Segmentation of gadolinium-enhanced lesions on MRI in multiple sclerosis

AI-based detection of contrast-enhancing MRI lesions in patients with multiple sclerosis

BACKGROUND

Contrast-enhancing (CE) lesions are an important finding on brain magnetic resonance imaging (MRI) in patients with multiple sclerosis (MS) but can be missed easily. Automated solutions for reliable CE lesion detection are emerging; however, independent validation of artificial intelligence (AI) tools in the clinical routine is still rare.

METHODS

A three-dimensional convolutional neural network for CE lesion segmentation was trained externally on 1488 datasets of 934 MS patients from 81 scanners using concatenated information from FLAIR and T1-weighted post-contrast imaging. This externally trained model was tested on an independent dataset comprising 504 T1-weighted post-contrast and FLAIR image datasets of MS patients from clinical routine. Two neuroradiologists (R1, R2) labeled CE lesions for gold standard definition in the clinical test dataset. The algorithmic output was evaluated on both patient- and lesion-level.

RESULTS

On a patient-level, recall, specificity, precision, and accuracy of the AI tool to predict patients with CE lesions were 0.75, 0.99, 0.91, and 0.96. The agreement between the AI tool and both readers was within the range of inter-rater agreement (Cohen's kappa; AI vs. R1: 0.69; AI vs. R2: 0.76; R1 vs. R2: 0.76). On a lesion-level, false negative lesions were predominately found in infratentorial location, significantly smaller, and at lower contrast than true positive lesions (p < 0.05).

CONCLUSIONS

AI-based identification of CE lesions on brain MRI is feasible, approaching human reader performance in independent clinical data and might be of help as a second reader in the neuroradiological assessment of active inflammation in MS patients.

CRITICAL RELEVANCE STATEMENT

Al-based detection of contrast-enhancing multiple sclerosis lesions approaches human reader performance, but careful visual inspection is still needed, especially for infratentorial, small and low-contrast lesions.

Deep learning segmentation of gadolinium-enhancing lesions in multiple sclerosis

OBJECTIVE

The aim of this study is to assess the performance of deep learning convolutional neural networks (CNNs) in segmenting gadolinium-enhancing lesions using a large cohort of multiple sclerosis (MS) patients.

METHODS

A three-dimensional (3D) CNN model was trained for segmentation of gadolinium-enhancing lesions using multispectral magnetic resonance imaging data (MRI) from 1006 relapsing-remitting MS patients. The network performance was evaluated for three combinations of multispectral MRI used as input: (U5) fluid-attenuated inversion recovery (FLAIR), T2-weighted, proton density-weighted, and pre- and post-contrast T1-weighted images; (U2) pre- and post-contrast T1-weighted images; and (U1) only post-contrast T1-weighted images. Segmentation performance was evaluated using the Dice similarity coefficient (DSC) and lesion-wise true-positive (TPR) and false-positive (FPR) rates. Performance was also evaluated as a function of enhancing lesion volume.

RESULTS

The DSC/TPR/FPR values averaged over all the enhancing lesion sizes were 0.77/0.90/0.23 using the U5 model. These values for the largest enhancement volumes (>500 mm3) were 0.81/0.97/0.04. For U2, the average DSC/TPR/FPR values were 0.72/0.86/0.31. Comparable performance was observed with U1. For all types of input, the network performance degraded with decreased enhancement size.

CONCLUSION

Excellent segmentation of enhancing lesions was observed for enhancement volume ⩾70 mm3. The best performance was achieved when the input included all five multispectral image sets.

Automatic brain lesion segmentation on standard magnetic resonance images: a scoping review

OBJECTIVES

Medical image analysis practices face challenges that can potentially be addressed with algorithm-based segmentation tools. In this study, we map the field of automatic MR brain lesion segmentation to understand the clinical applicability of prevalent methods and study designs, as well as challenges and limitations in the field.

DESIGN

Scoping review.

