concrete nouns

PubMed Central

Sudre, Gustavo; Pomerleau, Dean; Palatucci, Mark; Wehbe, Leila; Fyshe, Alona; Salmelin, Riitta; Mitchell, Tom

2015-01-01

We present a methodological approach employing magnetoencephalography (MEG) and machine learning techniques to investigate the flow of perceptual and semantic information decodable from neural activity in the half second during which the brain comprehends the meaning of a concrete noun. Important information about the cortical location of neural activity related to the representation of nouns in the human brain has been revealed by past studies using fMRI. However, the temporal sequence of processing from sensory input to concept comprehension remains unclear, in part because of the poor time resolution provided by fMRI. In this study, subjects answered 20 questions (e.g. is it alive?) about the properties of 60 different nouns prompted by simultaneous presentation of a pictured item and its written name. Our results show that the neural activity observed with MEG encodes a variety of perceptual and semantic features of stimuli at different times relative to stimulus onset, and in different cortical locations. By decoding these features, our MEG-based classifier was able to reliably distinguish between two different concrete nouns that it had never seen before. The results demonstrate that there are clear differences between the time course of the magnitude of MEG activity and that of decodable semantic information. Perceptual features were decoded from MEG activity earlier in time than semantic features, and features related to animacy, size, and manipulability were decoded consistently across subjects. We also observed that regions commonly associated with semantic processing in the fMRI literature may not show high decoding results in MEG. We believe that this type of approach and the accompanying machine learning methods can form the basis for further modeling of the flow of neural information during language processing and a variety of other cognitive processes. PMID:22565201

Automatic Denoising of Functional MRI Data: Combining Independent Component Analysis and Hierarchical Fusion of Classifiers

PubMed Central

Salimi-Khorshidi, Gholamreza; Douaud, Gwenaëlle; Beckmann, Christian F; Glasser, Matthew F; Griffanti, Ludovica; Smith, Stephen M

2014-01-01

Many sources of fluctuation contribute to the fMRI signal, and this makes identifying the effects that are truly related to the underlying neuronal activity difficult. Independent component analysis (ICA) - one of the most widely used techniques for the exploratory analysis of fMRI data - has shown to be a powerful technique in identifying various sources of neuronally-related and artefactual fluctuation in fMRI data (both with the application of external stimuli and with the subject “at rest”). ICA decomposes fMRI data into patterns of activity (a set of spatial maps and their corresponding time series) that are statistically independent and add linearly to explain voxel-wise time series. Given the set of ICA components, if the components representing “signal” (brain activity) can be distinguished form the “noise” components (effects of motion, non-neuronal physiology, scanner artefacts and other nuisance sources), the latter can then be removed from the data, providing an effective cleanup of structured noise. Manual classification of components is labour intensive and requires expertise; hence, a fully automatic noise detection algorithm that can reliably detect various types of noise sources (in both task and resting fMRI) is desirable. In this paper, we introduce FIX (“FMRIB’s ICA-based X-noiseifier”), which provides an automatic solution for denoising fMRI data via accurate classification of ICA components. For each ICA component FIX generates a large number of distinct spatial and temporal features, each describing a different aspect of the data (e.g., what proportion of temporal fluctuations are at high frequencies). The set of features is then fed into a multi-level classifier (built around several different Classifiers). Once trained through the hand-classification of a sufficient number of training datasets, the classifier can then automatically classify new datasets. The noise components can then be subtracted from (or regressed out of) the original data, to provide automated cleanup. On conventional resting-state fMRI (rfMRI) single-run datasets, FIX achieved about 95% overall accuracy. On high-quality rfMRI data from the Human Connectome Project, FIX achieves over 99% classification accuracy, and as a result is being used in the default rfMRI processing pipeline for generating HCP connectomes. FIX is publicly available as a plugin for FSL. PMID:24389422

Lateral cephalometric analysis for treatment planning in orthodontics based on MRI compared with radiographs: A feasibility study in children and adolescents

PubMed Central

Lazo Gonzalez, Eduardo; Hilgenfeld, Tim; Kickingereder, Philipp; Bendszus, Martin; Heiland, Sabine; Ozga, Ann-Kathrin; Sommer, Andreas; Lux, Christopher J.; Zingler, Sebastian

2017-01-01

Objective The objective of this prospective study was to evaluate whether magnetic resonance imaging (MRI) is equivalent to lateral cephalometric radiographs (LCR, “gold standard”) in cephalometric analysis. Methods The applied MRI technique was optimized for short scanning time, high resolution, high contrast and geometric accuracy. Prior to orthodontic treatment, 20 patients (mean age ± SD, 13.95 years ± 5.34) received MRI and LCR. MRI datasets were postprocessed into lateral cephalograms. Cephalometric analysis was performed twice by two independent observers for both modalities with an interval of 4 weeks. Eight bilateral and 10 midsagittal landmarks were identified, and 24 widely used measurements (14 angles, 10 distances) were calculated. Statistical analysis was performed by using intraclass correlation coefficient (ICC), Bland-Altman analysis and two one-sided tests (TOST) within the predefined equivalence margin of ± 2°/mm. Results Geometric accuracy of the MRI technique was confirmed by phantom measurements. Mean intraobserver ICC were 0.977/0.975 for MRI and 0.975/0.961 for LCR. Average interobserver ICC were 0.980 for MRI and 0.929 for LCR. Bland-Altman analysis showed high levels of agreement between the two modalities, bias range (mean ± SD) was -0.66 to 0.61 mm (0.06 ± 0.44) for distances and -1.33 to 1.14° (0.06 ± 0.71) for angles. Except for the interincisal angle (p = 0.17) all measurements were statistically equivalent (p < 0.05). Conclusions This study demonstrates feasibility of orthodontic treatment planning without radiation exposure based on MRI. High-resolution isotropic MRI datasets can be transformed into lateral cephalograms allowing reliable measurements as applied in orthodontic routine with high concordance to the corresponding measurements on LCR. PMID:28334054

The functional magnetic resonance imaging (fMRI) procedure as experienced by healthy participants and stroke patients--a pilot study.

PubMed

Szameitat, André J; Shen, Shan; Sterr, Annette

2009-07-31

An important aspect in functional imaging research employing magnetic resonance imaging (MRI) is how participants perceive the MRI scanning itself. For instance, the knowledge of how (un)comfortable MRI scanning is perceived may help institutional review boards (IRBs) or ethics committees to decide on the approval of a study, or researchers to design their experiments. We provide empirical data from our lab gained from 70 neurologically healthy mainly student subjects and from 22 mainly elderly patients suffering from motor deficits after brain damage. All participants took part in various basic research fMRI studies using a 3T MRI scanner. Directly after the scanning, all participants completed a questionnaire assessing their experience with the fMRI procedure. 87.2% of the healthy subjects and 77.3% of the patients rated the MRI procedure as acceptable to comfortable. In healthy subjects, males found the procedure more comfortable, while the opposite was true for patients. 12.1% of healthy subjects considered scanning durations between 30 and 60 min as too long, while no patient considered their 30 min scanning interval as too long. 93.4% of the healthy subjects would like to participate in an fMRI study again, with a significantly lower rate for the subjects who considered the scanning as too long. Further factors, such as inclusion of a diffusion tensor imaging (DTI) scan, age, and study duration had no effect on the questionnaire responses. Of the few negative comments, the main issues were noise, the restriction to keep still for the whole time, and occasional feelings of dizziness. MRI scanning in the basic research setting is an acceptable procedure for elderly and patient participants as well as young healthy subjects.

The functional magnetic resonance imaging (fMRI) procedure as experienced by healthy participants and stroke patients – A pilot study

PubMed Central

2009-01-01

Background An important aspect in functional imaging research employing magnetic resonance imaging (MRI) is how participants perceive the MRI scanning itself. For instance, the knowledge of how (un)comfortable MRI scanning is perceived may help institutional review boards (IRBs) or ethics committees to decide on the approval of a study, or researchers to design their experiments. Methods We provide empirical data from our lab gained from 70 neurologically healthy mainly student subjects and from 22 mainly elderly patients suffering from motor deficits after brain damage. All participants took part in various basic research fMRI studies using a 3T MRI scanner. Directly after the scanning, all participants completed a questionnaire assessing their experience with the fMRI procedure. Results 87.2% of the healthy subjects and 77.3% of the patients rated the MRI procedure as acceptable to comfortable. In healthy subjects, males found the procedure more comfortable, while the opposite was true for patients. 12.1% of healthy subjects considered scanning durations between 30 and 60 min as too long, while no patient considered their 30 min scanning interval as too long. 93.4% of the healthy subjects would like to participate in an fMRI study again, with a significantly lower rate for the subjects who considered the scanning as too long. Further factors, such as inclusion of a diffusion tensor imaging (DTI) scan, age, and study duration had no effect on the questionnaire responses. Of the few negative comments, the main issues were noise, the restriction to keep still for the whole time, and occasional feelings of dizziness. Conclusion MRI scanning in the basic research setting is an acceptable procedure for elderly and patient participants as well as young healthy subjects. PMID:19646238

Lateral cephalometric analysis for treatment planning in orthodontics based on MRI compared with radiographs: A feasibility study in children and adolescents.

PubMed

Heil, Alexander; Lazo Gonzalez, Eduardo; Hilgenfeld, Tim; Kickingereder, Philipp; Bendszus, Martin; Heiland, Sabine; Ozga, Ann-Kathrin; Sommer, Andreas; Lux, Christopher J; Zingler, Sebastian

2017-01-01

The objective of this prospective study was to evaluate whether magnetic resonance imaging (MRI) is equivalent to lateral cephalometric radiographs (LCR, "gold standard") in cephalometric analysis. The applied MRI technique was optimized for short scanning time, high resolution, high contrast and geometric accuracy. Prior to orthodontic treatment, 20 patients (mean age ± SD, 13.95 years ± 5.34) received MRI and LCR. MRI datasets were postprocessed into lateral cephalograms. Cephalometric analysis was performed twice by two independent observers for both modalities with an interval of 4 weeks. Eight bilateral and 10 midsagittal landmarks were identified, and 24 widely used measurements (14 angles, 10 distances) were calculated. Statistical analysis was performed by using intraclass correlation coefficient (ICC), Bland-Altman analysis and two one-sided tests (TOST) within the predefined equivalence margin of ± 2°/mm. Geometric accuracy of the MRI technique was confirmed by phantom measurements. Mean intraobserver ICC were 0.977/0.975 for MRI and 0.975/0.961 for LCR. Average interobserver ICC were 0.980 for MRI and 0.929 for LCR. Bland-Altman analysis showed high levels of agreement between the two modalities, bias range (mean ± SD) was -0.66 to 0.61 mm (0.06 ± 0.44) for distances and -1.33 to 1.14° (0.06 ± 0.71) for angles. Except for the interincisal angle (p = 0.17) all measurements were statistically equivalent (p < 0.05). This study demonstrates feasibility of orthodontic treatment planning without radiation exposure based on MRI. High-resolution isotropic MRI datasets can be transformed into lateral cephalograms allowing reliable measurements as applied in orthodontic routine with high concordance to the corresponding measurements on LCR.

Cost Analysis of the Addition of Hyperacute Magnetic Resonance Imaging for Selection of Patients for Endovascular Stroke Therapy.

PubMed

John, Seby; Thompson, Nicolas R; Lesko, Terry; Papesh, Nancy; Obuchowski, Nancy; Tomic, Dan; Wisco, Dolora; Khawaja, Zeshaun; Uchino, Ken; Man, Shumei; Cheng-Ching, Esteban; Toth, Gabor; Masaryk, Thomas; Ruggieri, Paul; Modic, Michael; Hussain, Muhammad Shazam

2017-10-01

Patient selection is important to determine the best candidates for endovascular stroke therapy. In application of a hyperacute magnetic resonance imaging (MRI) protocol for patient selection, we have shown decreased utilization with improved outcomes. A cost analysis comparing the pre- and post-MRI protocol time periods was performed to determine if the previous findings translated into cost opportunities. We retrospectively identified individuals considered for endovascular stroke therapy from January 2008 to August 2012 who were ≤8 h from stroke symptoms onset. Patients prior to April 30, 2010 were selected based on results of the computed tomography/computed tomography angiography alone (pre-hyperacute), whereas patients after April 30, 2010 were selected based on results of MRI (post-hyperacute MRI). Demographic, outcome, and financial information was collected. Log-transformed average daily direct costs were regressed on time period. The regression model included demographic and clinical covariates as potential confounders. Multiple imputation was used to account for missing data. We identified 267 patients in our database (88 patients in pre-hyperacute MRI period, 179 in hyperacute MRI protocol period). Patient length of stay was not significantly different in the hyperacute MRI protocol period as compared to the pre-hyperacute MRI period (10.6 vs. 9.9 days, p < 0.42). The median of average daily direct costs was reduced by 24.5% (95% confidence interval 14.1-33.7%, p < 0.001). Use of the hyperacute MRI protocol translated into reduced costs, in addition to reduced utilization and better outcomes. MRI selection of patients is an effective strategy, both for patients and hospital systems.

Ultrasound-based logistic regression model LR2 versus magnetic resonance imaging for discriminating between benign and malignant adnexal masses: a prospective study.

PubMed

Shimada, Kanane; Matsumoto, Koji; Mimura, Takashi; Ishikawa, Tetsuya; Munechika, Jiro; Ohgiya, Yoshimitsu; Kushima, Miki; Hirose, Yusuke; Asami, Yuka; Iitsuka, Chiaki; Miyamoto, Shingo; Onuki, Mamiko; Tsunoda, Hajime; Matsuoka, Ryu; Ichizuka, Kiyotake; Sekizawa, Akihiko

2018-06-01

The diagnostic performances of the International Ovarian Tumor Analysis (IOTA) ultrasound-based logistic regression model (LR2) and magnetic resonance imaging (MRI) in discriminating between benign and malignant adnexal masses have not been directly compared in a single study. Using the IOTA LR2 model and subjective interpretation of MRI findings by experienced radiologists, 265 consecutive patients with adnexal masses were preoperatively evaluated in two hospitals between February 2014 and December 2015. Definitive histological diagnosis of excised tissues was used as a gold standard. From the 265 study subjects, 54 (20.4%) tumors were histologically diagnosed as malignant (including 11 borderline and 3 metastatic tumors). Preoperative diagnoses of malignant tumors showed 91.7% total agreement between IOTA LR2 and MRI, with a kappa value of 0.77 [95% confidence interval (CI), 0.68-0.86]. Sensitivity of IOTA LR2 (0.94, 95% CI, 0.85-0.98) for predicting malignant tumors was similar to that of MRI (0.96, 95% CI, 0.87-0.99; P = 0.99), whereas specificity of IOTA LR2 (0.98, 95% CI, 0.95-0.99) was significantly higher than that of MRI (0.91, 95% CI, 0.87-0.95; P = 0.002). Combined IOTA LR2 and MRI results gave the greatest sensitivity (1.00, 95% CI, 0.93-1.00) and had similar specificity (0.91, 95% CI, 0.86-0.94) to MRI. The IOTA LR2 model had a similar sensitivity to MRI for discriminating between benign and malignant tumors and a higher specificity compared with MRI. Our findings suggest that the IOTA LR2 model, either alone or in conjunction with MRI, should be included in preoperative evaluation of adnexal masses.

Integrated imaging using MRI and 123I metaiodobenzylguanidine scintigraphy to improve sensitivity and specificity in the diagnosis of pediatric neuroblastoma.

PubMed

Pfluger, Thomas; Schmied, Christoph; Porn, Ute; Leinsinger, Gerda; Vollmar, Christian; Dresel, Stefan; Schmid, Irene; Hahn, Klaus

2003-10-01

The objectives of this study were to compare MRI and iodine-123 ((123)I) metaiodobenzylguanidine (MIBG) scintigraphy in the detection of neuroblastoma lesions in pediatric patients and to assess the additional value of combined imaging. Fifty MRI and 50 (123)I MIBG examinations (mean interval, 6.4 days) were analyzed retrospectively with regard to suspected or proven neuroblastoma lesions (n = 193) in 28 patients. MRI and MIBG scans were reviewed by two independent observers each. Separate and combined analyses of MRI and MIBG scintigraphy were compared with clinical and histologic findings. With regard to the diagnosis of neuroblastoma lesion, MIBG scintigraphy, MRI, and combined analysis showed a sensitivity of 69%, 86%, and 99% and a specificity of 85%, 77%, and 95%, respectively. On MRI, 15 false-positive findings were recorded: posttherapeutic reactive changes (n = 10), benign adrenal tumors (n = 3), and enlarged lymph nodes (n = 2). On MIBG scintigraphy, 10 false-positive findings occurred: ganglioneuromas (n = 2), benign liver tumors (n = 2), and physiologic uptake (n = 6). Thirteen neuroblastoma metastases and two residual masses under treatment with chemotherapy were judged to be false-negative findings on MRI. Two primary or residual neuroblastomas and one orbital metastasis were misinterpreted as Wilms' tumor, reactive changes after surgery, and rhabdomyosarcoma on MRI. Thirty-two bone metastases, six other neuroblastoma metastases, and one adrenal neuroblastoma showed no MIBG uptake. On combined imaging, one false-negative (bone metastasis) and three false-positive (two ganglioneuromas and one pheochromocytoma) findings remained. In the assessment of neuroblastoma lesions in pediatric patients, MRI showed a higher sensitivity and MIBG scintigraphy a higher specificity. However, integrated imaging showed an increase in both sensitivity and specificity.

Multiparametric Magnetic Resonance Imaging of the Prostate for Tumour Detection and Local Staging: Imaging in 1.5T and Histopathologic Correlation.

