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Abstract #0610

Compressive Diffusion MRI Part 1: Why Low-Rank?

Hao Gao1, 2, Longchuan Li3, Xiaoping P. Hu3

1Department of Mathematics and Computer Science, Emory University, Atlanta, GA, United States; 2Department of Radiology and Imaging Sciences, Emory University, Atlanta, GA, United States; 3Department of Biomedical Engineering, Emory University and Georgia Institute of Technology, Atlanta, GA, United States

This work compares several sparsity models for dynamic MRI with the focus on diffusion MRI. The low-rank model, a global sparsification of diffusion images via SVD, generally was found to be the best model, while the rank-sparsity decomposition was shown to be the best when the diffusion images are non-low-rank.