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Abstract #3701

Automatic Tissue Decomposition using Nonnegative Matrix Factorization for Noisy MR Magnitude Images

Daeun Kim 1 , Joong Hee Kim 2 , and Justin P. Haldar 1

1 Department of Electrical Engineering, University of Southern California, Los Angeles, CA, United States, 2 Department of Neurology, Washington University, St. Louis, MO, United States

This work proposes a novel data-driven method for automatically decomposing a multi-contrast MRI dataset into a mixture of constituent spatially-overlapping tissue components. The approach is non-parametric (no physical models are necessary), instead relying on a combination of low-rank matrix modeling, sparsity, and nonnegativity constraints through the nonnegative matrix factorization (NMF) framework. We demonstrate that NMF, when combined with an appropriate non-central chi noise model, can be used to automatically decompose diffusion and relaxation MRI datasets, yielding partial volume maps of white matter, gray matter, cerebrospinal fluid, and abnormal/injured tissue components.

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