PubMed

Loggitsi, Dimitra; Gyftopoulos, Anastasios; Economopoulos, Nikolaos; Apostolaki, Aikaterini; Kalogeropoulos, Theodoros; Thanos, Anastasios; Alexopoulou, Efthimia; Kelekis, Nikolaos L

2017-11-01

The study sought to prospectively evaluate which technique among T2-weighted images, dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI), diffusion-weighted (DW) MRI, or a combination of the 2, is best suited for prostate cancer detection and local staging. Twenty-seven consecutive patients with biopsy-proven adenocarcinoma of the prostate underwent MRI on a 1.5T scanner with a surface phased-array coil prior radical prostatectomy. Combined anatomical and functional imaging was performed with the use of T2-weighted sequences, DCE MRI, and DW MRI. We compared the imaging results with whole mount histopathology. For the multiparametric approach, significantly higher sensitivity values, that is, 53% (95% confidence interval [CI]: 41.0-64.1) were obtained as compared with each modality alone or any combination of the 3 modalities (P < .05). The specificity for this multiparametric approach, being 90.3% (95% CI: 86.3-93.3) was not significantly higher (P < .05) as compared with the values of the combination of T2+DCE MRI, DW+DCE MRI, or DCE MRI alone. Among the 3 techniques, DCE had the best performance for tumour detection in both the peripheral and the transition zone. High negative predictive value rates (>86%) were obtained for both tumour detection and local staging. The combination of T2-weighted sequences, DCE MRI, and DW MRI yields higher diagnostic performance for tumour detection and local staging than can any of these techniques alone or even any combination of them. Copyright © 2017 Canadian Association of Radiologists. Published by Elsevier Inc. All rights reserved.

Dynamic detection-rate-based bit allocation with genuine interval concealment for binary biometric representation.

PubMed

Lim, Meng-Hui; Teoh, Andrew Beng Jin; Toh, Kar-Ann

2013-06-01

Biometric discretization is a key component in biometric cryptographic key generation. It converts an extracted biometric feature vector into a binary string via typical steps such as segmentation of each feature element into a number of labeled intervals, mapping of each interval-captured feature element onto a binary space, and concatenation of the resulted binary output of all feature elements into a binary string. Currently, the detection rate optimized bit allocation (DROBA) scheme is one of the most effective biometric discretization schemes in terms of its capability to assign binary bits dynamically to user-specific features with respect to their discriminability. However, we learn that DROBA suffers from potential discriminative feature misdetection and underdiscretization in its bit allocation process. This paper highlights such drawbacks and improves upon DROBA based on a novel two-stage algorithm: 1) a dynamic search method to efficiently recapture such misdetected features and to optimize the bit allocation of underdiscretized features and 2) a genuine interval concealment technique to alleviate crucial information leakage resulted from the dynamic search. Improvements in classification accuracy on two popular face data sets vindicate the feasibility of our approach compared with DROBA.

Use of clinical and neuroimaging characteristics to distinguish temporal lobe herpes simplex encephalitis from its mimics.

PubMed

Chow, Felicia C; Glaser, Carol A; Sheriff, Heather; Xia, Dongxiang; Messenger, Sharon; Whitley, Richard; Venkatesan, Arun

2015-05-01

We describe the spectrum of etiologies associated with temporal lobe (TL) encephalitis and identify clinical and radiologic features that distinguish herpes simplex encephalitis (HSE) from its mimics. We reviewed all adult cases of encephalitis with TL abnormalities on magnetic resonance imaging (MRI) from the California Encephalitis Project. We evaluated the association between specific clinical and MRI characteristics and HSE compared with other causes of TL encephalitis and used multivariate logistic modeling to identify radiologic predictors of HSE. Of 251 cases of TL encephalitis, 43% had an infectious etiology compared with 16% with a noninfectious etiology. Of infectious etiologies, herpes simplex virus was the most commonly identified agent (n = 60), followed by tuberculosis (n = 8) and varicella zoster virus (n = 7). Of noninfectious etiologies, more than half (n = 21) were due to autoimmune disease. Patients with HSE were older (56.8 vs 50.2 years; P = .012), more likely to be white (53% vs 35%; P = .013), more likely to present acutely (88% vs 64%; P = .001) and with a fever (80% vs 49%; P < .001), and less likely to present with a rash (2% vs 15%; P = .010). In a multivariate model, bilateral TL involvement (odds ratio [OR], 0.38; 95% confidence interval [CI], .18-.79; P = .010) and lesions outside the TL, insula, or cingulate (OR, 0.37; 95% CI, .18-.74; P = .005) were associated with lower odds of HSE. In addition to HSE, other infectious and noninfectious etiologies should be considered in the differential diagnosis for TL encephalitis, depending on the presentation. Specific clinical and imaging features may aid in distinguishing HSE from non-HSE causes of TL encephalitis. © The Author 2015. Published by Oxford University Press on behalf of the Infectious Diseases Society of America. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Breast MRI BI-RADS assessments and abnormal interpretation rates by clinical indication in US community practices.

PubMed

Lee, Christoph I; Ichikawa, Laura; Rochelle, Michele C; Kerlikowske, Karla; Miglioretti, Diana L; Sprague, Brian L; DeMartini, Wendy B; Wernli, Karen J; Joe, Bonnie N; Yankaskas, Bonnie C; Lehman, Constance D

2014-11-01

As breast magnetic resonance imaging (MRI) use grows, benchmark performance parameters are needed for auditing and quality assurance purposes. We describe the variation in breast MRI abnormal interpretation rates (AIRs) by clinical indication among a large sample of US community practices. We analyzed data from 41 facilities across five Breast Cancer Surveillance Consortium imaging registries. Each registry obtained institutional review board approval for this Health Insurance Portability and Accountability Act compliant analysis. We included 11,654 breast MRI examinations conducted in 2005-2010 among women aged 18-79 years. We categorized clinical indications as 1) screening, 2) extent of disease, 3) diagnostic (eg, breast symptoms), and 4) other (eg, short-interval follow-up). We characterized assessments as positive (ie, Breast Imaging Reporting and Data System [BI-RADS] 0, 4, and 5) or negative (ie, BI-RADS 1, 2, and 6) and provide results with BI-RADS 3 categorized as positive and negative. We tested for differences in AIRs across clinical indications both unadjusted and adjusted for patient characteristics and registry and assessed for changes in AIRs by year within each clinical indication. When categorizing BI-RADS 3 as positive, AIRs were 21.0% (95% confidence interval [CI], 19.8-22.3) for screening, 31.7% (95% CI, 29.6-33.8) for extent of disease, 29.7% (95% CI, 28.3-31.1) for diagnostic, and 27.4% (95% CI, 25.0-29.8) for other indications (P < .0001). When categorizing BI-RADS 3 as negative, AIRs were 10.5% (95% CI, 9.5-11.4) for screening, 21.8% (95% CI, 19.9-23.6) for extent of disease, 17.7% (95% CI, 16.5-18.8) for diagnostic, and 13.3% (95% CI, 11.6-15.2) for other indications (P < .0001). The significant differences in AIRs by indication persisted even after adjusting for patient characteristics and registry (P < .0001). In addition, for most indications, there were no significant changes in AIRs over time. Breast MRI AIRs differ significantly by clinical indication. Practices should stratify breast MRI examinations by indication for quality assurance and auditing purposes. Copyright © 2014 AUR. Published by Elsevier Inc. All rights reserved.

Mastocytosis: magnetic resonance imaging patterns of marrow disease.

PubMed

Avila, N A; Ling, A; Metcalfe, D D; Worobec, A S

1998-03-01

To report the bone marrow MRI findings of patients with mastocytosis and correlate them with clinical, pathologic, and radiographic features. Eighteen patients with mastocytosis had T1-weighted spin echo and short tau inversion recovery MRI of the pelvis at 0.5 T. In each patient the MR pattern of marrow disease was classified according to intensity and uniformity and was correlated with the clinical category of mastocytosis, bone marrow biopsy results, and radiographic findings. Two patients had normal MRI scans and normal bone marrow biopsies. One patient had a normal MRI scan and a marrow biopsy consistent with mastocytosis. Fifteen patients had abnormal MRI scans and abnormal marrow biopsies. There were several different MR patterns of marrow involvement; none was specifically associated with any given clinical category of mastocytosis. Fifteen of the 18 patients had radiographs of the pelvis; of those, 13 with abnormal MRI scans and abnormal marrow biopsies had the following radiographic findings: normal (nine); sclerosis (three); diffuse osteopenia (one). While radiographs are very insensitive for the detection of marrow abnormalities in mastocytosis, MRI is very sensitive and may display several different patterns of marrow involvement.

MULTIMODAL CLASSIFICATION OF DEMENTIA USING FUNCTIONAL DATA, ANATOMICAL FEATURES AND 3D INVARIANT SHAPE DESCRIPTORS

PubMed Central

Mikhno, Arthur; Nuevo, Pablo Martinez; Devanand, Davangere P.; Parsey, Ramin V.; Laine, Andrew F.

2013-01-01

Multimodality classification of Alzheimer’s disease (AD) and its prodromal stage, Mild Cognitive Impairment (MCI), is of interest to the medical community. We improve on prior classification frameworks by incorporating multiple features from MRI and PET data obtained with multiple radioligands, fluorodeoxyglucose (FDG) and Pittsburg compound B (PIB). We also introduce a new MRI feature, invariant shape descriptors based on 3D Zernike moments applied to the hippocampus region. Classification performance is evaluated on data from 17 healthy controls (CTR), 22 MCI, and 17 AD subjects. Zernike significantly outperforms volume, accuracy (Zernike to volume): CTR/AD (90.7% to 71.6%), CTR/MCI (76.2% to 60.0%), MCI/AD (84.3% to 65.5%). Zernike also provides comparable and complementary performance to PET. Optimal accuracy is achieved when Zernike and PET features are combined (accuracy, specificity, sensitivity), CTR/AD (98.8%, 99.5%, 98.1%), CTR/MCI (84.3%, 82.9%, 85.9%) and MCI/AD (93.3%, 93.6%, 93.3%). PMID:24576927

MULTIMODAL CLASSIFICATION OF DEMENTIA USING FUNCTIONAL DATA, ANATOMICAL FEATURES AND 3D INVARIANT SHAPE DESCRIPTORS.

PubMed

Mikhno, Arthur; Nuevo, Pablo Martinez; Devanand, Davangere P; Parsey, Ramin V; Laine, Andrew F

2012-01-01

Multimodality classification of Alzheimer's disease (AD) and its prodromal stage, Mild Cognitive Impairment (MCI), is of interest to the medical community. We improve on prior classification frameworks by incorporating multiple features from MRI and PET data obtained with multiple radioligands, fluorodeoxyglucose (FDG) and Pittsburg compound B (PIB). We also introduce a new MRI feature, invariant shape descriptors based on 3D Zernike moments applied to the hippocampus region. Classification performance is evaluated on data from 17 healthy controls (CTR), 22 MCI, and 17 AD subjects. Zernike significantly outperforms volume, accuracy (Zernike to volume): CTR/AD (90.7% to 71.6%), CTR/MCI (76.2% to 60.0%), MCI/AD (84.3% to 65.5%). Zernike also provides comparable and complementary performance to PET. Optimal accuracy is achieved when Zernike and PET features are combined (accuracy, specificity, sensitivity), CTR/AD (98.8%, 99.5%, 98.1%), CTR/MCI (84.3%, 82.9%, 85.9%) and MCI/AD (93.3%, 93.6%, 93.3%).

Nitroxide-Based Macromolecular Contrast Agents with Unprecedented Transverse Relaxivity and Stability for Magnetic Resonance Imaging of Tumors

PubMed Central

2017-01-01

Metal-free magnetic resonance imaging (MRI) agents could overcome the established toxicity associated with metal-based agents in some patient populations and enable new modes of functional MRI in vivo. Herein, we report nitroxide-functionalized brush-arm star polymer organic radical contrast agents (BASP-ORCAs) that overcome the low contrast and poor in vivo stability associated with nitroxide-based MRI contrast agents. As a consequence of their unique nanoarchitectures, BASP-ORCAs possess per-nitroxide transverse relaxivities up to ∼44-fold greater than common nitroxides, exceptional stability in highly reducing environments, and low toxicity. These features combine to provide for accumulation of a sufficient concentration of BASP-ORCA in murine subcutaneous tumors up to 20 h following systemic administration such that MRI contrast on par with metal-based agents is observed. BASP-ORCAs are, to our knowledge, the first nitroxide MRI contrast agents capable of tumor imaging over long time periods using clinical high-field 1H MRI techniques. PMID:28776023

A Pictorial Review of Hepatobiliary Magnetic Resonance Imaging With Hepatocyte-Specific Contrast Agents: Uses, Findings, and Pitfalls of Gadoxetate Disodium and Gadobenate Dimeglumine.

PubMed

Scali, Elena P; Walshe, Triona; Tiwari, Hina Arif; Harris, Alison C; Chang, Silvia D

2017-08-01

Magnetic resonance imaging (MRI) has a well-established role as a highly specific and accurate modality for characterizing benign and malignant focal liver lesions. In particular, contrast-enhanced MRI using hepatocyte-specific contrast agents (HSCAs) improves lesion detection and characterization compared to other imaging modalities and MRI techniques. In this pictorial review, the mechanism of action of gadolinium-based MRI contrast agents, with a focus on HSCAs, is described. The clinical indications, protocols, and emerging uses of the 2 commercially available combined contrast agents available in the United States, gadoxetate disodium and gadobenate dimeglumine, are discussed. The MRI features of these agents are compared with examples of focal hepatic masses, many of which have been obtained within the same patient therefore allowing direct lesion comparison. Finally, the pitfalls in the use of combined contrast agents in liver MRI are highlighted. Copyright © 2016 Canadian Association of Radiologists. Published by Elsevier Inc. All rights reserved.

Comparison of breast DCE-MRI contrast time points for predicting response to neoadjuvant chemotherapy using deep convolutional neural network features with transfer learning

NASA Astrophysics Data System (ADS)

Huynh, Benjamin Q.; Antropova, Natasha; Giger, Maryellen L.

2017-03-01

DCE-MRI datasets have a temporal aspect to them, resulting in multiple regions of interest (ROIs) per subject, based on contrast time points. It is unclear how the different contrast time points vary in terms of usefulness for computer-aided diagnosis tasks in conjunction with deep learning methods. We thus sought to compare the different DCE-MRI contrast time points with regard to how well their extracted features predict response to neoadjuvant chemotherapy within a deep convolutional neural network. Our dataset consisted of 561 ROIs from 64 subjects. Each subject was categorized as a non-responder or responder, determined by recurrence-free survival. First, features were extracted from each ROI using a convolutional neural network (CNN) pre-trained on non-medical images. Linear discriminant analysis classifiers were then trained on varying subsets of these features, based on their contrast time points of origin. Leave-one-out cross validation (by subject) was used to assess performance in the task of estimating probability of response to therapy, with area under the ROC curve (AUC) as the metric. The classifier trained on features from strictly the pre-contrast time point performed the best, with an AUC of 0.85 (SD = 0.033). The remaining classifiers resulted in AUCs ranging from 0.71 (SD = 0.028) to 0.82 (SD = 0.027). Overall, we found the pre-contrast time point to be the most effective at predicting response to therapy and that including additional contrast time points moderately reduces variance.

Automated Segmentation of the Parotid Gland Based on Atlas Registration and Machine Learning: A Longitudinal MRI Study in Head-and-Neck Radiation Therapy

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

Yang, Xiaofeng; Wu, Ning; Cheng, Guanghui

Purpose: To develop an automated magnetic resonance imaging (MRI) parotid segmentation method to monitor radiation-induced parotid gland changes in patients after head and neck radiation therapy (RT). Methods and Materials: The proposed method combines the atlas registration method, which captures the global variation of anatomy, with a machine learning technology, which captures the local statistical features, to automatically segment the parotid glands from the MRIs. The segmentation method consists of 3 major steps. First, an atlas (pre-RT MRI and manually contoured parotid gland mask) is built for each patient. A hybrid deformable image registration is used to map the pre-RTmore » MRI to the post-RT MRI, and the transformation is applied to the pre-RT parotid volume. Second, the kernel support vector machine (SVM) is trained with the subject-specific atlas pair consisting of multiple features (intensity, gradient, and others) from the aligned pre-RT MRI and the transformed parotid volume. Third, the well-trained kernel SVM is used to differentiate the parotid from surrounding tissues in the post-RT MRIs by statistically matching multiple texture features. A longitudinal study of 15 patients undergoing head and neck RT was conducted: baseline MRI was acquired prior to RT, and the post-RT MRIs were acquired at 3-, 6-, and 12-month follow-up examinations. The resulting segmentations were compared with the physicians' manual contours. Results: Successful parotid segmentation was achieved for all 15 patients (42 post-RT MRIs). The average percentage of volume differences between the automated segmentations and those of the physicians' manual contours were 7.98% for the left parotid and 8.12% for the right parotid. The average volume overlap was 91.1% ± 1.6% for the left parotid and 90.5% ± 2.4% for the right parotid. The parotid gland volume reduction at follow-up was 25% at 3 months, 27% at 6 months, and 16% at 12 months. Conclusions: We have validated our automated parotid segmentation algorithm in a longitudinal study. This segmentation method may be useful in future studies to address radiation-induced xerostomia in head and neck radiation therapy.« less

Characterization of the collagen component of cartilage repair tissue of the talus with quantitative MRI: comparison of T2 relaxation time measurements with a diffusion-weighted double-echo steady-state sequence (dwDESS).

