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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