SETTING

Three databases (PubMed, IEEE Xplore and Scopus) were searched with tailored queries. Studies were included based on predefined criteria. Emerging themes during consecutive title, abstract, methods and whole-text screening were identified. The full-text analysis focused on materials, preprocessing, performance evaluation and comparison.

RESULTS

Out of 2990 unique articles identified through the search, 441 articles met the eligibility criteria, with an estimated growth rate of 10% per year. We present a general overview and trends in the field with regard to publication sources, segmentation principles used and types of lesions. Algorithms are predominantly evaluated by measuring the agreement of segmentation results with a trusted reference. Few articles describe measures of clinical validity.

CONCLUSIONS

The observed reporting practices leave room for improvement with a view to studying replication, method comparison and clinical applicability. To promote this improvement, we propose a list of recommendations for future studies in the field.

Deep Learning for Predicting Enhancing Lesions in Multiple Sclerosis from Noncontrast MRI

Background Enhancing lesions on MRI scans obtained after contrast material administration are commonly thought to represent disease activity in multiple sclerosis (MS); it is desirable to develop methods that can predict enhancing lesions without the use of contrast material. Purpose To evaluate whether deep learning can predict enhancing lesions on MRI scans obtained without the use of contrast material. Materials and Methods This study involved prospective analysis of existing MRI data. A convolutional neural network was used for classification of enhancing lesions on unenhanced MRI scans. This classification was performed for each slice, and the slice scores were combined by using a fully connected network to produce participant-wise predictions. The network input consisted of 1970 multiparametric MRI scans from 1008 patients recruited from 2005 to 2009. Enhanced lesions on postcontrast T1-weighted images served as the ground truth. The network performance was assessed by using fivefold cross-validation. Statistical analysis of the network performance included calculation of lesion detection rates and areas under the receiver operating characteristic curve (AUCs). Results MRI scans from 1008 participants (mean age, 37.7 years ± 9.7; 730 women) were analyzed. At least one enhancing lesion was observed in 519 participants. The sensitivity and specificity averaged across the five test sets were 78% ± 4.3 and 73% ± 2.7, respectively, for slice-wise prediction. The corresponding participant-wise values were 72% ± 9.0 and 70% ± 6.3. The diagnostic performances (AUCs) were 0.82 ± 0.02 and 0.75 ± 0.03 for slice-wise and participant-wise enhancement prediction, respectively. Conclusion Deep learning used with conventional MRI identified enhanced lesions in multiple sclerosis from images from unenhanced multiparametric MRI with moderate to high accuracy. © RSNA, 2019.

Novel fMRI working memory paradigm accurately detects cognitive impairment in multiple sclerosis

BACKGROUND

Cognitive impairment (CI) cannot be diagnosed by magnetic resonance imaging (MRI). Functional magnetic resonance imaging (fMRI) paradigms, such as the immediate/delayed memory task (I/DMT), detect varying degrees of working memory (WM). Preliminary findings using I/DMT showed differences in blood oxygenation level dependent (BOLD) activation between impaired (MSCI, n = 12) and non-impaired (MSNI, n = 9) multiple sclerosis (MS) patients.

OBJECTIVES

The aim of the study was to confirm CI detection based on I/DMT BOLD activation in a larger cohort of MS patients. The role of T2 lesion volume (LV) and Expanded Disability Status Scale (EDSS) in magnitude of BOLD signal was also sought.

METHODS

A total of 50 patients (EDSS mean ( m) = 3.2, disease duration (DD) m = 12 years, and age m = 40 years) underwent the Minimal Assessment of Cognitive Function in Multiple Sclerosis (MACFIMS) and I/DMT. Working memory activation (WMa) represents BOLD signal during DMT minus signal during IMT. CI was based on MACFIMS.

RESULTS

A total of 10 MSNI, 30 MSCI, and 4 borderline patients were included in the analyses. Analysis of variance (ANOVA) showed MSNI had significantly greater WMa than MSCI, in the left prefrontal cortex and left supplementary motor area ( p = 0.032). Regression analysis showed significant inverse correlations between WMa and T2 LV/EDSS in similar areas ( p = 0.005, 0.004, respectively).