PubMed

Kretzschmar, M; Bieri, O; Miska, M; Wiewiorski, M; Hainc, N; Valderrabano, V; Studler, U

2015-04-01

The purpose of this study was to characterize the collagen component of repair tissue (RT) of the talus after autologous matrix-induced chondrogenesis (AMIC) using quantitative T2 and diffusion-weighted imaging. Mean T2 values and diffusion coefficients of AMIC-RT and normal cartilage of the talus of 25 patients with posttraumatic osteochondral lesions and AMIC repair were compared in a cross-sectional design using partially spoiled steady-state free precession (pSSFP) for T2 quantification, and diffusion-weighted double-echo steady-state (dwDESS) for diffusion measurement. RT and cartilage were graded with modified Noyes and MOCART scores on morphological sequences. An association between follow-up interval and quantitative MRI measures was assessed using multivariate regression, after stratifying the cohort according to time interval between surgery and MRI. Mean T2 of the AMIC-RT and cartilage were 43.1 ms and 39.1 ms, respectively (p = 0.26). Mean diffusivity of the RT (1.76 μm(2)/ms) was significantly higher compared to normal cartilage (1.46 μm(2)/ms) (p = 0.0092). No correlation was found between morphological and quantitative parameters. RT diffusivity was lowest in the subgroup with follow-up >28 months (p = 0.027). Compared to T2-mapping, dwDESS demonstrated greater sensitivity in detecting differences in the collagen matrix between AMIC-RT and cartilage. Decreased diffusivity in patients with longer follow-up times may indicate an increased matrix organization of RT. • MRI is used to assess morphology of the repair tissue during follow-up. • Quantitative MRI allows an estimation of biochemical properties of the repair tissue. • Differences between repair tissue and cartilage were more significant with dwDESS than T2 mapping.

Differentiation Between Luminal-A and Luminal-B Breast Cancer Using Intravoxel Incoherent Motion and Dynamic Contrast-Enhanced Magnetic Resonance Imaging.

PubMed

Kawashima, Hiroko; Miyati, Tosiaki; Ohno, Naoki; Ohno, Masako; Inokuchi, Masafumi; Ikeda, Hiroko; Gabata, Toshifumi

2017-12-01

The study aimed to investigate whether intravoxel incoherent motion (IVIM) and dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) can differentiate luminal-B from luminal-A breast cancer MATERIALS AND METHODS: Biexponential analyses of IVIM and DCE MRI were performed using a 3.0-T MRI scanner, involving 134 patients with 137 pathologically confirmed luminal-type invasive breast cancers. Luminal-type breast cancer was categorized as luminal-B breast cancer (LBBC, Ki-67 ≧ 14%) or luminal-A breast cancer (LABC, Ki-67 < 14%). Quantitative parameters from IVIM (pure diffusion coefficient [D], perfusion-related diffusion coefficient [D*], and fraction [f]) and DCE MRI (initial percentage of enhancement and signal enhancement ratio [SER]) were calculated. The apparent diffusion coefficient (ADC) was also calculated using monoexponential fitting. We correlated these data with the Ki-67 status. The D and ADC values of LBBC were significantly lower than those of LABC (P = 0.028, P = 0.037). The SER of LBBC was significantly higher than that of LABC (P = 0.004). A univariate analysis showed that a significantly lower D (<0.847 x 10 -3 mm 2 /s), lower ADC (<0.960 × 10 -3 mm 2 /s), and higher SER (>1.071) values were associated with LBBC (all P values <0.01), compared to LABC. In a multivariate analysis, a higher SER (>1.071; odds ratio: 3.0099, 95% confidence interval: 1.4246-6.3593; P = 0.003) value and a lower D (<0.847 × 10 -3 mm 2 /s; odds ratio: 2.6878, 95% confidence interval: 1.0445-6.9162; P = 0.040) value were significantly associated with LBBC, compared to LABC. The SER derived from DCE MRI and the D derived from IVIM are associated independently with the Ki-67 status in patients with luminal-type breast cancer. Copyright © 2017 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.

Rotator cuff tear shape characterization: a comparison of two-dimensional imaging and three-dimensional magnetic resonance reconstructions.

PubMed

Gyftopoulos, Soterios; Beltran, Luis S; Gibbs, Kevin; Jazrawi, Laith; Berman, Phillip; Babb, James; Meislin, Robert

2016-01-01

The purpose of this study was to see if 3-dimensional (3D) magnetic resonance imaging (MRI) could improve our understanding of rotator cuff tendon tear shapes. We believed that 3D MRI would be more accurate than two-dimensional (2D) MRI for classifying tear shapes. We performed a retrospective review of MRI studies of patients with arthroscopically proven full-thickness rotator cuff tears. Two orthopedic surgeons reviewed the information for each case, including scope images, and characterized the shape of the cuff tear into crescent, longitudinal, U- or L-shaped longitudinal, and massive type. Two musculoskeletal radiologists reviewed the corresponding MRI studies independently and blind to the arthroscopic findings and characterized the shape on the basis of the tear's retraction and size using 2D MRI. The 3D reconstructions of each cuff tear were reviewed by each radiologist to characterize the shape. Statistical analysis included 95% confidence intervals and intraclass correlation coefficients. The study reviewed 34 patients. The accuracy for differentiating between crescent-shaped, longitudinal, and massive tears using measurements on 2D MRI was 70.6% for reader 1 and 67.6% for reader 2. The accuracy for tear shape characterization into crescent and longitudinal U- or L-shaped using 3D MRI was 97.1% for reader 1 and 82.4% for reader 2. When further characterizing the longitudinal tears as massive or not using 3D MRI, both readers had an accuracy of 76.9% (10 of 13). The overall accuracy of 3D MRI was 82.4% (56 of 68), significantly different (P = .021) from 2D MRI accuracy (64.7%). Our study has demonstrated that 3D MR reconstructions of the rotator cuff improve the accuracy of characterizing rotator cuff tear shapes compared with current 2D MRI-based techniques. Copyright © 2016 Journal of Shoulder and Elbow Surgery Board of Trustees. Published by Elsevier Inc. All rights reserved.

The reliability of continuous brain responses during naturalistic listening to music.

PubMed

Burunat, Iballa; Toiviainen, Petri; Alluri, Vinoo; Bogert, Brigitte; Ristaniemi, Tapani; Sams, Mikko; Brattico, Elvira

2016-01-01

Low-level (timbral) and high-level (tonal and rhythmical) musical features during continuous listening to music, studied by functional magnetic resonance imaging (fMRI), have been shown to elicit large-scale responses in cognitive, motor, and limbic brain networks. Using a similar methodological approach and a similar group of participants, we aimed to study the replicability of previous findings. Participants' fMRI responses during continuous listening of a tango Nuevo piece were correlated voxelwise against the time series of a set of perceptually validated musical features computationally extracted from the music. The replicability of previous results and the present study was assessed by two approaches: (a) correlating the respective activation maps, and (b) computing the overlap of active voxels between datasets at variable levels of ranked significance. Activity elicited by timbral features was better replicable than activity elicited by tonal and rhythmical ones. These results indicate more reliable processing mechanisms for low-level musical features as compared to more high-level features. The processing of such high-level features is probably more sensitive to the state and traits of the listeners, as well as of their background in music. Copyright © 2015 Elsevier Inc. All rights reserved.

Body Imaging

NASA Technical Reports Server (NTRS)

1990-01-01

Magnetic Resonance Imaging (MRI) and Computer-aided Tomography (CT) images are often complementary. In most cases, MRI is good for viewing soft tissue but not bone, while CT images are good for bone but not always good for soft tissue discrimination. Physicians and engineers in the Department of Radiology at the University of Michigan Hospitals are developing a technique for combining the best features of MRI and CT scans to increase the accuracy of discriminating one type of body tissue from another. One of their research tools is a computer program called HICAP. The program can be used to distinguish between healthy and diseased tissue in body images.

PREDICTION OF MALIGNANT BREAST LESIONS FROM MRI FEATURES: A COMPARISON OF ARTIFICIAL NEURAL NETWORK AND LOGISTIC REGRESSION TECHNIQUES

PubMed Central

McLaren, Christine E.; Chen, Wen-Pin; Nie, Ke; Su, Min-Ying

2009-01-01

Rationale and Objectives Dynamic contrast enhanced MRI (DCE-MRI) is a clinical imaging modality for detection and diagnosis of breast lesions. Analytical methods were compared for diagnostic feature selection and performance of lesion classification to differentiate between malignant and benign lesions in patients. Materials and Methods The study included 43 malignant and 28 benign histologically-proven lesions. Eight morphological parameters, ten gray level co-occurrence matrices (GLCM) texture features, and fourteen Laws’ texture features were obtained using automated lesion segmentation and quantitative feature extraction. Artificial neural network (ANN) and logistic regression analysis were compared for selection of the best predictors of malignant lesions among the normalized features. Results Using ANN, the final four selected features were compactness, energy, homogeneity, and Law_LS, with area under the receiver operating characteristic curve (AUC) = 0.82, and accuracy = 0.76. The diagnostic performance of these 4-features computed on the basis of logistic regression yielded AUC = 0.80 (95% CI, 0.688 to 0.905), similar to that of ANN. The analysis also shows that the odds of a malignant lesion decreased by 48% (95% CI, 25% to 92%) for every increase of 1 SD in the Law_LS feature, adjusted for differences in compactness, energy, and homogeneity. Using logistic regression with z-score transformation, a model comprised of compactness, NRL entropy, and gray level sum average was selected, and it had the highest overall accuracy of 0.75 among all models, with AUC = 0.77 (95% CI, 0.660 to 0.880). When logistic modeling of transformations using the Box-Cox method was performed, the most parsimonious model with predictors, compactness and Law_LS, had an AUC of 0.79 (95% CI, 0.672 to 0.898). Conclusion The diagnostic performance of models selected by ANN and logistic regression was similar. The analytic methods were found to be roughly equivalent in terms of predictive ability when a small number of variables were chosen. The robust ANN methodology utilizes a sophisticated non-linear model, while logistic regression analysis provides insightful information to enhance interpretation of the model features. PMID:19409817

Swiss cheese striatum: clinical implications.

PubMed

Burnett, Melinda S; Witte, Robert J; Ahlskog, J Eric

2014-06-01

Markedly enlarged Virchow-Robin spaces throughout the striatum appear occasionally on magnetic resonance imaging (MRI) scans of the elderly, and this type of striatum is known as the Swiss cheese striatum (SCS); however, its clinical impact is unknown. To determine the clinical features associated with SCS detected on MRI scans. A blinded, retrospective case-control study using medical records from 2000 to 2007 obtained from an MRI database at the Mayo Clinic in Rochester, Minnesota, of residents 40 years of age or older of Olmsted County, Minnesota, who had extensive Mayo Clinic medical records and MRI reports suggestive of SCS. Cases with a severe form of SCS (n = 27) were randomly selected for comparison with age-, sex-, and examination year-matched controls (n = 52) with a minimal form of SCS or no SCS. Magnetic resonance imaging. Associations of clinical and imaging features with the presence of a severe form of SCS. Medical records were reviewed for clinical features such as parkinsonism, dementia, and vascular risk factors. The MRI scans were visually scored for degree of leukoaraiosis, central atrophy, and cortical atrophy. No significant differences were found between those with a severe form of SCS and controls in rates of parkinsonism (19% vs 17%; odds ratio, 1.09 [95% CI, 0.28-4.16]) or dementia of any type (30% vs 21%; odds ratio, 1.57 [95% CI, 0.48-5.13]). Vascular risk factors were not significantly different between groups. Swiss cheese striatum correlated with degree of leukoaraiosis (P < .001). Potential associations with visualized cortical atrophy (P = .01), nonobstructive urinary incontinence (18.5% vs 3.9%; P = .04), and syncope (37% vs 9.6%; P = .01) did not hold up after correction for the false discovery rate. Our study suggests that marked cribriform change in the striatum was not associated with the development of extrapyramidal clinical disorders, including parkinsonism. The association of SCS with leukoaraiosis suggests that it is part of a more generalized cerebrovascular process. Skepticism is called for when attributing clinical symptoms to this MRI finding.

Training Humans to Categorize Monkey Calls: Auditory Feature- and Category-Selective Neural Tuning Changes.

PubMed

Jiang, Xiong; Chevillet, Mark A; Rauschecker, Josef P; Riesenhuber, Maximilian

2018-04-18

Grouping auditory stimuli into common categories is essential for a variety of auditory tasks, including speech recognition. We trained human participants to categorize auditory stimuli from a large novel set of morphed monkey vocalizations. Using fMRI-rapid adaptation (fMRI-RA) and multi-voxel pattern analysis (MVPA) techniques, we gained evidence that categorization training results in two distinct sets of changes: sharpened tuning to monkey call features (without explicit category representation) in left auditory cortex and category selectivity for different types of calls in lateral prefrontal cortex. In addition, the sharpness of neural selectivity in left auditory cortex, as estimated with both fMRI-RA and MVPA, predicted the steepness of the categorical boundary, whereas categorical judgment correlated with release from adaptation in the left inferior frontal gyrus. These results support the theory that auditory category learning follows a two-stage model analogous to the visual domain, suggesting general principles of perceptual category learning in the human brain. Copyright © 2018 Elsevier Inc. All rights reserved.

Metastatic breast cancer to the rectum: A case report with emphasis on MRI features.

PubMed

Lau, Li Ching; Wee, Bernard; Wang, Shi; Thian, Yee Liang

2017-04-01

Less than 1% of breast carcinomas metastasize to the gastrointestinal tract. The diagnosis is frequently not recognized especially when the history of breast carcinoma is remote. A 61-year-old female with a remote history of breast carcinoma presented with a 3-month history of change in bowel habits. Colonoscopy showed a circumferential rectal mass with initial impression of primary rectal cancer. MRI of the rectum showed findings that are atypical for primary rectal cancer. Deep biopsy of the rectal mass confirmed lobular breast carcinoma metastasis to the rectum. The patient was treated with radiotherapy and hormonal therapy. She is symptomatically well 2 years after presentation and remains on hormonal therapy. Lobular breast cancer which metastasizes to the rectum can mimic primary rectal cancer clinically. The unique MRI features described in our case when present with a concordant history of lobular breast carcinoma should alert the radiologist to the possibility of this diagnosis which has important treatment implications.

Magnetic resonance imaging after anterior cruciate ligament reconstruction: A practical guide

PubMed Central

Grassi, Alberto; Bailey, James R; Signorelli, Cecilia; Carbone, Giuseppe; Tchonang Wakam, Andy; Lucidi, Gian Andrea; Zaffagnini, Stefano

2016-01-01

Anterior cruciate ligament (ACL) reconstruction is one of the most common orthopedic procedures performed worldwide. In this regard, magnetic resonance imaging (MRI) represents a useful pre-operative tool to confirm a disruption of the ACL and to assess for potential associated injuries. However, MRI is also valuable post-operatively, as it is able to identify, in a non-invasive way, a number of aspects and situations that could suggest potential problems to clinicians. Graft signal and integrity, correct tunnel placement, tunnel widening, and problems with fixation devices or the donor site could all compromise the surgical outcomes and potentially predict the failure of the ACL reconstruction. Furthermore, several anatomical features of the knee could be associated to worst outcomes or higher risk of failure. This review provides a practical guide for the clinician to evaluate the post-surgical ACL through MRI, and to analyze all the parameters and features directly or indirectly related to ACL reconstruction, in order to assess for normal or pathologic conditions. PMID:27795945

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.

Study of MRI in stratified viscous plasma configuration

NASA Astrophysics Data System (ADS)

Carlevaro, Nakia; Montani, Giovanni; Renzi, Fabrizio

2017-02-01

We analyze the morphology of the magneto-rotational instability (MRI) for a stratified viscous plasma disk configuration in differential rotation, taking into account the so-called corotation theorem for the background profile. In order to select the intrinsic Alfvénic nature of MRI, we deal with an incompressible plasma and we adopt a formulation of the local perturbation analysis based on the use of the magnetic flux function as a dynamical variable. Our study outlines, as consequence of the corotation condition, a marked asymmetry of the MRI with respect to the equatorial plane, particularly evident in a complete damping of the instability over a positive critical height on the equatorial plane. We also emphasize how such a feature is already present (although less pronounced) even in the ideal case, restoring a dependence of the MRI on the stratified morphology of the gravitational field.

Optimizing MRI for imaging peripheral arthritis.

PubMed

Hodgson, Richard J; O'Connor, Philip J; Ridgway, John P

2012-11-01

MRI is increasingly used for the assessment of both inflammatory arthritis and osteoarthritis. The wide variety of MRI systems in use ranges from low-field, low-cost extremity units to whole-body high-field 7-T systems, each with different strengths for specific applications. The availability of dedicated radiofrequency phased-array coils allows the rapid acquisition of high-resolution images of one or more peripheral joints. MRI is uniquely flexible in its ability to manipulate image contrast, and individual MR sequences may be combined into protocols to sensitively visualize multiple features of arthritis including synovitis, bone marrow lesions, erosions, cartilage changes, and tendinopathy. Careful choice of the imaging parameters allows images to be generated with optimal quality while minimizing unwanted artifacts. Finally, there are many novel MRI techniques that can quantify disease levels in arthritis in tissues including synovitis and cartilage. Thieme Medical Publishers 333 Seventh Avenue, New York, NY 10001, USA.

MRI-conditional pacemakers: current perspectives.

PubMed

Ferreira, António M; Costa, Francisco; Tralhão, António; Marques, Hugo; Cardim, Nuno; Adragão, Pedro

2014-01-01

Use of both magnetic resonance imaging (MRI) and pacing devices has undergone remarkable growth in recent years, and it is estimated that the majority of patients with pacemakers will need an MRI during their lifetime. These investigations will generally be denied due to the potentially dangerous interactions between cardiac devices and the magnetic fields and radio frequency energy used in MRI. Despite the increasing reports of uneventful scanning in selected patients with conventional pacemakers under close surveillance, MRI is still contraindicated in those circumstances and cannot be considered a routine procedure. These limitations prompted a series of modifications in generator and lead engineering, designed to minimize interactions that could compromise device function and patient safety. The resulting MRI-conditional pacemakers were first introduced in 2008 and the clinical experience gathered so far supports their safety in the MRI environment if certain conditions are fulfilled. With this technology, new questions and controversies arise regarding patient selection, clinical impact, and cost-effectiveness. In this review, we discuss the potential risks of MRI in patients with electronic cardiac devices and present updated information regarding the features of MRI-conditional pacemakers and the clinical experience with currently available models. Finally, we provide some guidance on how to scan patients who have these devices and discuss future directions in the field.

