Sushmita Datta

Unified Approach for Multiple Sclerosis Lesion Segmentation on Brain MRI

The presence of large number of false lesion classification on segmented brain MR images is a major problem in the accurate determination of lesion volumes in multiple sclerosis (MS) brains. In order to minimize the false lesion classifications, a strategy that combines parametric and nonparametric techniques is developed and implemented. This approach uses the information from the proton density (PD)- and T2-weighted and fluid attenuation inversion recovery (FLAIR) images. This strategy involves CSF and lesion classification using the Parzen window classifier. Image processing, morphological operations, and ratio maps of PD- and T2-weighted images are used for minimizing false positives. Contextual information is exploited for minimizing the false negative lesion classifications using hidden Markov random field-expectation maximization (HMRF-EM) algorithm. Lesions are delineated using fuzzy connectivity. The performance of this algorithm is quantitatively evaluated on 23 MS patients. Similarity index, percentages of over, under, and correct estimations of lesions are computed by spatially comparing the results of present procedure with expert manual segmentation. The automated processing scheme detected 80% of the manually segmented lesions in the case of low lesion load and 93% of the lesions in those cases with high lesion load.

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.

Deep gray matter atrophy in multiple sclerosis: A tensor based morphometry

Tensor based morphometry (TBM) was applied to determine the atrophy of deep gray matter (DGM) structures in 88 relapsing multiple sclerosis (MS) patients. For group analysis of atrophy, an unbiased atlas was constructed from 20 normal brains. The MS brain images were co-registered with the unbiased atlas using a symmetric inverse consistent nonlinear registration. These studies demonstrate significant atrophy of thalamus, caudate nucleus, and putamen even at a modest clinical disability, as assessed by the expanded disability status score (EDSS). A significant correlation between atrophy and EDSS was observed for different DGM structures: (thalamus: r=-0.51, p=3.85 x 10(-7); caudate nucleus: r=-0.43, p=2.35 x 10(-5); putamen: r=-0.36, p=6.12 x 10(-6)). Atrophy of these structures also correlated with 1) T2 hyperintense lesion volumes (thalamus: r=-0.56, p=9.96 x 10(-9); caudate nucleus: r=-0.31, p=3.10 x 10(-3); putamen: r=-0.50, p=6.06 x 10(-7)), 2) T1 hypointense lesion volumes (thalamus: r=-0.61, p=2.29 x 10(-10); caudate nucleus: r=-0.35, p=9.51 x 10(-4); putamen: r=-0.43, p=3.51 x 10(-5)), and 3) normalized CSF volume (thalamus: r=-0.66, p=3.55 x 10(-12); caudate nucleus: r=-0.52, p=2.31 x 10(-7), and putamen: r=-0.66, r=2.13 x 10(-12)). More severe atrophy was observed mainly in thalamus at higher EDSS. These studies appear to suggest a link between the white matter damage and DGM atrophy in MS.

Segmentation and quantification of black holes in multiple sclerosis.

A technique that involves minimal operator intervention was developed and implemented for identification and quantification of black holes on T1-weighted magnetic resonance images (T1 images) in multiple sclerosis (MS). Black holes were segmented on T1 images based on grayscale morphological operations. False classification of black holes was minimized by masking the segmented images with images obtained from the orthogonalization of T2-weighted and T1 images. Enhancing lesion voxels on postcontrast images were automatically identified and eliminated from being included in the black hole volume. Fuzzy connectivity was used for the delineation of black holes. The performance of this algorithm was quantitatively evaluated on 14 MS patients.

Magnetic resonance imaging outcomes from a phase III trial of teriflunomide

Objective: The purpose of this study was to determine the effects of oral teriflunomide on multiple sclerosis (MS) pathology inferred by magnetic resonance imaging (MRI). Methods: Patients (n=1088) with relapsing MS were randomized to once-daily teriflunomide 7 mg or 14 mg, or placebo, for 108 weeks. MRI was recorded at baseline, 24, 48, 72 and 108 weeks. Annualized relapse rate and confirmed progression of disability (sustained ≥12 weeks) were the primary and key secondary outcomes. The principal MRI outcome was change in total lesion volume. Results: After 108 weeks, increase in total lesion volume was 67.4% (p=0.0003) and 39.4% (p=0.0317) lower in the 14 and 7 mg dose groups versus placebo. Other measures favoring teriflunomide were accumulated enhanced lesions, combined unique activity, T2-hyperintense and T1-hypointense component lesion volumes, white matter volume, and a composite MRI score; all were significant for teriflunomide 14 mg and most significant for 7 mg versus placebo. Conclusions: Teriflunomide provided benefits on brain MRI activity across multiple measures, with a dose effect evident on several markers. These effects were also consistent across selected subgroups of the study population. These findings complement clinical data showing significant teriflunomide-related reductions in relapse rate and disease progression, and demonstrate containment of MRI-defined disease progression.

