Abstract #3335

Assessing myelin changes in lesional and non-lesional white matter in patients with MS using the T1w/FLAIR-ratio: a longitudinal study

Lis J.M. van den Boogaard^1,2,3, Gerhard S. Drenthen^1,3, Simone Monachino⁴, Stephanie Knippenberg², Svitlana Zinger⁴, Catarina D. Fernandes⁴, Marcel Breeuwer⁵, Shahab Jolani⁶, Jacobus F.A. Jansen^1,3,4, and Oliver H.H. Gerlach^2,3

¹Department of Radiology and Nuclear Medicine, Maastricht University Medical Center+, Maastricht, Netherlands, ²Academic MS Center, Department of Neurology, Zuyderland Medical Center, Sittard-Geleen, Netherlands, ³Mental Health and Neuroscience Research Institute, Maastricht University, Maastricht, Netherlands, ⁴Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands, ⁵Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands, ⁶Department of Methodology and Statistics, Maastricht University, Maastricht, Netherlands

Synopsis

Keywords: Multiple Sclerosis, Multiple Sclerosis, T1w/FLAIR-ratio, myelin, white matter, neurodegeneration, longitudinal

Motivation: Conventional MRI is not sensitive enough to detect subtle changes in myelin content of patients with relapsing-remitting multiple sclerosis (RRMS). T1w/FLAIR-ratio was introduced to improve contrast for myelin content.

Goal(s): Using T1w/FLAIR-ratio longitudinally to assess white matter (peri)lesion progression and changes in normal-appearing white matter (NAWM) in patients with RRMS, with correlations to expanded disability status scale (EDSS).

Approach: T1w- and FLAIR-sequences, and clinical data will be collected retrospectively of approximately 50 patients with RRMS. Linear mixed effects models will be used to assess (peri)lesional and NAWM changes of the brain for correlations between the T1w/FLAIR-ratio and clinical measures.

Impact: The T1w/FLAIR-ratio can potentially be a valuable metric to evaluate white matter changes in the myelin content. This study will investigate whether this myelin-proxy can provide estimates that can help predict progression of clinical symptoms in RRMS.

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