Photography of the histological and radiological analysis of the ligaments of the distal radioulnar joint.

PubMed

Clayton, Gemma

2013-06-01

This project was undertaken as part of the PhD research project of Paul Malone, Pricipal Investigator, Covance plc, Harrogate. Mr Malone approached the photography department for involvement in the study with the aim of settling the current debate on the anatomical and histological features of the distal radioulnar ligaments by capturing the anatomy photographically throughout the process of dissection via a microtome. The author was approached to lead on the photographic protocol as part of her post-graduate certificate training at Staffordshire University. High-resolution digital images of an entire human arm were required, the main area of interest being the distal radioulnar joint of the wrist. Images were to be taken at 40 μm intervals as the specimen was sliced. When microtomy was undertaken through the ligaments images were made at 20 μm intervals. A method of suspending a camera approximately 1 metre above the specimen was devised, together with the preparation for the capture, processing and storage of images. The resulting images were then to be subject to further analysis in the form of 3-Dimensional reconstruction, using computer modelling techniques and software. The possibility of merging the images with sequences obtained from both CT & MRI using image handling software is also an area of exploration, in collaboration with the University of Manchester's Visualisation Centre.

Dynamic deformable models for 3D MRI heart segmentation

NASA Astrophysics Data System (ADS)

Zhukov, Leonid; Bao, Zhaosheng; Gusikov, Igor; Wood, John; Breen, David E.

2002-05-01

Automated or semiautomated segmentation of medical images decreases interstudy variation, observer bias, and postprocessing time as well as providing clincally-relevant quantitative data. In this paper we present a new dynamic deformable modeling approach to 3D segmentation. It utilizes recently developed dynamic remeshing techniques and curvature estimation methods to produce high-quality meshes. The approach has been implemented in an interactive environment that allows a user to specify an initial model and identify key features in the data. These features act as hard constraints that the model must not pass through as it deforms. We have employed the method to perform semi-automatic segmentation of heart structures from cine MRI data.

Clinical Utility of Magnetic Resonance Imaging (MRI) and Ultrasonography (US) for Diagnosis of Polycystic Ovary Syndrome (PCOS) in Adolescent Girls

PubMed Central

Kenigsberg, Lisa E; Agarwal, Chhavi; Sin, Sanghun; Shifteh, Keivan; Isasi, Carmen R; Crespi, Rebecca; Ivanova, Janeta; Coupey, Susan M; Heptulla, Rubina A; Arens, Raanan

2015-01-01

Objectives Evaluate ovarian morphology using 3-dimensional MRI in adolescent girls with and without PCOS. Compare the utility of MRI versus ultrasonography (US) for diagnosis of PCOS Design Cross-sectional Setting Urban academic tertiary-care children’s hospital Patients Thirty-nine adolescent girls with untreated PCOS and 22 age/BMI-matched controls. Intervention MRI and/or transvaginal/transabdominal US Main Outcome Measure Ovarian volume (OV); follicle number per section (FNPS); correlation between OV on MRI and US; proportion of subjects with features of polycystic ovaries on MRI and US. Results MRI demonstrated larger OV and higher FNPS in subjects with PCOS compared to controls. Within the PCOS group, median OV was 11.9 (7.7) cm3 by MRI, compared with 8.8 (7.8) cm3 by US. Correlation coefficient between OV by MRI and US was 0.701. Due to poor resolution, FNPS could not be determined by US or compared with MRI. ROC curve analysis for MRI demonstrated that increasing volume cut-offs for polycystic ovaries from 10cm3 to 14cm3, increased specificity from 77% to 95%. For FNPS on MRI, specificity increased from 82% to 98% by increasing cut-offs from ≥12 to ≥17. Using Rotterdam cut-offs, 91% of subjects with PCOS met polycystic ovary criteria on MRI, while only 52% met criteria by US. Conclusions US measures smaller OV than MRI, cannot accurately detect follicle number, and is a poor imaging modality for characterizing polycystic ovaries in adolescents with suspected PCOS. For adolescents in whom diagnosis of PCOS remains uncertain after clinical and laboratory evaluation, MRI should be considered as a diagnostic imaging modality. PMID:26354095

Segmentation propagation for the automated quantification of ventricle volume from serial MRI

NASA Astrophysics Data System (ADS)

Linguraru, Marius George; Butman, John A.

2009-02-01

Accurate ventricle volume estimates could potentially improve the understanding and diagnosis of communicating hydrocephalus. Postoperative communicating hydrocephalus has been recognized in patients with brain tumors where the changes in ventricle volume can be difficult to identify, particularly over short time intervals. Because of the complex alterations of brain morphology in these patients, the segmentation of brain ventricles is challenging. Our method evaluates ventricle size from serial brain MRI examinations; we (i) combined serial images to increase SNR, (ii) automatically segmented this image to generate a ventricle template using fast marching methods and geodesic active contours, and (iii) propagated the segmentation using deformable registration of the original MRI datasets. By applying this deformation to the ventricle template, serial volume estimates were obtained in a robust manner from routine clinical images (0.93 overlap) and their variation analyzed.

Clustering-Constrained ICA for Ballistocardiogram Artifacts Removal in Simultaneous EEG-fMRI

PubMed Central

Wang, Kai; Li, Wenjie; Dong, Li; Zou, Ling; Wang, Changming

2018-01-01

Combination of electroencephalogram (EEG) recording and functional magnetic resonance imaging (fMRI) plays a potential role in neuroimaging due to its high spatial and temporal resolution. However, EEG is easily influenced by ballistocardiogram (BCG) artifacts and may cause false identification of the related EEG features, such as epileptic spikes. There are many related methods to remove them, however, they do not consider the time-varying features of BCG artifacts. In this paper, a novel method using clustering algorithm to catch the BCG artifacts' features and together with the constrained ICA (ccICA) is proposed to remove the BCG artifacts. We first applied this method to the simulated data, which was constructed by adding the BCG artifacts to the EEG signal obtained from the conventional environment. Then, our method was tested to demonstrate the effectiveness during EEG and fMRI experiments on 10 healthy subjects. In simulated data analysis, the value of error in signal amplitude (Er) computed by ccICA method was lower than those from other methods including AAS, OBS, and cICA (p < 0.005). In vivo data analysis, the Improvement of Normalized Power Spectrum (INPS) calculated by ccICA method in all electrodes was much higher than AAS, OBS, and cICA methods (p < 0.005). We also used other evaluation index (e.g., power analysis) to compare our method with other traditional methods. In conclusion, our novel method successfully and effectively removed BCG artifacts in both simulated and vivo EEG data tests, showing the potentials of removing artifacts in EEG-fMRI applications. PMID:29487499

Magnetic resonance evaluation of the knee in children and adolescents with achondroplasia.

PubMed

Akyol, Yakup; Averill, Lauren W; Atanda, Alfred; Kecskemethy, Heidi H; Bober, Michael B; Mackenzie, William G

2015-06-01

Achondroplasia is the most common form of skeletal dysplasia. Although the radiographic features are well described, MRI features of the knee in achondroplasia have not been reported. To describe common MRI characteristics of the knee joint in symptomatic children and adolescents with achondroplasia. We retrospectively evaluated 10 knee MRI examinations in 8 children and young adults (age range 11-20 years, mean 16.3 years) with achondroplasia. We measured modified Insall-Salvati index, knee flexion angle, anterior cruciate ligament (ACL)-Blumensaat line angle, ACL-tibial angle, posterior cruciate ligament (PCL) angle, intercondylar notch width index, and intercondylar notch depth index. We compared our findings with an age- and gender-matched control group of 20 children (age range 15-18 years; mean 16 years) with normal knee MRIs. All 10 knees in the achondroplasia group had discoid lateral meniscus; 8 meniscal tears were identified. Patella baja was present in half of the study cases. Greater knee flexion and increased ACL-Blumensaat line and PCL angles were seen in all achondroplasia knees. ACL-tibial angle was similar in the study and in the control group. Children with achondroplasia had deeper A-shape femoral notches that extended more anteriorly than those seen in the control group. MRI findings were confirmed in all seven knees with arthroscopic correlation. Discoid lateral meniscus, often with tear, is a consistent feature in knee MRIs of symptomatic children and adolescents with achondroplasia. Other findings include patella baja, knee flexion, deep A-shape intercondylar notch, increased ACL-Blumensaat line angle and taut PCL.

Association of magnetic resonance imaging findings and histologic diagnosis in dogs with nasal disease: 78 cases (2001-2004).

PubMed

Miles, Macon S; Dhaliwal, Ravinder S; Moore, Michael P; Reed, Ann L

2008-06-15

OBJECTIVE-To determine whether magnetic resonance imaging (MRI) features correlated with histologic diagnosis in dogs with nasal disease. DESIGN-Retrospective case series. ANIMALS-78 Dogs undergoing MRI for evaluation of nasal disease. PROCEDURES-Medical records and MRI reports of dogs were reviewed to identify MRI features associated with histologic diagnosis. Features evaluated were presence of a mass effect, frontal sinus involvement, sphenoid sinus involvement, maxillary recess involvement, nasopharyngeal infiltration by soft tissue, nasal turbinate destruction, vomer bone lysis, paranasal bone destruction, cribriform plate erosion, and lesion extent (ie, unilateral vs bilateral). RESULTS-33 Dogs had neoplastic disease, 38 had inflammatory rhinitis, and 7 had fungal rhinitis. Lesion extent was not significantly associated with histologic diagnosis. Absence of a mass effect was significantly associated with inflammatory disease. However, presence of a mass was not specific for neoplasia. In dogs with evidence of a mass on magnetic resonance (MR) images, nasal turbinate destruction, frontal sinus invasion, and maxillary recess invasion were not useful in distinguishing neoplastic from nonneoplastic disease, but cribriform plate erosion, vomer bone lysis, paranasal bone destruction, sphenoid sinus invasion, and nasopharyngeal invasion were. CONCLUSIONS AND CLINICAL RELEVANCE-Results suggested that in dogs with nasal disease, the lack of a mass effect on MR images was significantly associated with inflammatory disease. In dogs with a mass effect on MR images, vomer bone lysis, cribriform plate erosion, paranasal bone destruction, sphenoid sinus invasion by a mass, and nasopharyngeal invasion by a mass were significantly associated with a diagnosis of neoplasia.

Mapping feature-sensitivity and attentional modulation in human auditory cortex with functional magnetic resonance imaging

PubMed Central

Paltoglou, Aspasia E; Sumner, Christian J; Hall, Deborah A

2011-01-01

Feature-specific enhancement refers to the process by which selectively attending to a particular stimulus feature specifically increases the response in the same region of the brain that codes that stimulus property. Whereas there are many demonstrations of this mechanism in the visual system, the evidence is less clear in the auditory system. The present functional magnetic resonance imaging (fMRI) study examined this process for two complex sound features, namely frequency modulation (FM) and spatial motion. The experimental design enabled us to investigate whether selectively attending to FM and spatial motion enhanced activity in those auditory cortical areas that were sensitive to the two features. To control for attentional effort, the difficulty of the target-detection tasks was matched as closely as possible within listeners. Locations of FM-related and motion-related activation were broadly compatible with previous research. The results also confirmed a general enhancement across the auditory cortex when either feature was being attended to, as compared with passive listening. The feature-specific effects of selective attention revealed the novel finding of enhancement for the nonspatial (FM) feature, but not for the spatial (motion) feature. However, attention to spatial features also recruited several areas outside the auditory cortex. Further analyses led us to conclude that feature-specific effects of selective attention are not statistically robust, and appear to be sensitive to the choice of fMRI experimental design and localizer contrast. PMID:21447093

Computer-aided classification of Alzheimer's disease based on support vector machine with combination of cerebral image features in MRI

NASA Astrophysics Data System (ADS)

Jongkreangkrai, C.; Vichianin, Y.; Tocharoenchai, C.; Arimura, H.; Alzheimer's Disease Neuroimaging Initiative

2016-03-01

Several studies have differentiated Alzheimer's disease (AD) using cerebral image features derived from MR brain images. In this study, we were interested in combining hippocampus and amygdala volumes and entorhinal cortex thickness to improve the performance of AD differentiation. Thus, our objective was to investigate the useful features obtained from MRI for classification of AD patients using support vector machine (SVM). T1-weighted MR brain images of 100 AD patients and 100 normal subjects were processed using FreeSurfer software to measure hippocampus and amygdala volumes and entorhinal cortex thicknesses in both brain hemispheres. Relative volumes of hippocampus and amygdala were calculated to correct variation in individual head size. SVM was employed with five combinations of features (H: hippocampus relative volumes, A: amygdala relative volumes, E: entorhinal cortex thicknesses, HA: hippocampus and amygdala relative volumes and ALL: all features). Receiver operating characteristic (ROC) analysis was used to evaluate the method. AUC values of five combinations were 0.8575 (H), 0.8374 (A), 0.8422 (E), 0.8631 (HA) and 0.8906 (ALL). Although “ALL” provided the highest AUC, there were no statistically significant differences among them except for “A” feature. Our results showed that all suggested features may be feasible for computer-aided classification of AD patients.

[Case of interval form of carbon monoxide poisoning without increased carboxyhemoglobin level diagnosed by characteristic MR spectroscopy findings].

PubMed

Kamisawa, Tomoko; Ikawa, Masamichi; Hamano, Tadanori; Nagata, Miwako; Kimura, Hirohiko; Yoneda, Makoto

2014-01-01

A 67-year-old man living alone was admitted for acute disturbance of consciousness during winter. He presented with semicoma, a decorticate posture, and exaggerated tendon reflexes of the limbs, but brainstem reflexes were intact. The carboxyhemoglobin (COHb) level was normal in arterial blood gas on admission, and protein in cerebrospinal fluid was increased without pleocytosis. Brain MRI showed diffuse T2 high intensities in the deep white matter bilaterally without a contrast effect and abnormal T1 intensity in the pallidum. (1)H-MR spectroscopy (MRS) of the white matter lesion demonstrated findings suggesting demyelination as an increased choline peak, enhanced anaerobic metabolism as increased lactate and lipids peaks, and reduced neurons as a decreased N-acetylaspartate peak, which corresponded to delayed encephalopathy due to the interval form of carbon monoxide (CO) poisoning. The possibility of CO exposure due to coal briquette use 2 weeks before the symptomatic onset was indicated by his family, so he was diagnosed with CO poisoning. His consciousness slightly improved with corticosteroid therapy and repetitive hyperbaric oxygen therapy, but brain MRI and MRS findings did not improve. Characteristic MRS findings of leukoencephalopathy are helpful for diagnosing the interval form of CO poisoning in the case of a normal COHb level.

Computed tomography versus magnetic resonance imaging for diagnosing cervical lymph node metastasis of head and neck cancer: a systematic review and meta-analysis

PubMed Central

Sun, J; Li, B; Li, CJ; Li, Y; Su, F; Gao, QH; Wu, FL; Yu, T; Wu, L; Li, LJ

2015-01-01

Computed tomography (CT) and magnetic resonance imaging (MRI) are common imaging methods to detect cervical lymph node metastasis of head and neck cancer. We aimed to assess the diagnostic efficacy of CT and MRI in detecting cervical lymph node metastasis, and to establish unified diagnostic criteria via systematic review and meta-analysis. A systematic literature search in five databases until January 2014 was carried out. All retrieved studies were reviewed and eligible studies were qualitatively summarized. Besides pooling the sensitivity (SEN) and specificity (SPE) data of CT and MRI, summary receiver operating characteristic curves were generated. A total of 63 studies including 3,029 participants were involved. The pooled results of meta-analysis showed that CT had a higher SEN (0.77 [95% confidence interval {CI} 0.73–0.87]) than MRI (0.72 [95% CI 0.70–0.74]) when node was considered as unit of analysis (P<0.05); MRI had a higher SPE (0.81 [95% CI 0.80–0.82]) than CT (0.72 [95% CI 0.69–0.74]) when neck level was considered as unit of analysis (P<0.05) and MRI had a higher area under concentration-time curve than CT when the patient was considered as unit of analysis (P<0.05). With regards to diagnostic criteria, for MRI, the results showed that the minimal axial diameter of 10 mm could be considered as the best size criterion, compared to 12 mm for CT. Overall, MRI conferred significantly higher SPE while CT demonstrated higher SEN. The diagnostic criteria for MRI and CT on size of metastatic lymph nodes were suggested as 10 and 12 mm, respectively. PMID:26089682

Influence of low back pain and prognostic value of MRI in sciatica patients in relation to back pain.

PubMed

el Barzouhi, Abdelilah; Vleggeert-Lankamp, Carmen L A M; Lycklama à Nijeholt, Geert J; Van der Kallen, Bas F; van den Hout, Wilbert B; Koes, Bart W; Peul, Wilco C

2014-01-01

Patients with sciatica frequently complain about associated back pain. It is not known whether there are prognostic relevant differences in Magnetic Resonance Imaging (MRI) findings between sciatica patients with and without disabling back pain. The study population contained patients with sciatica who underwent a baseline MRI to assess eligibility for a randomized trial designed to compare the efficacy of early surgery with prolonged conservative care for sciatica. Two neuroradiologists and one neurosurgeon independently evaluated all MR images. The MRI readers were blinded to symptom status. The MRI findings were compared between sciatica patients with and without disabling back pain. The presence of disabling back pain at baseline was correlated with perceived recovery at one year. Of 379 included sciatica patients, 158 (42%) had disabling back pain. Of the patients with both sciatica and disabling back pain 68% did reveal a herniated disc with nerve root compression on MRI, compared to 88% of patients with predominantly sciatica (P<0.001). The existence of disabling back pain in sciatica at baseline was negatively associated with perceived recovery at one year (Odds ratio [OR] 0.32, 95% Confidence Interval 0.18-0.56, P<0.001). Sciatica patients with disabling back pain in absence of nerve root compression on MRI at baseline reported less perceived recovery at one year compared to those with predominantly sciatica and nerve root compression on MRI (50% vs 91%, P<0.001). Sciatica patients with disabling low back pain reported an unfavorable outcome at one-year follow-up compared to those with predominantly sciatica. If additionally a clear herniated disc with nerve root compression on MRI was absent, the results were even worse.