CONCLUSION

I/DMT-based BOLD activation detects CI in MS. Larger studies are needed to confirm these findings.

A semi-automated measuring system of brain diffusion and perfusion magnetic resonance imaging abnormalities in patients with multiple sclerosis based on the integration of coregistration and tissue segmentation procedures

Background

Diffusion-weighted imaging (DWI) and perfusion-weighted imaging (PWI) abnormalities in patients with multiple sclerosis (MS) are currently measured by a complex combination of separate procedures. Therefore, the purpose of this study was to provide a reliable method for reducing analysis complexity and obtaining reproducible results.

Methods

We implemented a semi-automated measuring system in which different well-known software components for magnetic resonance imaging (MRI) analysis are integrated to obtain reliable measurements of DWI and PWI disturbances in MS.

Results

We generated the Diffusion/Perfusion Project (DPP) Suite, in which a series of external software programs are managed and harmonically and hierarchically incorporated by in-house developed Matlab software to perform the following processes: 1) image pre-processing, including imaging data anonymization and conversion from DICOM to Nifti format; 2) co-registration of 2D and 3D non-enhanced and Gd-enhanced T1-weighted images in fluid-attenuated inversion recovery (FLAIR) space; 3) lesion segmentation and classification, in which FLAIR lesions are at first segmented and then categorized according to their presumed evolution; 4) co-registration of segmented FLAIR lesion in T1 space to obtain the FLAIR lesion mask in the T1 space; 5) normal appearing tissue segmentation, in which T1 lesion mask is used to segment basal ganglia/thalami, normal appearing grey matter (NAGM) and normal appearing white matter (NAWM); 6) DWI and PWI map generation; 7) co-registration of basal ganglia/thalami, NAGM, NAWM, DWI and PWI maps in previously segmented FLAIR space; 8) data analysis. All these steps are automatic, except for lesion segmentation and classification.

Conclusion

We developed a promising method to limit misclassifications and user errors, providing clinical researchers with a practical and reproducible tool to measure DWI and PWI changes in MS.

Antivirus immune activity in multiple sclerosis correlates with MRI activity

OBJECTIVE

The objective of this study was to determine whether reactivation of Epstein-Barr (EBV) or activation of the anti-EBV immune response correlates with MS disease activity on MR imaging.

METHODS

Subjects with early, active relapsing-remitting MS were studied for 16 weeks with blood and saliva samples collected every 2 weeks and brain MRI performed every 4 weeks. We isolated peripheral blood mononuclear cells from each blood sample and tested the immune response to EBV, autologous EBV-infected lymphoblastoid cell lines (LCL), human herpesvirus 6 (HHV6), varicella zoster virus (VZV), tetanus, and mitogens. We measured the proliferative response and the number of interferon-γ secreting cells with ELISPOT. We measured the amounts of EBV, HHV6, and VZV DNA in blood and saliva with quantitative PCR. On MRI, we measured number and volume of contrast enhancing and T2 lesions. We tested for correlation between the immunologic assays and the MRI results, assessing different time intervals between the MRI and immunologic assays.

RESULTS

We studied 20 subjects. Ten had enhancing lesions on one or more MRI scans and one had new T2 lesions without enhancement. The most significant correlation was between proliferation to autologous LCL and the number of combined unique active lesions on MRI 4 weeks later. Both proliferation and number of cells secreting interferon-γ in response to LCL correlated with the number of enhancing lesions 8 weeks later.

CONCLUSIONS

We find evidence for correlation of antiviral immune responses in the blood with subsequent disease activity on MRI scans.

