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Abstract #0429

Improving myelin water mapping using a new data-driven paradigm for multicomponent analysis of T2 relaxation times

Noam Omer1, Neta Stern1, Tamar Blumenfeld-Katzir1, Chen Solomon1, Meirav Galun2, and Noam Ben-Eliezer1,3,4
1Biomedical Engineering, Tel Aviv University, Tel Aviv, Israel, 2Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot, Israel, 3Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel, 4Center for Advanced Imaging Innovation and Research (CAI2R), New-York University Langone Medical Center, New York, NY, United States


The common approach to myelin mapping relies on multi-T2 component (mcT2) analysis, where the signal from a single voxel is separated into its underlying distribution of T2 values. This approach is highly challenging due to the ill posedness of extracting multiple free parameters from a single voxel data. We present a new data-driven paradigm, where the white matter is first analyzed to identify a finite set of multi-T2 distributions, which are then used to locally analyze the signal in each voxel. Application on white matter tissue produced improved myelin quantifications, without a priory fixing the number of sub-voxel compartments.

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