Influence of Low Back Pain and Prognostic Value of MRI in Sciatica Patients in Relation to Back Pain

PubMed Central

el Barzouhi, Abdelilah; Vleggeert-Lankamp, Carmen L. A. M.; Lycklama à Nijeholt, Geert J.; Van der Kallen, Bas F.; van den Hout, Wilbert B.; Koes, Bart W.; Peul, Wilco C.

2014-01-01

Background Patients with sciatica frequently complain about associated back pain. It is not known whether there are prognostic relevant differences in Magnetic Resonance Imaging (MRI) findings between sciatica patients with and without disabling back pain. Methods The study population contained patients with sciatica who underwent a baseline MRI to assess eligibility for a randomized trial designed to compare the efficacy of early surgery with prolonged conservative care for sciatica. Two neuroradiologists and one neurosurgeon independently evaluated all MR images. The MRI readers were blinded to symptom status. The MRI findings were compared between sciatica patients with and without disabling back pain. The presence of disabling back pain at baseline was correlated with perceived recovery at one year. Results Of 379 included sciatica patients, 158 (42%) had disabling back pain. Of the patients with both sciatica and disabling back pain 68% did reveal a herniated disc with nerve root compression on MRI, compared to 88% of patients with predominantly sciatica (P<0.001). The existence of disabling back pain in sciatica at baseline was negatively associated with perceived recovery at one year (Odds ratio [OR] 0.32, 95% Confidence Interval 0.18–0.56, P<0.001). Sciatica patients with disabling back pain in absence of nerve root compression on MRI at baseline reported less perceived recovery at one year compared to those with predominantly sciatica and nerve root compression on MRI (50% vs 91%, P<0.001). Conclusion Sciatica patients with disabling low back pain reported an unfavorable outcome at one-year follow-up compared to those with predominantly sciatica. If additionally a clear herniated disc with nerve root compression on MRI was absent, the results were even worse. PMID:24637890

Preoperative Breast Magnetic Resonance Imaging Use by Breast Density and Family History of Breast Cancer.

PubMed

Henderson, Louise M; Hubbard, Rebecca A; Zhu, Weiwei; Weiss, Julie; Wernli, Karen J; Goodrich, Martha E; Kerlikowske, Karla; DeMartini, Wendy; Ozanne, Elissa M; Onega, Tracy

2018-01-15

Use of preoperative breast magnetic resonance imaging (MRI) among women with a new breast cancer has increased over the past decade. MRI use is more frequent in younger women and those with lobular carcinoma, but associations with breast density and family history of breast cancer are unknown. Data for 3075 women ages >65 years with stage 0-III breast cancer who underwent breast conserving surgery or mastectomy from 2005 to 2010 in the Breast Cancer Surveillance Consortium were linked to administrative claims data to assess associations of preoperative MRI use with mammographic breast density and first-degree family history of breast cancer. Multivariable logistic regression estimated adjusted odds ratios (OR) and 95% confidence intervals (95% CI) for the association of MRI use with breast density and family history, adjusting for woman and tumor characteristics. Overall, preoperative MRI use was 16.4%. The proportion of women receiving breast MRI was similar by breast density (17.6% dense, 16.9% nondense) and family history (17.1% with family history, 16.5% without family history). After adjusting for potential confounders, we found no difference in preoperative MRI use by breast density (OR = 0.95 for dense vs. nondense, 95% CI: 0.73-1.22) or family history (OR = 0.99 for family history vs. none, 95% CI: 0.73-1.32). Among women aged >65 years with breast cancer, having dense breasts or a first-degree relative with breast cancer was not associated with greater preoperative MRI use. This utilization is in keeping with lack of evidence that MRI has higher yield of malignancy in these subgroups.

Longitudinal DSC-MRI for Distinguishing Tumor Recurrence From Pseudoprogression in Patients With a High-grade Glioma.

PubMed

Boxerman, Jerrold L; Ellingson, Benjamin M; Jeyapalan, Suriya; Elinzano, Heinrich; Harris, Robert J; Rogg, Jeffrey M; Pope, Whitney B; Safran, Howard

2017-06-01

For patients with high-grade glioma on clinical trials it is important to accurately assess time of disease progression. However, differentiation between pseudoprogression (PsP) and progressive disease (PD) is unreliable with standard magnetic resonance imaging (MRI) techniques. Dynamic susceptibility contrast perfusion MRI (DSC-MRI) can measure relative cerebral blood volume (rCBV) and may help distinguish PsP from PD. A subset of patients with high-grade glioma on a phase II clinical trial with temozolomide, paclitaxel poliglumex, and concurrent radiation were assessed. Nine patients (3 grade III, 6 grade IV), with a total of 19 enhancing lesions demonstrating progressive enhancement (≥25% increase from nadir) on postchemoradiation conventional contrast-enhanced MRI, had serial DSC-MRI. Mean leakage-corrected rCBV within enhancing lesions was computed for all postchemoradiation time points. Of the 19 progressively enhancing lesions, 10 were classified as PsP and 9 as PD by biopsy/surgery or serial enhancement patterns during interval follow-up MRI. Mean rCBV at initial progressive enhancement did not differ significantly between PsP and PD (2.35 vs. 2.17; P=0.67). However, change in rCBV at first subsequent follow-up (-0.84 vs. 0.84; P=0.001) and the overall linear trend in rCBV after initial progressive enhancement (negative vs. positive slope; P=0.04) differed significantly between PsP and PD. Longitudinal trends in rCBV may be more useful than absolute rCBV in distinguishing PsP from PD in chemoradiation-treated high-grade gliomas with DSC-MRI. Further studies of DSC-MRI in high-grade glioma as a potential technique for distinguishing PsP from PD are indicated.

Feature space analysis of MRI

NASA Astrophysics Data System (ADS)

Soltanian-Zadeh, Hamid; Windham, Joe P.; Peck, Donald J.

1997-04-01

This paper presents development and performance evaluation of an MRI feature space method. The method is useful for: identification of tissue types; segmentation of tissues; and quantitative measurements on tissues, to obtain information that can be used in decision making (diagnosis, treatment planning, and evaluation of treatment). The steps of the work accomplished are as follows: (1) Four T2-weighted and two T1-weighted images (before and after injection of Gadolinium) were acquired for ten tumor patients. (2) Images were analyed by two image analysts according to the following algorithm. The intracranial brain tissues were segmented from the scalp and background. The additive noise was suppressed using a multi-dimensional non-linear edge- preserving filter which preserves partial volume information on average. Image nonuniformities were corrected using a modified lowpass filtering approach. The resulting images were used to generate and visualize an optimal feature space. Cluster centers were identified on the feature space. Then images were segmented into normal tissues and different zones of the tumor. (3) Biopsy samples were extracted from each patient and were subsequently analyzed by the pathology laboratory. (4) Image analysis results were compared to each other and to the biopsy results. Pre- and post-surgery feature spaces were also compared. The proposed algorithm made it possible to visualize the MRI feature space and to segment the image. In all cases, the operators were able to find clusters for normal and abnormal tissues. Also, clusters for different zones of the tumor were found. Based on the clusters marked for each zone, the method successfully segmented the image into normal tissues (white matter, gray matter, and CSF) and different zones of the lesion (tumor, cyst, edema, radiation necrosis, necrotic core, and infiltrated tumor). The results agreed with those obtained from the biopsy samples. Comparison of pre- to post-surgery and radiation feature spaces confirmed that the tumor was not present in the second study but radiation necrosis was generated as a result of radiation.

Cerebral gray matter volume in patients with chronic migraine: correlations with clinical features.

PubMed

Coppola, Gianluca; Petolicchio, Barbara; Di Renzo, Antonio; Tinelli, Emanuele; Di Lorenzo, Cherubino; Parisi, Vincenzo; Serrao, Mariano; Calistri, Valentina; Tardioli, Stefano; Cartocci, Gaia; Ambrosini, Anna; Caramia, Francesca; Di Piero, Vittorio; Pierelli, Francesco

2017-12-08

To date, few MRI studies have been performed in patients affected by chronic migraine (CM), especially in those without medication overuse. Here, we performed magnetic resonance imaging (MRI) voxel-based morphometry (VBM) analyses to investigate the gray matter (GM) volume of the whole brain in patients affected by CM. Our aim was to investigate whether fluctuations in the GM volumes were related to the clinical features of CM. Twenty untreated patients with CM without a past medical history of medication overuse underwent 3-Tesla MRI scans and were compared to a group of 20 healthy controls (HCs). We used SPM12 and the CAT12 toolbox to process the MRI data and to perform VBM analyses of the structural T1-weighted MRI scans. The GM volume of patients was compared to that of HCs with various corrected and uncorrected thresholds. To check for possible correlations, patients' clinical features and GM maps were regressed. Initially, we did not find significant differences in the GM volume between patients with CM and HCs (p < 0.05 corrected for multiple comparisons). However, using more-liberal uncorrected statistical thresholds, we noted that compared to HCs, patients with CM exhibited clusters of regions with lower GM volumes including the cerebellum, left middle temporal gyrus, left temporal pole/amygdala/hippocampus/pallidum/orbitofrontal cortex, and left occipital areas (Brodmann areas 17/18). The GM volume of the cerebellar hemispheres was negatively correlated with the disease duration and positively correlated with the number of tablets taken per month. No gross morphometric changes were observed in patients with CM when compared with HCs. However, using more-liberal uncorrected statistical thresholds, we observed that CM is associated with subtle GM volume changes in several brain areas known to be involved in nociception/antinociception, multisensory integration, and analgesic dependence. We speculate that these slight morphometric impairments could lead, at least in a subgroup of patients, to the development and continuation of maladaptive acute medication usage.

Kernel Principal Component Analysis for dimensionality reduction in fMRI-based diagnosis of ADHD.

PubMed

Sidhu, Gagan S; Asgarian, Nasimeh; Greiner, Russell; Brown, Matthew R G

2012-01-01

This study explored various feature extraction methods for use in automated diagnosis of Attention-Deficit Hyperactivity Disorder (ADHD) from functional Magnetic Resonance Image (fMRI) data. Each participant's data consisted of a resting state fMRI scan as well as phenotypic data (age, gender, handedness, IQ, and site of scanning) from the ADHD-200 dataset. We used machine learning techniques to produce support vector machine (SVM) classifiers that attempted to differentiate between (1) all ADHD patients vs. healthy controls and (2) ADHD combined (ADHD-c) type vs. ADHD inattentive (ADHD-i) type vs. controls. In different tests, we used only the phenotypic data, only the imaging data, or else both the phenotypic and imaging data. For feature extraction on fMRI data, we tested the Fast Fourier Transform (FFT), different variants of Principal Component Analysis (PCA), and combinations of FFT and PCA. PCA variants included PCA over time (PCA-t), PCA over space and time (PCA-st), and kernelized PCA (kPCA-st). Baseline chance accuracy was 64.2% produced by guessing healthy control (the majority class) for all participants. Using only phenotypic data produced 72.9% accuracy on two class diagnosis and 66.8% on three class diagnosis. Diagnosis using only imaging data did not perform as well as phenotypic-only approaches. Using both phenotypic and imaging data with combined FFT and kPCA-st feature extraction yielded accuracies of 76.0% on two class diagnosis and 68.6% on three class diagnosis-better than phenotypic-only approaches. Our results demonstrate the potential of using FFT and kPCA-st with resting-state fMRI data as well as phenotypic data for automated diagnosis of ADHD. These results are encouraging given known challenges of learning ADHD diagnostic classifiers using the ADHD-200 dataset (see Brown et al., 2012).

Prediction Errors but Not Sharpened Signals Simulate Multivoxel fMRI Patterns during Speech Perception

PubMed Central

Davis, Matthew H.

2016-01-01

Successful perception depends on combining sensory input with prior knowledge. However, the underlying mechanism by which these two sources of information are combined is unknown. In speech perception, as in other domains, two functionally distinct coding schemes have been proposed for how expectations influence representation of sensory evidence. Traditional models suggest that expected features of the speech input are enhanced or sharpened via interactive activation (Sharpened Signals). Conversely, Predictive Coding suggests that expected features are suppressed so that unexpected features of the speech input (Prediction Errors) are processed further. The present work is aimed at distinguishing between these two accounts of how prior knowledge influences speech perception. By combining behavioural, univariate, and multivariate fMRI measures of how sensory detail and prior expectations influence speech perception with computational modelling, we provide evidence in favour of Prediction Error computations. Increased sensory detail and informative expectations have additive behavioural and univariate neural effects because they both improve the accuracy of word report and reduce the BOLD signal in lateral temporal lobe regions. However, sensory detail and informative expectations have interacting effects on speech representations shown by multivariate fMRI in the posterior superior temporal sulcus. When prior knowledge was absent, increased sensory detail enhanced the amount of speech information measured in superior temporal multivoxel patterns, but with informative expectations, increased sensory detail reduced the amount of measured information. Computational simulations of Sharpened Signals and Prediction Errors during speech perception could both explain these behavioural and univariate fMRI observations. However, the multivariate fMRI observations were uniquely simulated by a Prediction Error and not a Sharpened Signal model. The interaction between prior expectation and sensory detail provides evidence for a Predictive Coding account of speech perception. Our work establishes methods that can be used to distinguish representations of Prediction Error and Sharpened Signals in other perceptual domains. PMID:27846209

Imaging and histological features of central subchondral osteophytes in racehorses with metacarpophalangeal joint osteoarthritis.

PubMed

Olive, J; D'Anjou, M A; Girard, C; Laverty, S; Theoret, C L

2009-12-01

Marginal osteophytes represent a well known component of osteoarthritis in man and animals. Conversely, central subchondral osteophytes (COs), which are commonly present in human knees with osteoarthritis, have not been reported in horses. To describe and compare computed radiography (CR), single-slice computed tomography (CT), 1.5 Tesla magnetic resonance imaging (MRI), and histological features of COs in equine metacarpophalangeal joints with macroscopic evidence of naturally-occurring osteoarthritis. MRI sequences (sagittal spoiled gradient recalled echo [SPGR] with fat saturation, sagittal T2-weighted fast spin echo with fat saturation [T2-FS], dorsal and transverse T1-weighted gradient-recalled echo [GRE], and sagittal T2*-weighted gradient echo with fast imaging employing steady state acquisition [FIESTA]), as well as transverse and reformatted sagittal CTI and 4 computed radiographic (CR) views of 20 paired metacarpophalangeal joints were acquired ex vivo. Following macroscopic evaluation, samples were harvested in predetermined sites of the metacarpal condyle for subsequent histology. The prevalence and detection level of COs was determined for each imaging modality. Abnormalities consistent with COs were clearly depicted on MRI, using the SPGR sequence, in 7/20 (35%) joints. They were identified as a focal hypointense protuberance from the subchondral plate into the cartilage, at the palmarodistal aspect (n=7) and/or at the very dorsal aspect (n=2) of the metacarpal condyle. COs were visible but less obvious in 5 of the 7 joints using FIESTA and reformatted sagittal CT, and were not identifiable on T2-FS, T1-GRE or CR. Microscopically, they consisted of dense bone protruding into the calcified cartilage and disrupting the tidemarks, and they were consistently associated with overlying cartilage defects. Subchondral osteophytes are a feature of osteoarthritis of equine metacarpophalangeal joints and they may be diagnosed using 1.5 Tesla MRI and CT. Central subchondral osteophytes on MRI represent indirect evidence of cartilage damage in horses.

Prospective study to evaluate the clinical and radiological outcome of patients with scleroderma of the face.

PubMed

Careta, Mariana Figueiroa; Leite, Claudia da Costa; Cresta, Fernando; Albino, Jose; Tsunami, Mirian; Romiti, Ricardo

2013-09-01

Scleroderma featuring rare connective tissue disease that manifests as skin sclerosis and variable systemic involvement. Two categories of scleroderma are known: systemic sclerosis, characterized by cutaneous sclerosis and visceral involvement and localized scleroderma or morphea which classically presents benign evolution and self-limited, confined to the skin and/or underlying tissue. Recent studies show that the localized form may possibly course with involvement of internal organs and variable morbidity. This study aimed to determine the demographic characteristics, the prevalence of systemic manifestations and laboratory findings, as well as the association with autoimmune diseases, and the evolution of neurological findings, both clinical as brain MRI in patients with scleroderma of the face and its relation with the activity skin. Patients with localized scleroderma with facial involvement were evaluated and underwent neurological examination, magnetic resonance imaging and ophthalmology evaluation. After 3years, the patients were subjected again to MRI. We studied 12 patients with localized scleroderma of the face. Of this total, headache being the most frequent complaint found in 66.7% of patients, 33.3% had neurological changes possibly associated with scleroderma. As for ophthalmologic evaluation, 25% of patients showed abnormalities. The most frequent parenchymal finding was the presence of lesions with hyperintense or hypointense signal in 75% of patients, followed by ventricular asymmetry at 16.7%. Of the patients who had neurological deficits, 75% also had a change to MRI. In all patients, imaging findings after 3years were unchanged. During this interval of 3years, 25% of patients showed signs of activity of scleroderma. Patients with localized scleroderma of the face have a high prevalence of neurological and ophthalmological changes. Based on these findings, we suggest that all cases of localized scleroderma of the face should be thoroughly examined for the presence of systemic changes. Copyright © 2013 Elsevier B.V. All rights reserved.

