Characterization of multiple sclerosis lesions with distinct clinical correlates through quantitative diffusion MRI
•Macroscopic and microscopic diffusion properties discriminate between MS lesion types.•The number and volume of lesions with larger diffusion changes are associated with worse clinical outcomes.•Diffusion MRI provides useful information of the pathological heterogeneity in plaques. Diffusion magnet...
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| Published in: | NeuroImage clinical 2020-01, Vol.28, p.102411-102411, Article 102411 |
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| Main Authors: | , , , , , , , , , , , , , , |
| Format: | Article |
| Language: | eng |
| Subjects: | |
| Online Access: | Get full text |
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| Summary: | •Macroscopic and microscopic diffusion properties discriminate between MS lesion types.•The number and volume of lesions with larger diffusion changes are associated with worse clinical outcomes.•Diffusion MRI provides useful information of the pathological heterogeneity in plaques.
Diffusion magnetic resonance imaging can reveal quantitative information about the tissue changes in multiple sclerosis. The recently developed multi-compartment spherical mean technique can map different microscopic properties based only on local diffusion signals, and it may provide specific information on the underlying microstructural modifications that arise in multiple sclerosis. Given that the lesions in multiple sclerosis may reflect different degrees of damage, we hypothesized that quantitative diffusion maps may help characterize the severity of lesions “in vivo” and correlate these to an individual’s clinical profile. We evaluated this in a cohort of 59 multiple sclerosis patients (62% female, mean age 44.7 years), for whom demographic and disease information was obtained, and who underwent a comprehensive physical and cognitive evaluation. The magnetic resonance imaging protocol included conventional sequences to define focal lesions, and multi-shell diffusion imaging was used with b-values of 1000, 2000 and 3000 s/mm2 in 180 encoding directions. Quantitative diffusion properties on a macro- and micro-scale were used to discriminate distinct types of lesions through a k-means clustering algorithm, and the number and volume of those lesion types were correlated with parameters of the disease. The combination of diffusion tensor imaging metrics (fractional anisotropy and radial diffusivity) and multi-compartment spherical mean technique values (microscopic fractional anisotropy and intra-neurite volume fraction) differentiated two type of lesions, with a prediction strength of 0.931. The B-type lesions had larger diffusion changes compared to the A-type lesions, irrespective of their location (P |
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| ISSN: | 2213-1582 2213-1582 |