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Abstract #3646

Data driven algorithm for multicomponent T2 analysis based on identification of spatially global sub-voxel features

Noam Omer1, Neta Stern1, Tamar Blumenfeld-Katzir1, Meirav Galun2, and Noam Ben-Eliezer1,3,4,5,6
1Department of Biomedical Engineering, Tel-Aviv University, Tel-Aviv, Israel, 2Department of Computer Science and Applied Mathematics, Weitzman institute of science, Rehovot, Israel, 3Department of Orthopedics, Shamir Medical Center, Zerifin, Israel, 4Sackler Faculty of Medicine, Tel-Aviv University, Tel-Aviv, Israel, 5Sagol School of Neuroscience, Tel-Aviv University, Tel-Aviv, Israel, 6Center for Advanced Imaging Innovation and Research (CAI2R), New-York University Langone Medical Center, New York, NY, United States

Multicomponent T2 analysis (mcT2) yields a voxel-wise distribution of T2 values, which can be used to estimate sub-voxel information such as myelin content. Producing such data, however, remains challenging due to the large ambiguity in the T2 space. We present a data-driven approach for mcT2 analysis, which learns the anatomy in question and identifies microscopic tissue-specific features as a preprocessing step. It then utilizes them for analyzing each voxel locally using a designated optimization scheme. Experiments in human brain data show reproducible myelin content estimations at clinical settings without any prior assumptions.

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