Magnetic Resonance Imaging to Evaluate Cervical Spinal Cord Injury from Gunshot Wounds from Handguns.

PubMed

Slavin, Justin; Beaty, Narlin; Raghavan, Prashant; Sansur, Charles; Aarabi, Bizhan

2015-12-01

Patients presenting with gunshot wounds (GSWs) to the neck are difficult to assess because of their injuries are often severe and they are incompletely evaluated by computed tomography (CT) alone. Our institution treats hundreds of patients with GSWs each year and we present our experience using magnetic resonance imaging (MRI) in the evaluation of cervical GSWs. From August 2000 to July 2012, all GSWs to the cervical spine treated at our institution were cataloged. Seventeen patients had 1 or more MRI studies of the cervical spine. Informed consent was obtained before MRI indicating the risks of retained metal fragments in the setting of high magnetic fields. CT scans were obtained before and after MRI to document any possible migration of metal fragments. We documented patients' neurologic examination results before and after MRI and at follow-up. Patients' age range was 18-56 years (mean 29.8 years). Eleven of 17 patients had retained metal fragments seen on CT scan, including 3 patients with fragments within the spinal canal. No patient experienced a decline in neurologic function after MRI. No migration of retained fragments was observed. Fifteen of 17 patients returned for follow-up examinations, with an average follow-up interval of 39.1 weeks (range, 1.3-202.3 weeks; median, 8 weeks). For carefully selected patients, MRI can be an effective tool in assessing GSWs to the neck and it can significantly improve the evaluation and management of this cohort. No patient in our series experienced a complication related to MRI. Copyright © 2015 Elsevier Inc. All rights reserved.

Prenatal Diagnosis of Placenta Accreta: Sonography or Magnetic Resonance Imaging?

PubMed Central

Dwyer, Bonnie K.; Belogolovkin, Victoria; Tran, Lan; Rao, Anjali; Carroll, Ian; Barth, Richard; Chitkara, Usha

2009-01-01

Objective The purpose of this study was to compare the accuracy of transabdominal sonography and magnetic resonance imaging (MRI) for prenatal diagnosis of placenta accreta. Methods A historical cohort study was undertaken at 3 institutions identifying women at risk for placenta accreta who had undergone both sonography and MRI prenatally. Sonographic and MRI findings were compared with the final diagnosis as determined at delivery and by pathologic examination. Results Thirty-two patients who had both sonography and MRI prenatally to evaluate for placenta accreta were identified. Of these, 15 had confirmation of placenta accreta at delivery. Sonography correctly identified the presence of placenta accreta in 14 of 15 patients (93% sensitivity; 95% confidence interval [CI], 80%–100%) and the absence of placenta accreta in 12 of 17 patients (71% specificity; 95% CI, 49%–93%). Magnetic resonance imaging correctly identified the presence of placenta accreta in 12 of 15 patients (80% sensitivity; 95% CI, 60%–100%) and the absence of placenta accreta in 11 of 17 patients (65% specificity; 95% CI, 42%–88%). In 7 of 32 cases, sonography and MRI had discordant diagnoses: sonography was correct in 5 cases, and MRI was correct in 2. There was no statistical difference in sensitivity (P = .25) or specificity (P = .5) between sonography and MRI. Conclusions Both sonography and MRI have fairly good sensitivity for prenatal diagnosis of placenta accreta; however, specificity does not appear to be as good as reported in other studies. In the case of inconclusive findings with one imaging modality, the other modality may be useful for clarifying the diagnosis. PMID:18716136

Coronary Microembolization with Normal Epicardial Coronary Arteries and No Visible Infarcts on Nitrobluetetrazolium Chloride-Stained Specimens: Evaluation with Cardiac Magnetic Resonance Imaging in a Swine Model.

PubMed

Jin, Hang; Yun, Hong; Ma, Jianying; Chen, Zhangwei; Chang, Shufu; Zeng, Mengsu

2016-01-01

To assess magnetic resonance imaging (MRI) features of coronary microembolization in a swine model induced by small-sized microemboli, which may cause microinfarcts invisible to the naked eye. Eleven pigs underwent intracoronary injection of small-sized microspheres (42 µm) and catheter coronary angiography was obtained before and after microembolization. Cardiac MRI and measurement of cardiac troponin T (cTnT) were performed at baseline, 6 hours, and 1 week after microembolization. Postmortem evaluation was performed after completion of the imaging studies. Coronary angiography pre- and post-microembolization revealed normal epicardial coronary arteries. Systolic wall thickening of the microembolized regions decreased significantly from 42.6 ± 2.0% at baseline to 20.3 ± 2.3% at 6 hours and 31.5 ± 2.1% at 1 week after coronary microembolization (p < 0.001 for both). First-pass perfusion defect was visualized at 6 hours but the extent was largely decreased at 1 week. Delayed contrast enhancement MRI (DE-MRI) demonstrated hyperenhancement within the target area at 6 hours but not at 1 week. The microinfarcts on gross specimen stained with nitrobluetetrazolium chloride were invisible to the naked eye and only detectable microscopically. Increased cTnT was observed at 6 hours and 1 week after microembolization. Coronary microembolization induced by a certain load of small-sized microemboli may result in microinfarcts invisible to the naked eye with normal epicardial coronary arteries. MRI features of myocardial impairment secondary to such microembolization include the decline in left ventricular function and myocardial perfusion at cine and first-pass perfusion imaging, and transient hyperenhancement at DE-MRI.

Magnetic Resonance Imaging Assessment of Squamous Cell Carcinoma of the Anal Canal Before and After Chemoradiation: Can MRI Predict for Eventual Clinical Outcome?

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

Goh, Vicky, E-mail: vicky.goh@stricklandscanner.org.u; Gollub, Frank K.; Liaw, Jonathan

2010-11-01

Purpose: To describe the MRI appearances of squamous cell carcinoma of the anal canal before and after chemoradiation and to assess whether MRI features predict for clinical outcome. Methods and Materials: Thirty-five patients (15 male, 20 female; mean age 60.8 years) with histologically proven squamous cell cancer of the anal canal underwent MRI before and 6-8 weeks after definitive chemoradiation. Images were reviewed retrospectively by two radiologists in consensus blinded to clinical outcome: tumor size, signal intensity, extent, and TNM stage were recorded. Following treatment, patients were defined as responders by T and N downstaging and Response Evaluation Criteria inmore » Solid Tumors (RECIST). Final clinical outcome was determined by imaging and case note review: patients were divided into (1) disease-free and (2) with relapse and compared using appropriate univariate methods to identify imaging predictors; statistical significance was at 5%. Results: The majority of tumors were {<=}T2 (23/35; 65.7%) and N0 (21/35; 60%), mean size 3.75cm, and hyperintense (++ to +++, 24/35 patients; 68%). Following chemoradiation, there was a size reduction in all cases (mean 73.3%) and a reduction in signal intensity in 26/35 patients (74.2%). The majority of patients were classified as responders (26/35 (74.2%) patients by T and N downstaging; and 30/35 (85.7%) patients by RECIST). At a median follow-up of 33.5 months, 25 patients (71.4%) remained disease-free; 10 patients (28.6%) had locoregional or metastatic disease. Univariate analysis showed that no individual MRI features were predictive of eventual outcome. Conclusion: Early assessment of response by MRI at 6-8 weeks is unhelpful in predicting future clinical outcome.« less

Pre-radiographic MRI findings are associated with onset of knee symptoms: the most study

PubMed Central

Javaid, M. K.; Lynch, J. A.; Tolstykh, I.; Guermazi, A.; Roemer, F.; Aliabadi, P.; McCulloch, C.; Curtis, J.; Felson, D.; Lane, N. E.; Torner, J.; Nevitt, M.

2010-01-01

Summary Objective Magnetic resonance imaging (MRI) has greater sensitivity to detect osteoarthritis (OA) damage than radiographs but it is uncertain which MRI findings in early OA are clinically important. We examined MRI abnormalities detected in knees without radiographic OA and their association with incident knee symptoms. Method Participants from the Multicenter Osteoarthritis Study (MOST) without frequent knee symptoms (FKS) at baseline were eligible if they also lacked radiographic features of OA at baseline. At 15 months, knees that developed FKS were defined as cases while control knees were drawn from those that remained without FKS. Baseline MRIs were scored at each subregion for cartilage lesions (CARTs); osteophytes (OST); bone marrow lesions (BML) and cysts. We compared cases and controls using marginal logistic regression models, adjusting for age, gender, race, body mass index (BMI), previous injury and clinic site. Results 36 case knees and 128 control knees were analyzed. MRI damage was common in both cases and controls. The presence of a severe CART (P = 0.03), BML (P = 0.02) or OST (P = 0.02) in the whole knee joint was more common in cases while subchondral cysts did not differ significantly between cases and controls (P > 0.1). Case status at 15 months was predicted by baseline damage at only two locations; a BML in the lateral patella (P = 0.047) and at the tibial subspinous subregions (P = 0.01). Conclusion In knees without significant symptoms or radiographic features of OA, MRI lesions of OA in only a few specific locations preceded onset of clinical symptoms and suggest that changes in bone play a role in the early development of knee pain. Confirmation of these findings in other prospective studies of knee OA is warranted. PMID:19919856

Prevalence of abnormalities in knees detected by MRI in adults without knee osteoarthritis: population based observational study (Framingham Osteoarthritis Study).

PubMed

Guermazi, Ali; Niu, Jingbo; Hayashi, Daichi; Roemer, Frank W; Englund, Martin; Neogi, Tuhina; Aliabadi, Piran; McLennan, Christine E; Felson, David T

2012-08-29

To examine use of magnetic resonance imaging (MRI) of knees with no radiographic evidence of osteoarthritis to determine the prevalence of structural lesions associated with osteoarthritis and their relation to age, sex, and obesity. Population based observational study. Community cohort in Framingham, MA, United States (Framingham osteoarthritis study). 710 people aged >50 who had no radiographic evidence of knee osteoarthritis (Kellgren-Lawrence grade 0) and who underwent MRI of the knee. Prevalence of MRI findings that are suggestive of knee osteoarthritis (osteophytes, cartilage damage, bone marrow lesions, subchondral cysts, meniscal lesions, synovitis, attrition, and ligamentous lesions) in all participants and after stratification by age, sex, body mass index (BMI), and the presence or absence of knee pain. Pain was assessed by three different questions and also by WOMAC questionnaire. Of the 710 participants, 393 (55%) were women, 660 (93%) were white, and 206 (29%) had knee pain in the past month. The mean age was 62.3 years and mean BMI was 27.9. Prevalence of "any abnormality" was 89% (631/710) overall. Osteophytes were the most common abnormality among all participants (74%, 524/710), followed by cartilage damage (69%, 492/710) and bone marrow lesions (52%, 371/710). The higher the age, the higher the prevalence of all types of abnormalities detectable by MRI. There were no significant differences in the prevalence of any of the features between BMI groups. The prevalence of at least one type of pathology ("any abnormality") was high in both painful (90-97%, depending on pain definition) and painless (86-88%) knees. MRI shows lesions in the tibiofemoral joint in most middle aged and elderly people in whom knee radiographs do not show any features of osteoarthritis, regardless of pain.

Acute myonecrosis at MRI: Etiologies in an oncologic cohort, and assessment of interobserver variability

PubMed Central

Cunningham, Jane; Sharma, Richa; Kirzner, Anna; Hwang, Sinchun; Lefkowitz, Robert; Greenspan, Daniel; Shapoval, Anton; Panicek, David M.

2016-01-01

Objective To determine etiologies of myonecrosis in oncology patients and to assess interobserver variability in interpreting its MRI features. Materials and Methods Pathology records in our tertiary cancer hospital were searched for proven myonecrosis, and MRIs of affected regions in those patients were identified. MRI reports that suggested myonecrosis also were identified. Each MRI was reviewed independently by two of six readers to assess anatomic site, size, and signal intensities of muscle changes, and presence of the previously reported stipple sign (enhancing foci within a region defined by rim enhancement). The stipple sign was assessed again, weeks after a training session. Cohen kappa and percent agreement were calculated. Medical records were reviewed for contemporaneous causes of myonecrosis. Results MRI reports in 73 patients suggested the diagnosis of myonecrosis; pathologic proof was available in another two. Myonecrosis was frequently associated with radiotherapy (n=34 (45%) patients)); less frequent causes included intraoperative immobilization, trauma, therapeutic embolization, ablation therapy, exercise, and diabetes. Myonecrosis usually involved lower extremity, pelvis, and upper extremity; mean size was 13.0 cm. Stipple sign was observed in 55–95% of patients at first assessment (k=0.09–0.42; 60–80% agreement) and 55–100% at second (k=0.0–0.58; 72–90% agreement). Enhancement surrounded myonecrosis in 55–100% patients (k=0.03 – 0.32; 58–70% agreement). Conclusion Myonecrosis in oncology patients usually occurred after radiotherapy, and less commonly after intraoperative immobilization, trauma, therapeutic embolization, ablation therapy, exercise, or diabetes. Although interobserver variability for MRI features of myonecrosis exists (even after focused training), a combination of findings facilitates diagnosis and conservative management. PMID:27105618

Identifying key radiogenomic associations between DCE-MRI and micro-RNA expressions for breast cancer

NASA Astrophysics Data System (ADS)

Samala, Ravi K.; Chan, Heang-Ping; Hadjiiski, Lubomir; Helvie, Mark A.; Kim, Renaid

2017-03-01

Understanding the key radiogenomic associations for breast cancer between DCE-MRI and micro-RNA expressions is the foundation for the discovery of radiomic features as biomarkers for assessing tumor progression and prognosis. We conducted a study to analyze the radiogenomic associations for breast cancer using the TCGA-TCIA data set. The core idea that tumor etiology is a function of the behavior of miRNAs is used to build the regression models. The associations based on regression are analyzed for three study outcomes: diagnosis, prognosis, and treatment. The diagnosis group consists of miRNAs associated with clinicopathologic features of breast cancer and significant aberration of expression in breast cancer patients. The prognosis group consists of miRNAs which are closely associated with tumor suppression and regulation of cell proliferation and differentiation. The treatment group consists of miRNAs that contribute significantly to the regulation of metastasis thereby having the potential to be part of therapeutic mechanisms. As a first step, important miRNA expressions were identified and their ability to classify the clinical phenotypes based on the study outcomes was evaluated using the area under the ROC curve (AUC) as a figure-of-merit. The key mapping between the selected miRNAs and radiomic features were determined using least absolute shrinkage and selection operator (LASSO) regression analysis within a two-loop leave-one-out cross-validation strategy. These key associations indicated a number of radiomic features from DCE-MRI to be potential biomarkers for the three study outcomes.

Automatic classification of patients with idiopathic Parkinson's disease and progressive supranuclear palsy using diffusion MRI datasets

NASA Astrophysics Data System (ADS)

Talai, Sahand; Boelmans, Kai; Sedlacik, Jan; Forkert, Nils D.

2017-03-01

Parkinsonian syndromes encompass a spectrum of neurodegenerative diseases, which can be classified into various subtypes. The differentiation of these subtypes is typically conducted based on clinical criteria. Due to the overlap of intra-syndrome symptoms, the accurate differential diagnosis based on clinical guidelines remains a challenge with failure rates up to 25%. The aim of this study is to present an image-based classification method of patients with Parkinson's disease (PD) and patients with progressive supranuclear palsy (PSP), an atypical variant of PD. Therefore, apparent diffusion coefficient (ADC) parameter maps were calculated based on diffusion-tensor magnetic resonance imaging (MRI) datasets. Mean ADC values were determined in 82 brain regions using an atlas-based approach. The extracted mean ADC values for each patient were then used as features for classification using a linear kernel support vector machine classifier. To increase the classification accuracy, a feature selection was performed, which resulted in the top 17 attributes to be used as the final input features. A leave-one-out cross validation based on 56 PD and 21 PSP subjects revealed that the proposed method is capable of differentiating PD and PSP patients with an accuracy of 94.8%. In conclusion, the classification of PD and PSP patients based on ADC features obtained from diffusion MRI datasets is a promising new approach for the differentiation of Parkinsonian syndromes in the broader context of decision support systems.

Discriminative analysis of schizophrenia using support vector machine and recursive feature elimination on structural MRI images.

PubMed

Lu, Xiaobing; Yang, Yongzhe; Wu, Fengchun; Gao, Minjian; Xu, Yong; Zhang, Yue; Yao, Yongcheng; Du, Xin; Li, Chengwei; Wu, Lei; Zhong, Xiaomei; Zhou, Yanling; Fan, Ni; Zheng, Yingjun; Xiong, Dongsheng; Peng, Hongjun; Escudero, Javier; Huang, Biao; Li, Xiaobo; Ning, Yuping; Wu, Kai

2016-07-01

Structural abnormalities in schizophrenia (SZ) patients have been well documented with structural magnetic resonance imaging (MRI) data using voxel-based morphometry (VBM) and region of interest (ROI) analyses. However, these analyses can only detect group-wise differences and thus, have a poor predictive value for individuals. In the present study, we applied a machine learning method that combined support vector machine (SVM) with recursive feature elimination (RFE) to discriminate SZ patients from normal controls (NCs) using their structural MRI data. We first employed both VBM and ROI analyses to compare gray matter volume (GMV) and white matter volume (WMV) between 41 SZ patients and 42 age- and sex-matched NCs. The method of SVM combined with RFE was used to discriminate SZ patients from NCs using significant between-group differences in both GMV and WMV as input features. We found that SZ patients showed GM and WM abnormalities in several brain structures primarily involved in the emotion, memory, and visual systems. An SVM with a RFE classifier using the significant structural abnormalities identified by the VBM analysis as input features achieved the best performance (an accuracy of 88.4%, a sensitivity of 91.9%, and a specificity of 84.4%) in the discriminative analyses of SZ patients. These results suggested that distinct neuroanatomical profiles associated with SZ patients might provide a potential biomarker for disease diagnosis, and machine-learning methods can reveal neurobiological mechanisms in psychiatric diseases.