Adaptive multi-level conditional random fields for detection and segmentation of small enhanced pathology in medical images

Detection and segmentation of large structures in an image or within a region of interest have received great attention in the medical image processing domains. However, the problem of small pathology detection and segmentation still remains an unresolved challenge due to the small size of these pathologies, their low contrast and variable position, shape and texture. In many contexts, early detection of these pathologies is critical in diagnosis and assessing the outcome of treatment. In this paper, we propose a probabilistic Adaptive Multi-level Conditional Random Fields (AMCRF) with the incorporation of higher order cliques for detecting and segmenting such pathologies. In the first level of our graphical model, a voxel-based CRF is used to identify candidate lesions. In the second level, in order to further remove falsely detected regions, a new CRF is developed that incorporates higher order textural features, which are invariant to rotation and local intensity distortions. At this level, higher order textures are considered together with the voxel-wise cliques to refine boundaries and is therefore adaptive. The proposed algorithm is tested in the context of detecting enhancing Multiple Sclerosis (MS) lesions in brain MRI, where the problem is further complicated as many of the enhancing voxels are associated with normal structures (i.e. blood vessels) or noise in the MRI. The algorithm is trained and tested on large multi-center clinical trials from Relapsing-Remitting MS patients. The effect of several different parameter learning and inference techniques is further investigated. When tested on 120 cases, the proposed method reaches a lesion detection rate of 90%, with very few false positive lesion counts on average, ranging from 0.17 for very small (3-5 voxels) to 0 for very large (50+ voxels) regions. The proposed model is further tested on a very large clinical trial containing 2770 scans where a high sensitivity of 91% with an average false positive count of 0.5 is achieved. Incorporation of contextual information at different scales is also explored. Finally, superior performance is shown upon comparing with Support Vector Machine (SVM), Random Forest and variant of an MRF.

Effect of in-painting on cortical thickness measurements in multiple sclerosis: A large cohort study

A comprehensive analysis of the effect of lesion in-painting on the estimation of cortical thickness using magnetic resonance imaging was performed on a large cohort of 918 relapsing-remitting multiple sclerosis patients who participated in a phase III multicenter clinical trial. An automatic lesion in-painting algorithm was developed and implemented. Cortical thickness was measured using the FreeSurfer pipeline with and without in-painting. The effect of in-painting was evaluated using FreeSurfer's paired analysis pipeline. Multivariate regression analysis was also performed with field strength and lesion load as additional factors. Overall, the estimated cortical thickness was different with in-painting than without. The effect of in-painting was observed to be region dependent, more significant in the left hemisphere compared to the right, was more prominent at 1.5 T relative to 3 T, and was greater at higher lesion volumes. Our results show that even for data acquired at 1.5 T in patients with high lesion load, the mean cortical thickness difference with and without in-painting is ∼2%. Based on these results, it appears that in-painting has only a small effect on the estimated regional and global cortical thickness. Hum Brain Mapp 36:3749-3760, 2015. © 2015 Wiley Periodicals, Inc.

Temporal Hierarchical Adaptive Texture CRF for Automatic Detection of Gadolinium-Enhancing Multiple Sclerosis Lesions in Brain MRI

We propose a conditional random field (CRF) based classifier for segmentation of small enhanced pathologies. Specifically, we develop a temporal hierarchical adaptive texture CRF (THAT-CRF) and apply it to the challenging problem of gad enhancing lesion segmentation in brain MRI of patients with multiple sclerosis. In this context, the presence of many nonlesion enhancements (such as blood vessels) renders the problem more difficult. In addition to voxel-wise features, the framework exploits multiple higher order textures to discriminate the true lesional enhancements from the pool of other enhancements. Since lesional enhancements show more variation over time as compared to the nonlesional ones, we incorporate temporal texture analysis in order to study the textures of enhanced candidates over time. The parameters of the THAT-CRF model are learned based on 2380 scans from a multi-center clinical trial. The effect of different components of the model is extensively evaluated on 120 scans from a separate multi-center clinical trial. The incorporation of the temporal textures results in a general decrease of the false discovery rate. Specifically, THAT-CRF achieves overall sensitivity of 95% along with false discovery rate of 20% and average false positive count of 0.5 lesions per scan. The sensitivity of the temporal method to the trained time interval is further investigated on five different intervals of 69 patients. Moreover, superior performance is achieved by the reviewed labelings of our model compared to the fully manual labeling when applied to the context of separating different treatment arms in a real clinical trial.

