Objects: The diffusion-based spherical mean technique (SMT) provides a novel model to relate multi-b-value diffusion magnetic resonance imaging (MRI) data to features of tissue microstructure. We propose the first clinical application of SMT to image the brain of patients with multiple sclerosis (MS) and investigate clinical feasibility and translation.
Methods: Eighteen MS patients and nine age- and sex-matched healthy controls (HCs) underwent a 3.0 Tesla scan inclusive of clinical sequences and SMT images (isotropic resolution of 2 mm). Axial diffusivity (AD), apparent axonal volume fraction (Vax ), and effective neural diffusivity (Dax ) parametric maps were fitted. Differences in AD, Vax , and Dax between anatomically matched regions reflecting different tissues types were estimated using generalized linear mixed models for binary outcomes.
Results: Differences were seen in all SMT-derived parameters between chronic black holes (cBHs) and T2-lesions (P ≤ 0.0016), in Vax and AD between T2-lesions and normal appearing white matter (NAWM) (P < 0.0001), but not between the NAWM and normal WM in HCs. Inverse correlations were seen between Vax and AD in cBHs (r = -0.750, P = 0.02); in T2-lesions Dax values were associated with Vax (r = 0.824, P < 0.0001) and AD (r = 0.570, P = 0.014).
Interpretations: SMT-derived metrics are sensitive to pathological changes and hold potential for clinical application in MS patients.
© 2019 The Authors. Annals of Clinical and Translational Neurology published by Wiley Periodicals, Inc on behalf of American Neurological Association.