PET and MRI image fusion based on combination of 2-D Hilbert transform and IHS method.

PubMed

Haddadpour, Mozhdeh; Daneshvar, Sabalan; Seyedarabi, Hadi

2017-08-01

The process of medical image fusion is combining two or more medical images such as Magnetic Resonance Image (MRI) and Positron Emission Tomography (PET) and mapping them to a single image as fused image. So purpose of our study is assisting physicians to diagnose and treat the diseases in the least of the time. We used Magnetic Resonance Image (MRI) and Positron Emission Tomography (PET) as input images, so fused them based on combination of two dimensional Hilbert transform (2-D HT) and Intensity Hue Saturation (IHS) method. Evaluation metrics that we apply are Discrepancy (D k ) as an assessing spectral features and Average Gradient (AG k ) as an evaluating spatial features and also Overall Performance (O.P) to verify properly of the proposed method. In this paper we used three common evaluation metrics like Average Gradient (AG k ) and the lowest Discrepancy (D k ) and Overall Performance (O.P) to evaluate the performance of our method. Simulated and numerical results represent the desired performance of proposed method. Since that the main purpose of medical image fusion is preserving both spatial and spectral features of input images, so based on numerical results of evaluation metrics such as Average Gradient (AG k ), Discrepancy (D k ) and Overall Performance (O.P) and also desired simulated results, it can be concluded that our proposed method can preserve both spatial and spectral features of input images. Copyright © 2017 Chang Gung University. Published by Elsevier B.V. All rights reserved.

Altered resting-state whole-brain functional networks of neonates with intrauterine growth restriction.

PubMed

Batalle, Dafnis; Muñoz-Moreno, Emma; Tornador, Cristian; Bargallo, Nuria; Deco, Gustavo; Eixarch, Elisenda; Gratacos, Eduard

2016-04-01

The feasibility to use functional MRI (fMRI) during natural sleep to assess low-frequency basal brain activity fluctuations in human neonates has been demonstrated, although its potential to characterise pathologies of prenatal origin has not yet been exploited. In the present study, we used intrauterine growth restriction (IUGR) as a model of altered neurodevelopment due to prenatal condition to show the suitability of brain networks to characterise functional brain organisation at neonatal age. Particularly, we analysed resting-state fMRI signal of 20 neonates with IUGR and 13 controls, obtaining whole-brain functional networks based on correlations of blood oxygen level-dependent (BOLD) signal in 90 grey matter regions of an anatomical atlas (AAL). Characterisation of the networks obtained with graph theoretical features showed increased network infrastructure and raw efficiencies but reduced efficiency after normalisation, demonstrating hyper-connected but sub-optimally organised IUGR functional brain networks. Significant association of network features with neurobehavioral scores was also found. Further assessment of spatiotemporal dynamics displayed alterations into features associated to frontal, cingulate and lingual cortices. These findings show the capacity of functional brain networks to characterise brain reorganisation from an early age, and their potential to develop biomarkers of altered neurodevelopment. Copyright © 2016 Elsevier Ltd. All rights reserved.

Population Response Profiles in Early Visual Cortex Are Biased in Favor of More Valuable Stimuli

PubMed Central

Saproo, Sameer

2010-01-01

Voluntary and stimulus-driven shifts of attention can modulate the representation of behaviorally relevant stimuli in early areas of visual cortex. In turn, attended items are processed faster and more accurately, facilitating the selection of appropriate behavioral responses. Information processing is also strongly influenced by past experience and recent studies indicate that the learned value of a stimulus can influence relatively late stages of decision making such as the process of selecting a motor response. However, the learned value of a stimulus can also influence the magnitude of cortical responses in early sensory areas such as V1 and S1. These early effects of stimulus value are presumed to improve the quality of sensory representations; however, the nature of these modulations is not clear. They could reflect nonspecific changes in response amplitude associated with changes in general arousal or they could reflect a bias in population responses so that high-value features are represented more robustly. To examine this issue, subjects performed a two-alternative forced choice paradigm with a variable-interval payoff schedule to dynamically manipulate the relative value of two stimuli defined by their orientation (one was rotated clockwise from vertical, the other counterclockwise). Activation levels in visual cortex were monitored using functional MRI and feature-selective voxel tuning functions while subjects performed the behavioral task. The results suggest that value not only modulates the relative amplitude of responses in early areas of human visual cortex, but also sharpens the response profile across the populations of feature-selective neurons that encode the critical stimulus feature (orientation). Moreover, changes in space- or feature-based attention cannot easily explain the results because representations of both the selected and the unselected stimuli underwent a similar feature-selective modulation. This sharpening in the population response profile could theoretically improve the probability of correctly discriminating high-value stimuli from low-value alternatives. PMID:20410360

The value of right ventricular longitudinal strain in the evaluation of adult patients with repaired tetralogy of Fallot: a new tool for a contemporary challenge.

PubMed

Almeida-Morais, Luís; Pereira-da-Silva, Tiago; Branco, Luísa; Timóteo, Ana T; Agapito, Ana; de Sousa, Lídia; Oliveira, José A; Thomas, Boban; Jalles-Tavares, Nuno; Soares, Rui; Galrinho, Ana; Cruz-Ferreira, Rui

2017-04-01

The role of right ventricular longitudinal strain for assessing patients with repaired tetralogy of Fallot is not fully understood. In this study, we aimed to evaluate its relation with other structural and functional parameters in these patients. Patients followed-up in a grown-up CHD unit, assessed by transthoracic echocardiography, cardiac MRI, and treadmill exercise testing, were retrospectively evaluated. Right ventricular size and function and pulmonary regurgitation severity were assessed by echocardiography and MRI. Right ventricular longitudinal strain was evaluated in the four-chamber view using the standard semiautomatic method. In total, 42 patients were included (61% male, 32±8 years). The mean right ventricular longitudinal strain was -16.2±3.7%, and the right ventricular ejection fraction, measured by MRI, was 42.9±7.2%. Longitudinal strain showed linear correlation with tricuspid annular systolic excursion (r=-0.40) and right ventricular ejection fraction (r=-0.45) (all p<0.05), which in turn showed linear correlation with right ventricular fractional area change (r=0.50), pulmonary regurgitation colour length (r=0.35), right ventricular end-systolic volume (r=-0.60), and left ventricular ejection fraction (r=0.36) (all p<0.05). Longitudinal strain (β=-0.72, 95% confidence interval -1.41, -0.15) and left ventricular ejection fraction (β=0.39, 95% confidence interval 0.11, 0.67) were independently associated with right ventricular ejection fraction. The best threshold of longitudinal strain for predicting a right ventricular ejection fraction of <40% was -17.0%. Right ventricular longitudinal strain is a powerful method for evaluating patients with tetralogy of Fallot. It correlated with echocardiographic right ventricular function parameters and was independently associated with right ventricular ejection fraction derived by MRI.

Prognostic value of residual fluorescent tissue in glioblastoma patients after gross total resection in 5-aminolevulinic Acid-guided surgery.

PubMed

Aldave, Guillermo; Tejada, Sonia; Pay, Eva; Marigil, Miguel; Bejarano, Bartolomé; Idoate, Miguel A; Díez-Valle, Ricardo

2013-06-01

There is evidence in the literature supporting that fluorescent tissue signal in fluorescence-guided surgery extends farther than tissue highlighted in gadolinium in T1 sequence magnetic resonance imaging (MRI), which is the standard to quantify the extent of resection. To study whether the presence of residual fluorescent tissue after surgery carries a different prognosis for glioblastoma (GBM) cases with complete resection confirmed by MRI. A retrospective review in our center found 118 consecutive patients with high-grade gliomas operated on with the use of fluorescence-guided surgery with 5-aminolevulinic acid. Within that series, the 52 patients with newly diagnosed GBM and complete resection of enhancing tumor (CRET) in early MRI were selected for analysis. We studied the influence of residual fluorescence in the surgical field on overall survival and neurological complication rate. Multivariate analysis included potential relevant factors: age, Karnofsky Performance Scale, O-methylguanine methyltransferase methylation promoter status, tumor eloquent location, preoperative tumor volume, and adjuvant therapy. The median overall survival was 27.0 months (confidence interval = 22.4-31.6) in patients with nonresidual fluorescence (n = 25) and 17.5 months (confidence interval = 12.5-22.5) for the group with residual fluorescence (n = 27) (P = .015). The influence of residual fluorescence was maintained in the multivariate analysis with all covariables, hazard ratio = 2.5 (P = .041). The neurological complication rate was 18.5% in patients with nonresidual fluorescence and 8% for the group with residual fluorescence (P = .267). GBM patients with CRET in early MRI and no fluorescent residual tissue had longer overall survival than patients with CRET and residual fluorescent tissue.

Onset of multiple sclerosis before adulthood leads to failure of age-expected brain growth

PubMed Central

Aubert-Broche, Bérengère; Fonov, Vladimir; Narayanan, Sridar; Arnold, Douglas L.; Araujo, David; Fetco, Dumitru; Till, Christine; Sled, John G.; Collins, D. Louis

2014-01-01

Objective: To determine the impact of pediatric-onset multiple sclerosis (MS) on age-expected brain growth. Methods: Whole brain and regional volumes of 36 patients with relapsing-remitting MS onset prior to 18 years of age were segmented in 185 longitudinal MRI scans (2–11 scans per participant, 3-month to 2-year scan intervals). MRI scans of 25 age- and sex-matched healthy normal controls (NC) were also acquired at baseline and 2 years later on the same scanner as the MS group. A total of 874 scans from 339 participants from the NIH-funded MRI study of normal brain development acquired at 2-year intervals were used as an age-expected healthy growth reference. All data were analyzed with an automatic image processing pipeline to estimate the volume of brain and brain substructures. Mixed-effect models were built using age, sex, and group as fixed effects. Results: Significant group and age interactions were found with the adjusted models fitting brain volumes and normalized thalamus volumes (p < 10−4). These findings indicate a failure of age-normative brain growth for the MS group, and an even greater failure of thalamic growth. In patients with MS, T2 lesion volume correlated with a greater reduction in age-expected thalamic volume. To exclude any scanner-related influence on our data, we confirmed no significant interaction of group in the adjusted models between the NC and NIH MRI Study of Normal Brain Development groups. Conclusions: Our results provide evidence that the onset of MS during childhood and adolescence limits age-expected primary brain growth and leads to subsequent brain atrophy, implicating an early onset of the neurodegenerative aspect of MS. PMID:25378667

Another look at ultrasound and magnetic resonance imaging for diagnosis of placenta accreta.

PubMed

Budorick, Nancy E; Figueroa, Reinaldo; Vizcarra, Michael; Shin, James

2017-10-01

To compare the ability of magnetic resonance imaging (MRI) and ultrasound (US) in the diagnosis of placenta accreta, to examine the success of various sonographic and MRI features to correctly predict invasive placenta, and to define a specific role for MRI in placenta accreta. After Institutional Review Board approval, a blinded retrospective review was undertaken of US and MRI findings from 45 patients who had an obstetrical US and placental MRI between August 2006 and January 2012. Correlation with clinical history and pathologic findings was performed. US and MRI had similar sensitivity, specificity and positive and negative predictive values for placenta accreta. The best predictors of invasion by US were loss of the myometrial mantle, increased intraplacental vascularity and loss of the bladder wall echogenicity. The best predictors of invasion by MRI were loss of retroplacental myometrial mantle, a heterogeneous placenta, and intraplacental hemorrhage. Body mass index (BMI) did not affect the ability to make a diagnosis by either US or MRI. MRI proved effective in better evaluation of a posterior placenta with suspicion of placenta accreta. There was modality disagreement in 11 of 45 cases and MRI was correct in 9 of these 11 cases, all true negative (TN) cases. MRI should be considered in any case with posterior placenta previa and suspicion of accreta, in any case with clinical suspicion for accreta and discordant US findings, and in any case in which percreta is suspected.

MRI-based decision tree model for diagnosis of biliary atresia.

PubMed

Kim, Yong Hee; Kim, Myung-Joon; Shin, Hyun Joo; Yoon, Haesung; Han, Seok Joo; Koh, Hong; Roh, Yun Ho; Lee, Mi-Jung

2018-02-23

To evaluate MRI findings and to generate a decision tree model for diagnosis of biliary atresia (BA) in infants with jaundice. We retrospectively reviewed features of MRI and ultrasonography (US) performed in infants with jaundice between January 2009 and June 2016 under approval of the institutional review board, including the maximum diameter of periportal signal change on MRI (MR triangular cord thickness, MR-TCT) or US (US-TCT), visibility of common bile duct (CBD) and abnormality of gallbladder (GB). Hepatic subcapsular flow was reviewed on Doppler US. We performed conditional inference tree analysis using MRI findings to generate a decision tree model. A total of 208 infants were included, 112 in the BA group and 96 in the non-BA group. Mean age at the time of MRI was 58.7 ± 36.6 days. Visibility of CBD, abnormality of GB and MR-TCT were good discriminators for the diagnosis of BA and the MRI-based decision tree using these findings with MR-TCT cut-off 5.1 mm showed 97.3 % sensitivity, 94.8 % specificity and 96.2 % accuracy. MRI-based decision tree model reliably differentiates BA in infants with jaundice. MRI can be an objective imaging modality for the diagnosis of BA. • MRI-based decision tree model reliably differentiates biliary atresia in neonatal cholestasis. • Common bile duct, gallbladder and periportal signal changes are the discriminators. • MRI has comparable performance to ultrasonography for diagnosis of biliary atresia.

An fMRI Study of the Neural Systems Involved in Visually Cued Auditory Top-Down Spatial and Temporal Attention

PubMed Central

Li, Chunlin; Chen, Kewei; Han, Hongbin; Chui, Dehua; Wu, Jinglong

2012-01-01

Top-down attention to spatial and temporal cues has been thoroughly studied in the visual domain. However, because the neural systems that are important for auditory top-down temporal attention (i.e., attention based on time interval cues) remain undefined, the differences in brain activity between directed attention to auditory spatial location (compared with time intervals) are unclear. Using fMRI (magnetic resonance imaging), we measured the activations caused by cue-target paradigms by inducing the visual cueing of attention to an auditory target within a spatial or temporal domain. Imaging results showed that the dorsal frontoparietal network (dFPN), which consists of the bilateral intraparietal sulcus and the frontal eye field, responded to spatial orienting of attention, but activity was absent in the bilateral frontal eye field (FEF) during temporal orienting of attention. Furthermore, the fMRI results indicated that activity in the right ventrolateral prefrontal cortex (VLPFC) was significantly stronger during spatial orienting of attention than during temporal orienting of attention, while the DLPFC showed no significant differences between the two processes. We conclude that the bilateral dFPN and the right VLPFC contribute to auditory spatial orienting of attention. Furthermore, specific activations related to temporal cognition were confirmed within the superior occipital gyrus, tegmentum, motor area, thalamus and putamen. PMID:23166800

How Necessary Are the Stripes of a Tiger? Diagnostic and Characteristic Features in an fMRI Study of Word Meaning

ERIC Educational Resources Information Center

Grossman, Murray; Troiani, Vanessa; Koenig, Phyllis; Work, Melissa; Moore, Peachie

2007-01-01

This study contrasted two approaches to word meaning: the statistically determined role of high-contribution features like "striped" in the meaning of complex nouns like "tiger" typically used in studies of semantic memory, and the contribution of diagnostic features like "parent's brother" that play a critical role in the meaning of nominal kinds…

Understanding Patient Preference in Female Pelvic Imaging: Transvaginal Ultrasound and MRI.

PubMed

Sakala, Michelle D; Carlos, Ruth C; Mendiratta-Lala, Mishal; Quint, Elisabeth H; Maturen, Katherine E

2018-04-01

Women with pelvic pain or abnormal uterine bleeding may undergo diagnostic imaging. This study evaluates patient experience in transvaginal ultrasound (TVUS) and magnetic resonance imaging (MRI) and explores correlations between preference and symptom severity. Institutional review board approval was obtained for this Health Insurance Portability and Accountability Act-compliant prospective study. Fifty premenopausal women with pelvic symptoms evaluated by recent TVUS and MRI and without history of gynecologic cancer or hysterectomy were included. A phone questionnaire used validated survey instruments including Uterine Fibroid Symptoms Quality of Life index, Testing Morbidities Index, and Wait Trade Off for TVUS and MRI examinations. Using Wait Trade Off, patients preferred TVUS over MRI (3.58 vs 2.80 weeks, 95% confidence interval [CI] -1.63, 0.12; P = .08). Summary test utility of Testing Morbidities Index for MRI was worse than for TVUS (81.64 vs 87.42, 95%CI 0.41, 11.15; P = .03). Patients reported greater embarrassment during TVUS than during MRI (P <.0001), but greater fear and anxiety both before (P <.0001) and during (P <.001) MRI, and greater mental (P = .02) and physical (P = .02) problems after MRI versus TVUS. Subscale correlations showed physically inactive women rated TVUS more negatively (R = -0.32, P = .03), whereas women with more severe symptoms of loss of control of health (R = -0.28, P = .04) and sexual dysfunction (R = -0.30, P = .03) rated MRI more negatively. Women with pelvic symptoms had a slight but significant preference for TVUS over MRI. Identifying specific distressing aspects of each test and patient factors contributing to negative perceptions can direct improvement in both test environment and patient preparation. Improved patient experience may increase imaging value. Copyright © 2018 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.