Regional gray matter atrophy in relapsing remitting multiple sclerosis: baseline analysis of multi-center data

Regional gray matter (GM) atrophy in multiple sclerosis (MS) at disease onset and its temporal variation can provide objective information regarding disease evolution. An automated pipeline for estimating atrophy of various GM structures was developed using tensor based morphometry (TBM) and implemented on a multi-center sub-cohort of 1008 relapsing remitting MS (RRMS) patients enrolled in a Phase 3 clinical trial. Four hundred age and gender matched healthy controls were used for comparison. Using the analysis of covariance, atrophy differences between MS patients and healthy controls were assessed on a voxel-by-voxel analysis. Regional GM atrophy was observed in a number of deep GM structures that included thalamus, caudate nucleus, putamen, and cortical GM regions. General linear regression analysis was performed to analyze the effects of age, gender, and scanner field strength, and imaging sequence on the regional atrophy. Correlations between regional GM volumes and expanded disability status scale (EDSS) scores, disease duration (DD), T2 lesion load (T2 LL), T1 lesion load (T1 LL), and normalized cerebrospinal fluid (nCSF) were analyzed using Pearson׳s correlation coefficient. Thalamic atrophy observed in MS patients compared to healthy controls remained consistent within subgroups based on gender and scanner field strength. Weak correlations between thalamic volume and EDSS (r=-0.133; p<0.001) and DD (r=-0.098; p=0.003) were observed. Of all the structures, thalamic volume moderately correlated with T2 LL (r=-0.492; P-value<0.001), T1 LL (r=-0.473; P-value<0.001) and nCSF (r=-0.367; P-value<0.001).

Automatic segmentation and volumetric quantification of white matter hyperintensities on fluid-attenuated inversion recovery images using the extreme value distribution

INTRODUCTION

This study aims to develop an automatic segmentation framework on the basis of extreme value distribution (EVD) for the detection and volumetric quantification of white matter hyperintensities (WMHs) on fluid-attenuated inversion recovery (FLAIR) images.

METHODS

Two EVD-based segmentation methods, namely the Gumbel and Fréchet segmentation, were developed to detect WMHs on FLAIR (slice thickness = 5 mm; TR/TE/TI = 11,000/120/2,800 ms; flip angle = 90°) images. Another automatic segmentation method using a trimmed likelihood estimator (TLE) was implemented for comparison with our proposed segmentation framework. The performances of the three automatic segmentation methods were evaluated by comparing with the manual segmentation method.

RESULTS

The Dice similarity coefficients (DSCs) of the two EVD-based segmentation methods were larger than those of the TLE-based segmentation method (Gumbel, 0.823 ± 0.063; Fréchet, 0.843 ± 0.057; TLE, 0.817 ± 0.068), demonstrating that the EVD-based segmentation outperformed the TLE-based segmentation. The Fréchet segmentation obtained larger DSCs on patients with moderate to severe lesion loads and a comparable performance on patients with mild lesion loads, indicating that the Fréchet segmentation was superior to the Gumbel segmentation. The Gumbel segmentation underestimated the lesion volumes of all patients, whereas the Fréchet and TLE-based segmentation methods obtained overestimated lesion volumes (Manual, 13.71 ± 14.02 cc; Gumbel, 12.73 ± 13.21 cc; Fréchet, 13.88 ± 13.96 cc; TLE, 13.54 ± 12.27 cc). Moreover, the EVD-based segmentation was demonstrated to be comparable to other state-of-the-art methods on a publicly available dataset.

CONCLUSION

The proposed EVD-based segmentation framework is a promising, effective, and convenient tool for volumetric quantification and further study of WMHs in aging and dementia.

