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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
A novel data-driven approach for multicomponent analysis is introduced. This technique harnesses the statistical power of identifying global anatomical features prior to analyzing each voxel locally, offering reproducible estimation of myelin content in vivo.
Figure 2. Repeatability test of the new mcT2 algorithm on in vivo brain data. Parametric maps of white matter (WM) segments from 3 consecutive scans of the same subject using the suggested algorithm. (a-d) mask and myelin water fraction (MWF) maps of genu of corpus callosum (GCC). (e-h) mask and MWF maps of splenium of corpus callosum (GCC). (i-k) mask and MWF maps of the cortical WM segment (CSEG). MWF maps are presented with the same color scale and on top of a T2 map presented in gray scale.
Figure 1. Flowchart describing how to identify spatially global microscopic features for a given white matter (WM) segment. (a) A correlation-based probability score is computed between WM segment data and dictionary elements. (b-c) All scores are normalized and powered by β to prioritize the signals according to their probability to be found within the segment. (d) Summation of all signals scores to assign a global score for each dictionary element. (e) Selection of a L elements with the highest score.