Assessment of radiofrequency ablation margin by MRI-MRI image fusion in hepatocellular carcinoma.

PubMed

Wang, Xiao-Li; Li, Kai; Su, Zhong-Zhen; Huang, Ze-Ping; Wang, Ping; Zheng, Rong-Qin

2015-05-07

To investigate the feasibility and clinical value of magnetic resonance imaging (MRI)-MRI image fusion in assessing the ablative margin (AM) for hepatocellular carcinoma (HCC). A newly developed ultrasound workstation for MRI-MRI image fusion was used to evaluate the AM of 62 tumors in 52 HCC patients after radiofrequency ablation (RFA). The lesions were divided into two groups: group A, in which the tumor was completely ablated and 5 mm AM was achieved (n = 32); and group B, in which the tumor was completely ablated but 5 mm AM was not achieved (n = 29). To detect local tumor progression (LTP), all patients were followed every two months by contrast-enhanced ultrasound, contrast-enhanced MRI or computed tomography (CT) in the first year after RFA. Then, the follow-up interval was prolonged to every three months after the first year. Of the 62 tumors, MRI-MRI image fusion was successful in 61 (98.4%); the remaining case had significant deformation of the liver and massive ascites after RFA. The time required for creating image fusion and AM evaluation was 15.5 ± 5.5 min (range: 8-22 min) and 9.6 ± 3.2 min (range: 6-14 min), respectively. The follow-up period ranged from 1-23 mo (14.2 ± 5.4 mo). In group A, no LTP was detected in 32 lesions, whereas in group B, LTP was detected in 4 of 29 tumors, which occurred at 2, 7, 9, and 15 mo after RFA. The frequency of LTP in group B (13.8%; 4/29) was significantly higher than that in group A (0/32, P = 0.046). All of the LTPs occurred in the area in which the 5 mm AM was not achieved. The MRI-MRI image fusion using an ultrasound workstation is feasible and useful for evaluating the AM after RFA for HCC.

Assessment of radiofrequency ablation margin by MRI-MRI image fusion in hepatocellular carcinoma

PubMed Central

Wang, Xiao-Li; Li, Kai; Su, Zhong-Zhen; Huang, Ze-Ping; Wang, Ping; Zheng, Rong-Qin

2015-01-01

AIM: To investigate the feasibility and clinical value of magnetic resonance imaging (MRI)-MRI image fusion in assessing the ablative margin (AM) for hepatocellular carcinoma (HCC). METHODS: A newly developed ultrasound workstation for MRI-MRI image fusion was used to evaluate the AM of 62 tumors in 52 HCC patients after radiofrequency ablation (RFA). The lesions were divided into two groups: group A, in which the tumor was completely ablated and 5 mm AM was achieved (n = 32); and group B, in which the tumor was completely ablated but 5 mm AM was not achieved (n = 29). To detect local tumor progression (LTP), all patients were followed every two months by contrast-enhanced ultrasound, contrast-enhanced MRI or computed tomography (CT) in the first year after RFA. Then, the follow-up interval was prolonged to every three months after the first year. RESULTS: Of the 62 tumors, MRI-MRI image fusion was successful in 61 (98.4%); the remaining case had significant deformation of the liver and massive ascites after RFA. The time required for creating image fusion and AM evaluation was 15.5 ± 5.5 min (range: 8-22 min) and 9.6 ± 3.2 min (range: 6-14 min), respectively. The follow-up period ranged from 1-23 mo (14.2 ± 5.4 mo). In group A, no LTP was detected in 32 lesions, whereas in group B, LTP was detected in 4 of 29 tumors, which occurred at 2, 7, 9, and 15 mo after RFA. The frequency of LTP in group B (13.8%; 4/29) was significantly higher than that in group A (0/32, P = 0.046). All of the LTPs occurred in the area in which the 5 mm AM was not achieved. CONCLUSION: The MRI-MRI image fusion using an ultrasound workstation is feasible and useful for evaluating the AM after RFA for HCC. PMID:25954109

Prediction of BRAF mutation status of craniopharyngioma using magnetic resonance imaging features.

PubMed

Yue, Qi; Yu, Yang; Shi, Zhifeng; Wang, Yongfei; Zhu, Wei; Du, Zunguo; Yao, Zhenwei; Chen, Liang; Mao, Ying

2017-10-06

OBJECTIVE Treatment with a BRAF mutation inhibitor might shrink otherwise refractory craniopharyngiomas and is a promising preoperative treatment to facilitate tumor resection. The aim of this study was to investigate the noninvasive diagnosis of BRAF-mutated craniopharyngiomas based on MRI characteristics. METHODS Fifty-two patients with pathologically diagnosed craniopharyngioma were included in this study. Polymerase chain reaction was performed on tumor tissue specimens to detect BRAF and CTNNB1 mutations. MRI manifestations-including tumor location, size, shape, and composition; signal intensity of cysts; enhancement pattern; pituitary stalk morphology; and encasement of the internal carotid artery-were analyzed by 2 neuroradiologists blinded to patient identity and clinical characteristics, including BRAF mutation status. Results were compared between the BRAF-mutated and wild-type (WT) groups. Characteristics that were significantly more prevalent (p < 0.05) in the BRAF-mutated craniopharyngiomas were defined as diagnostic features. The minimum number of diagnostic features needed to make a diagnosis was determined by analyzing the receiver operating characteristic (ROC) curve. RESULTS Eight of the 52 patients had BRAF-mutated craniopharyngiomas, and the remaining 44 had BRAF WT tumors. The clinical characteristics did not differ significantly between the 2 groups. Interobserver agreement for MRI data analysis was relatively reliable, with values of Cohen κ ranging from 0.65 to 0.97 (p < 0.001). A comparison of findings in the 2 patient groups showed that BRAF-mutated craniopharyngiomas tended to be suprasellar (p < 0.001), spherical (p = 0.005), predominantly solid (p = 0.003), and homogeneously enhancing (p < 0.001), and that patients with these tumors tended to have a thickened pituitary stalk (p = 0.014). When at least 3 of these 5 features were present, a tumor might be identified as BRAF mutated with a sensitivity of 1.00 and a specificity of 0.91. The area under the ROC curve for the sum of all 5 diagnostic criteria was 0.989 (p < 0.001). CONCLUSIONS The BRAF mutation status of craniopharyngiomas might be predicted using certain MRI features with relatively high sensitivity and specificity, thus offering potential guidance for the preoperative administration of BRAF mutation inhibitors.

Alzheimer's Disease Early Diagnosis Using Manifold-Based Semi-Supervised Learning.

PubMed

Khajehnejad, Moein; Saatlou, Forough Habibollahi; Mohammadzade, Hoda

2017-08-20

Alzheimer's disease (AD) is currently ranked as the sixth leading cause of death in the United States and recent estimates indicate that the disorder may rank third, just behind heart disease and cancer, as a cause of death for older people. Clearly, predicting this disease in the early stages and preventing it from progressing is of great importance. The diagnosis of Alzheimer's disease (AD) requires a variety of medical tests, which leads to huge amounts of multivariate heterogeneous data. It can be difficult and exhausting to manually compare, visualize, and analyze this data due to the heterogeneous nature of medical tests; therefore, an efficient approach for accurate prediction of the condition of the brain through the classification of magnetic resonance imaging (MRI) images is greatly beneficial and yet very challenging. In this paper, a novel approach is proposed for the diagnosis of very early stages of AD through an efficient classification of brain MRI images, which uses label propagation in a manifold-based semi-supervised learning framework. We first apply voxel morphometry analysis to extract some of the most critical AD-related features of brain images from the original MRI volumes and also gray matter (GM) segmentation volumes. The features must capture the most discriminative properties that vary between a healthy and Alzheimer-affected brain. Next, we perform a principal component analysis (PCA)-based dimension reduction on the extracted features for faster yet sufficiently accurate analysis. To make the best use of the captured features, we present a hybrid manifold learning framework which embeds the feature vectors in a subspace. Next, using a small set of labeled training data, we apply a label propagation method in the created manifold space to predict the labels of the remaining images and classify them in the two groups of mild Alzheimer's and normal condition (MCI/NC). The accuracy of the classification using the proposed method is 93.86% for the Open Access Series of Imaging Studies (OASIS) database of MRI brain images, providing, compared to the best existing methods, a 3% lower error rate.

F-18 fluoride positron emission tomography/computed tomography in the diagnosis of avascular necrosis of the femoral head: Comparison with magnetic resonance imaging

PubMed Central

Gayana, Shankaramurthy; Bhattacharya, Anish; Sen, Ramesh Kumar; Singh, Paramjeet; Prakash, Mahesh; Mittal, Bhagwant Rai

2016-01-01

Objective: Femoral head avascular necrosis (FHAVN) is one of the increasingly common causes of musculoskeletal disability and poses a major diagnostic and therapeutic challenge. Although radiography, scintigraphy, computed tomography (CT), and magnetic resonance imaging (MRI) have been widely used in the diagnosis of FHAVN, positron emission tomography (PET) has recently been evaluated to assess vascularity of the femoral head. In this study, the authors compared F-18 fluoride PET/CT with MRI in the initial diagnosis of FHAVN. Patients and Methods: We prospectively studied 51 consecutive patients with a high clinical suspicion of FHAVN. All patients underwent MRI and F-18 fluoride PET/CT, the time interval between the two scans being 4–10 (mean 8) days. Two nuclear medicine physicians blinded to the MRI report read the PET/CT scans. Clinical assessment was also done. Final diagnoses were made by surgical pathology or clinical and radiologic follow-up. Results: A final diagnosis of avascular necrosis (AVN) was made in 40 patients. MRI was 96.5% sensitive, 100% specific, and 98.03% accurate while PET/CT was 100% sensitive, specific, and accurate in diagnosing FHAVN. The agreement between the two imaging modalities for the diagnosis of AVN was 96.07%. Conclusion: F-18 fluoride PET/CT showed good agreement with MRI in the initial diagnosis of FHAVN and can be better than MRI in detecting early disease. PMID:26917886

Contrast-enhanced computed tomography plus gadolinium-ethoxybenzyl diethylenetriamine pentaacetic acid-enhanced magnetic resonance imaging for gross classification of hepatocellular carcinoma.

PubMed

Chen, Chuang; Zhao, Hui; Fu, Xu; Huang, LuoShun; Tang, Min; Yan, XiaoPeng; Sun, ShiQuan; Jia, WenJun; Mao, Liang; Shi, Jiong; Chen, Jun; He, Jian; Zhu, Jin; Qiu, YuDong

2017-05-02

Accurate gross classification through imaging is critical for determination of hepatocellular carcinoma (HCC) patient prognoses and treatment strategies. The present retrospective study evaluated the utility of contrast-enhanced computed tomography (CE-CT) combined with gadolinium-ethoxybenzyl diethylenetriamine pentaacetic acid-enhanced magnetic resonance imaging (EOB-MRI) for diagnosis and classification of HCCs prior to surgery. Ninety-four surgically resected HCC nodules were classified as simple nodular (SN), SN with extranodular growth (SN-EG), confluent multinodular (CMN), or infiltrative (IF) types. SN-EG, CMN and IF samples were grouped as non-SN. The abilities of the two imaging modalities to differentiate non-SN from SN HCCs were assessed using the EOB-MRI hepatobiliary phase and CE-CT arterial, portal, and equilibrium phases. Areas under the ROC curves for non-SN diagnoses were 0.765 (95% confidence interval [CI]: 0.666-0.846) for CE-CT, 0.877 (95% CI: 0.793-0.936) for EOB-MRI, and 0.908 (95% CI: 0.830-0.958) for CE-CT plus EOB-MRI. Sensitivities, specificities, and accuracies with respect to identification of non-SN tumors of all sizes were 71.4%, 81.6%, and 75.5% for CE-CT; 96.4%, 78.9%, and 89.3% for EOB-MRI; and 98.2%, 84.2%, and 92.5% for CE-CT plus EOB-MRI. These results show that CE-CT combined with EOB-MRI offers a more accurate imaging evaluation for HCC gross classification than either modality alone.

Segmentation of human brain using structural MRI.

PubMed

Helms, Gunther

2016-04-01

Segmentation of human brain using structural MRI is a key step of processing in imaging neuroscience. The methods have undergone a rapid development in the past two decades and are now widely available. This non-technical review aims at providing an overview and basic understanding of the most common software. Starting with the basis of structural MRI contrast in brain and imaging protocols, the concepts of voxel-based and surface-based segmentation are discussed. Special emphasis is given to the typical contrast features and morphological constraints of cortical and sub-cortical grey matter. In addition to the use for voxel-based morphometry, basic applications in quantitative MRI, cortical thickness estimations, and atrophy measurements as well as assignment of cortical regions and deep brain nuclei are briefly discussed. Finally, some fields for clinical applications are given.

STAMPS: Software Tool for Automated MRI Post-processing on a supercomputer.

PubMed

Bigler, Don C; Aksu, Yaman; Miller, David J; Yang, Qing X

2009-08-01

This paper describes a Software Tool for Automated MRI Post-processing (STAMP) of multiple types of brain MRIs on a workstation and for parallel processing on a supercomputer (STAMPS). This software tool enables the automation of nonlinear registration for a large image set and for multiple MR image types. The tool uses standard brain MRI post-processing tools (such as SPM, FSL, and HAMMER) for multiple MR image types in a pipeline fashion. It also contains novel MRI post-processing features. The STAMP image outputs can be used to perform brain analysis using Statistical Parametric Mapping (SPM) or single-/multi-image modality brain analysis using Support Vector Machines (SVMs). Since STAMPS is PBS-based, the supercomputer may be a multi-node computer cluster or one of the latest multi-core computers.

Statistical Feature Extraction for Artifact Removal from Concurrent fMRI-EEG Recordings

PubMed Central

Liu, Zhongming; de Zwart, Jacco A.; van Gelderen, Peter; Kuo, Li-Wei; Duyn, Jeff H.

2011-01-01

We propose a set of algorithms for sequentially removing artifacts related to MRI gradient switching and cardiac pulsations from electroencephalography (EEG) data recorded during functional magnetic resonance imaging (fMRI). Special emphases are directed upon the use of statistical metrics and methods for the extraction and selection of features that characterize gradient and pulse artifacts. To remove gradient artifacts, we use a channel-wise filtering based on singular value decomposition (SVD). To remove pulse artifacts, we first decompose data into temporally independent components and then select a compact cluster of components that possess sustained high mutual information with the electrocardiogram (ECG). After the removal of these components, the time courses of remaining components are filtered by SVD to remove the temporal patterns phase-locked to the cardiac markers derived from the ECG. The filtered component time courses are then inversely transformed into multi-channel EEG time series free of pulse artifacts. Evaluation based on a large set of simultaneous EEG-fMRI data obtained during a variety of behavioral tasks, sensory stimulations and resting conditions showed excellent data quality and robust performance attainable by the proposed methods. These algorithms have been implemented as a Matlab-based toolbox made freely available for public access and research use. PMID:22036675

Primary perivascular epithelioid cell tumors of the liver: CT/MRI findings and clinical outcomes.

PubMed

O'Malley, Martin E; Chawla, Tanya P; Lavelle, Lisa P; Cleary, Sean; Fischer, Sandra

2017-06-01

The purpose of our study was to describe the CT and MRI features of primary PEComas of the liver and to document the associated clinical outcomes. Retrospective study included 20 patients with primary hepatic perivascular epithelioid cell tumors (PEComa) with pathology and clinical outcomes for correlation. Study group included 20 patients: 16 women, 4 men; mean age 53 (range 35-77) years. Initial pathology diagnoses were classic angiomyolipoma (AML) (n = 11), epithelioid AML (n = 7), and PEComa not otherwise specified (n = 2). Mean tumor size was 5.1 (range 1.3-15.0) cm. CT/MRI features included well-defined margins 20/20 (100%), arterial enhancement 18/19 (95%), subcapsular location 17/20 (85%), heterogeneous 16/20 (80%), dysmorphic vessels 14/20 (70%), fat 13/20 (65%), hemorrhage 4/20 (20%), cystic components 4/20 (20%), and calcification 1/20 (5%). At the time of discovery, 18 patients were asymptomatic and their tumors were incidentally detected on imaging, and 2 patients were symptomatic. Ultimately, 18 tumors were benign and 2 developed metastases. On CT/MRI, most primary hepatic PEComas were well-defined, arterial enhancing, subcapsular, heterogeneous masses that often had dysmorphic vessels and contained fat. Most tumors were benign but complications included local symptoms, bleeding, and malignant change.

Bayesian framework inspired no-reference region-of-interest quality measure for brain MRI images

PubMed Central

Osadebey, Michael; Pedersen, Marius; Arnold, Douglas; Wendel-Mitoraj, Katrina

2017-01-01

Abstract. We describe a postacquisition, attribute-based quality assessment method for brain magnetic resonance imaging (MRI) images. It is based on the application of Bayes theory to the relationship between entropy and image quality attributes. The entropy feature image of a slice is segmented into low- and high-entropy regions. For each entropy region, there are three separate observations of contrast, standard deviation, and sharpness quality attributes. A quality index for a quality attribute is the posterior probability of an entropy region given any corresponding region in a feature image where quality attribute is observed. Prior belief in each entropy region is determined from normalized total clique potential (TCP) energy of the slice. For TCP below the predefined threshold, the prior probability for a region is determined by deviation of its percentage composition in the slice from a standard normal distribution built from 250 MRI volume data provided by Alzheimer’s Disease Neuroimaging Initiative. For TCP above the threshold, the prior is computed using a mathematical model that describes the TCP–noise level relationship in brain MRI images. Our proposed method assesses the image quality of each entropy region and the global image. Experimental results demonstrate good correlation with subjective opinions of radiologists for different types and levels of quality distortions. PMID:28630885