Hypoperfusion and T1-hypointense lesions in white matter in multiple sclerosis

BACKGROUND

Longitudinal magnetic resonance imaging (MRI) studies show that a fraction of the multiple sclerosis (MS) T2-lesions contain T1-hypointense components that may persist to represent severe, irreversible tissue damage. It is not known why certain lesions convert to persistent T1-hypointense lesions.

OBJECTIVE

We hypothesized that the T1-hypointense lesions disproportionately distribute in the more hypoperfused areas of the brain. Here we investigated the association between hypoperfusion and T1-hypointense lesion distributions.

METHODS

MRI and cerebral blood flow (CBF) data were acquired on 45 multiple sclerosis (MS) patients and 20 healthy controls. CBF maps were generated using pseudo-continuous arterial spin labeling technique. The lesion probability distribution maps were superimposed on the CBF maps.

RESULTS

Two distinct CBF clusters were observed in the white matter (WM) both in healthy controls and MS patients. An overall reduction in CBF was observed in MS patients compared to healthy controls. The majority of the T1-hypointense lesions were concentrated almost exclusively in the WM regions with lower CBF. The T2-hyperintense lesions were more generally distributed in both higher and lower perfused WM.

CONCLUSION

This study suggests an association between hypoperfusion and T1-hypointense lesions.

Increasing the contrast of the brain MR FLAIR images using fuzzy membership functions and structural similarity indices in order to segment MS lesions

Segmentation is an important step for the diagnosis of multiple sclerosis (MS). This paper presents a new approach to the fully automatic segmentation of MS lesions in Fluid Attenuated Inversion Recovery (FLAIR) Magnetic Resonance (MR) images. With the aim of increasing the contrast of the FLAIR MR images with respect to the MS lesions, the proposed method first estimates the fuzzy memberships of brain tissues (i.e., the cerebrospinal fluid (CSF), the normal-appearing brain tissue (NABT), and the lesion). The procedure for determining the fuzzy regions of their member functions is performed by maximizing fuzzy entropy through Genetic Algorithm. Research shows that the intersection points of the obtained membership functions are not accurate enough to segment brain tissues. Then, by extracting the structural similarity (SSIM) indices between the FLAIR MR image and its lesions membership image, a new contrast-enhanced image is created in which MS lesions have high contrast against other tissues. Finally, the new contrast-enhanced image is used to segment MS lesions. To evaluate the result of the proposed method, similarity criteria from all slices from 20 MS patients are calculated and compared with other methods, which include manual segmentation. The volume of segmented lesions is also computed and compared with Gold standard using the Intraclass Correlation Coefficient (ICC) and paired samples t test. Similarity index for the patients with small lesion load, moderate lesion load and large lesion load was 0.7261, 0.7745 and 0.8231, respectively. The average overall similarity index for all patients is 0.7649. The t test result indicates that there is no statistically significant difference between the automatic and manual segmentation. The validated results show that this approach is very promising.

Hierarchical conditional random fields for detection of gad-enhancing lesions in multiple sclerosis

The detection of gad-enhancing lesions in brain MRI of Multiple Sclerosis (MS) patients is of great interest since they are important markers of disease activity. However, many of the enhancing voxels are associated with normal structures (i.e. blood vessels) or noise in the MRI, making the detection of gad-enhancing lesions a challenging task. Furthermore, these lesions are typically small and in close proximity to vessels. In this paper, we present an automatic, probabilistic Hierarchical Conditional Random Field (HCRF) framework for detection of gad-enhancing lesions in brain images of patients with MS. In the first level, a CRF with unary and pairwise potentials is used to identify candidate lesion voxel. In the second level, these lesion candidates are grouped based on anatomical and spatial features, and feature-specific lesion based CRF models are designed for each group. This lesion level CRF incorporates higher order potentials which account for shape, group intensities and symmetries. The proposed algorithm is trained on 92 multimodal clinical datasets acquired from Relapsing-Remitting MS patients during multicenter clinical trials and is evaluated on 30 independent cases. The experimental results show a sensitivity of 98%, a positive predictive value of 66% and an average false positive count of 1.55, outperforming the CRF and MRF frameworks proposed in.

