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Updated: Jan 11, 2026

Magnetic Resonance Imaging of Multiple Sclerosis at 7.0 Tesla
Published on: February 19, 2021
Correlations of myelin, axon, and inflammation metrics from multi echo T2 relaxation and multi-shell diffusion
Tigris S Joseph1, Hanwen Liu2, Shannon H Kolind3
1Department of Physics and Astronomy, University of British Columbia, 6224 Agricultural Road, Vancouver, BC, V6T 1Z1, Canada; International Collaboration on Repair Discoveries, University of British Columbia, 818 W 10th Ave, Vancouver, BC, V5Z 1N1, Canada.
Background:
Magnetic resonance imaging (MRI) metrics from multi-echo T2 relaxation measurements and diffusion imaging models are thought to reflect myelin, axon, and inflammation markers which may be useful for characterising tissue changes in multiple sclerosis (MS).
Objective:
To evaluate how relaxation, neurite orientation dispersion and density imaging (NODDI), and diffusion basis spectrum imaging (DBSI) metrics that quantify myelin, axon and inflammation microstructural changes are correlated.
Methods:
122 MS participants and 16 healthy controls underwent 48-echo gradient and spin echo, diffusion, 3DT1, proton-density and T2-weighted scans at 3T. Pairwise Spearman correlations were used to compare MRI metrics in white matter and MS lesions.
Results:
Most correlations were consistent with expected relationships. Unexpectedly, NODDI neurite density index only weakly correlated with DBSI fiber fraction (both axonal measures) in lesions. Geometric mean T2 of the intra/extracellular water pool may primarily increase due to edema/tissue loss, instead of an increase of inflammatory cells. Lesions and white matter showed different results for some metric pairs, with some correlations becoming stronger in lesions while others were weaker compared to the correlations in white matter. This could be due to debris in lesions impacting modeling assumptions, or lesions having a larger range of parameter values allowing stronger correlations.
Conclusions:
Diffusion and T2 relaxation metrics supposedly quantifying similar microstructural characteristics are uniquely influenced by pathology. Lesion pathology, such as inflammation and tissue debris, complicate modeling, and may decrease the specificity of MRI metrics.