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.

Generalized fuzzy clustering for segmentation of multi-spectral magnetic resonance images.

An integrated approach for multi-spectral segmentation of MR images is presented. This method is based on the fuzzy c-means (FCM) and includes bias field correction and contextual constraints over spatial intensity distribution and accounts for the non-spherical clusters shape in the feature space. The bias field is modeled as a linear combination of smooth polynomial basis functions for fast computation in the clustering iterations. Regularization terms for the neighborhood continuity of intensity are added into the FCM cost functions. To reduce the computational complexity, the contextual regularizations are separated from the clustering iterations. Since the feature space is not isotropic, distance measure adopted in Gustafson-Kessel (G-K) algorithm is used instead of the Euclidean distance, to account for the non-spherical shape of the clusters in the feature space. These algorithms are quantitatively evaluated on MR brain images using the similarity measures.

Segmentation of gadolinium-enhanced lesions on MRI in multiple sclerosis

To develop and implement a method for identification and quantification of gadolinium (Gd) enhancements with minimal human intervention.

Human brain atlas-based multimodal MRI analysis of volumetry, diffusimetry, relaxometry and lesion distribution in multiple sclerosis patients and healthy adult controls: Implications for understanding the pathogenesis of multiple sclerosis and consolidation of quantitative MRI results in MS

Multiple sclerosis (MS) is the most common immune-mediated disabling neurological disease of the central nervous system. The pathogenesis of MS is not fully understood. Histopathology implicates both demyelination and axonal degeneration as the major contributors to the accumulation of disability. The application of several in vivo quantitative magnetic resonance imaging (MRI) methods to both lesioned and normal-appearing brain tissue has not yet provided a solid conclusive support of the hypothesis that MS might be a diffuse disease. In this work, we adopted FreeSurfer to provide standardized macrostructure or volumetry of lesion free normal-appearing brain tissue in combination with multiple quantitative MRI metrics (T(2) relaxation time, diffusion tensor anisotropy and diffusivities) that characterize tissue microstructural integrity. By incorporating a large number of healthy controls, we have attempted to separate the natural age-related change from the disease-induced effects. Our work shows elevation in diffusivity and relaxation times and reduction in volume in a number of normal-appearing white matter and gray matter structures in relapsing-remitting multiple sclerosis patients. These changes were related in part with the spatial distribution of lesions. The whole brain lesion load and age-adjusted expanded disability status score showed strongest correlations in regions such as corpus callosum with qMRI metrics that are believed to be specific markers of axonal dysfunction, consistent with histologic data of others indicating axonal loss that is independent of focal lesions. Our results support that MS at least in part has a neurodegenerative component.

Comprehensive brain MRI segmentation in high risk preterm newborns.

Most extremely preterm newborns exhibit cerebral atrophy/growth disturbances and white matter signal abnormalities on MRI at term-equivalent age. MRI brain volumes could serve as biomarkers for evaluating the effects of neonatal intensive care and predicting neurodevelopmental outcomes. This requires detailed, accurate, and reliable brain MRI segmentation methods. We describe our efforts to develop such methods in high risk newborns using a combination of manual and automated segmentation tools. After intensive efforts to accurately define structural boundaries, two trained raters independently performed manual segmentation of nine subcortical structures using axial T2-weighted MRI scans from 20 randomly selected extremely preterm infants. All scans were re-segmented by both raters to assess reliability. High intra-rater reliability was achieved, as assessed by repeatability and intra-class correlation coefficients (ICC range: 0.97 to 0.99) for all manually segmented regions. Inter-rater reliability was slightly lower (ICC range: 0.93 to 0.99). A semi-automated segmentation approach was developed that combined the parametric strengths of the Hidden Markov Random Field Expectation Maximization algorithm with non-parametric Parzen window classifier resulting in accurate white matter, gray matter, and CSF segmentation. Final manual correction of misclassification errors improved accuracy (similarity index range: 0.87 to 0.89) and facilitated objective quantification of white matter signal abnormalities. The semi-automated and manual methods were seamlessly integrated to generate full brain segmentation within two hours. This comprehensive approach can facilitate the evaluation of large cohorts to rigorously evaluate the utility of regional brain volumes as biomarkers of neonatal care and surrogate endpoints for neurodevelopmental outcomes.