A comprehensive approach to the segmentation of multichannel three-dimensional MR brain images in multiple sclerosis

Accurate classification and quantification of brain tissues is important for monitoring disease progression, measurement of atrophy, and correlating magnetic resonance (MR) measures with clinical disability. Classification of MR brain images in the presence of lesions, such as multiple sclerosis (MS), is particularly challenging. Images obtained with lower resolution often suffer from partial volume averaging leading to false classifications. While partial volume averaging can be reduced by acquiring volumetric images at high resolution, image segmentation and quantification can be technically challenging. In this study, we integrated the brain anatomical knowledge with non-parametric and parametric statistical classifiers for automatically classifying tissues and lesions on high resolution multichannel three-dimensional images acquired on 60 MS brains. The results of automatic lesion segmentation were reviewed by the expert. The agreement between results obtained by the automated analysis and the expert was excellent as assessed by the quantitative metrics, low absolute volume difference percent (36.18 ± 34.90), low average symmetric surface distance (1.64 mm ± 1.30 mm), high true positive rate (84.75 ± 12.69), and low false positive rate (34.10 ± 16.00). The segmented results were also in close agreement with the corrected results as assessed by Bland-Altman and regression analyses. Finally, our lesion segmentation was validated using the MS lesion segmentation grand challenge dataset (MICCAI 2008).

Regional cortical thickness in relapsing remitting multiple sclerosis: A multi-center study

A comprehensive analysis of the global and regional values of cortical thickness based on 3D magnetic resonance images was performed on 250 relapsing remitting multiple sclerosis (MS) patients who participated in a multi-center, randomized, phase III clinical trial (the CombiRx Trial) and 125 normal controls. The MS cohort was characterized by relatively low clinical disability and short disease duration. An automatic pipeline was developed for identifying images with poor quality and artifacts. The global and regional cortical thicknesses were determined using FreeSurfer software. Our results indicate significant cortical thinning in multiple regions in the MS patient cohort relative to the controls. Both global cortical thinning and regional cortical thinning were more prominent in the left hemisphere relative to the right hemisphere. Modest correlation was observed between cortical thickness and clinical measures that included the extended disability status scale and disease duration. Modest correlation was also observed between cortical thickness and T1-hypointense and T2-hyperintense lesions. These correlations were very similar at 1.5 T and 3 T field strengths. A much weaker inverse correlation between cortical thickness and age was observed among the MS subjects compared to normal controls. This age-dependent correlation was also stronger in males than in females. The values of cortical thickness were very similar at 1.5 T and 3 T field strengths. However, the age-dependent changes in both global and regional cortical thicknesses were observed to be stronger at 3 T relative to 1.5 T.

Automatic detection of gadolinium-enhancing multiple sclerosis lesions in brain MRI using conditional random fields

Gadolinium-enhancing lesions in brain magnetic resonance imaging of multiple sclerosis (MS) patients are of great interest since they are markers of disease activity. Identification of gadolinium-enhancing lesions is particularly challenging because the vast majority of enhancing voxels are associated with normal structures, particularly blood vessels. Furthermore, these lesions are typically small and in close proximity to vessels. In this paper, we present an automatic, probabilistic framework for segmentation of gadolinium-enhancing lesions in MS using conditional random fields. Our approach, through the integration of different components, encodes different information such as correspondence between the intensities and tissue labels, patterns in the labels, or patterns in the intensities. The proposed algorithm is evaluated on 80 multimodal clinical datasets acquired from relapsing-remitting MS patients in the context of multicenter clinical trials. The experimental results exhibit a sensitivity of 98% with a low false positive lesion count. The performance of the proposed algorithm is also compared to a logistic regression classifier, a support vector machine and a Markov random field approach. The results demonstrate superior performance of the proposed algorithm at successfully detecting all of the gadolinium-enhancing lesions while maintaining a low false positive lesion count.

Intracortical lesions by 3T magnetic resonance imaging and correlation with cognitive impairment in multiple sclerosis

BACKGROUND

Accurate classification of multiple sclerosis (MS) lesions in the brain cortex may be important in understanding their impact on cognitive impairment (CI). Improved accuracy in identification/classification of cortical lesions was demonstrated in a study combining two magnetic resonance imaging (MRI) sequences: double inversion recovery (DIR) and T1-weighted phase-sensitive inversion recovery (PSIR).

OBJECTIVE

To evaluate the role of intracortical lesions (IC) in MS-related CI and compare it with the role of mixed (MX), juxtacortical (JX), the sum of IC + MX and with total lesions as detected on DIR/PSIR images. Correlations between CI and brain atrophy, disease severity and disease duration were also sought.

METHODS

A total of 39 patients underwent extensive neuropsychological testing and were classified into normal and impaired groups. Images were obtained on a 3T scanner and cortical lesions were assessed blind to the cognitive status of the subjects.

RESULTS

Some 238 cortical lesions were identified (130 IC, 108 MX) in 82% of the patients; 39 JX lesions were also identified. Correlations between CI and MX lesions alone (p = 0.010) and with the sum of IC + MX lesions (p = 0.030) were found. A correlation between severity of CI and Expanded Disability Status Scale was also seen (p = 0.009).

CONCLUSION

Cortical lesions play an important role in CI. However, our results suggest that lesions that remain contained within the cortical ribbon do not play a more important role than ones extending into the adjacent white matter; furthermore, the size of the cortical lesion, and not the tissue-specific location, may better explain their correlation with CI.

Automated brain extraction from T2-weighted magnetic resonance images

PURPOSE

To develop and implement an automated and robust technique to extract brain from T2-weighted images.

MATERIALS AND METHODS

Magnetic resonance imaging (MRI) was performed on 75 adult volunteers to acquire dual fast spin echo (FSE) images with fat-saturation technique on a 3T Philips scanner. Histogram-derived thresholds were derived directly from the original images followed by the application of regional labeling, regional connectivity, and mathematical morphological operations to extract brain from axial late-echo FSE (T2-weighted) images. The proposed technique was evaluated subjectively by an expert and quantitatively using Bland-Altman plot and Jaccard and Dice similarity measures.

RESULTS

Excellent agreement between the extracted brain volumes with the proposed technique and manual stripping by an expert was observed based on Bland-Altman plot and also as assessed by high similarity indices (Jaccard: 0.9825 ± 0.0045; Dice: 0.9912 ± 0.0023).

CONCLUSION

Brain extraction using the proposed automated methodology is robust and the results are reproducible.

MRI in multiple sclerosis: what's inside the toolbox?

Magnetic resonance imaging (MRI) has played a central role in the diagnosis and management of multiple sclerosis (MS). In addition, MRI metrics have become key supportive outcome measures to explore drug efficacy in clinical trials. Conventional MRI measures have contributed to the understanding of MS pathophysiology at the macroscopic level yet have failed to provide a complete picture of underlying MS pathology. They also show relatively weak relationships to clinical status such as predictive strength for clinical progression. Advanced quantitative MRI measures such as magnetization transfer, spectroscopy, diffusion imaging, and relaxometry techniques are somewhat more specific and sensitive for underlying pathology. These measures are particularly useful in revealing diffuse damage in cerebral white and gray matter and therefore may help resolve the dissociation between clinical and conventional MRI findings. In this article, we provide an overview of the array of tools available with brain and spinal cord MRI technology as it is applied to MS. We review the most recent data regarding the role of conventional and advanced MRI techniques in the assessment of MS. We focus on the most relevant pathologic and clinical correlation studies relevant to these measures.