Abstract #0740
Dynamic MRI Reconstruction using Low-Rank plus Sparse model with Optimal Rank Regularized Eigen-Shrinkage
Brian E. Moore 1 , Rajesh R. Nadakuditi 1 , and Jeffrey A. Fessler 1
1
Department of Electrical Engineering and
Computer Science, University of Michigan, Ann Arbor, MI,
United States
Low-rank plus sparse matrix decomposition algorithms
have seen fruitful application in dynamic
contrast-enhanced MRI because the data is well modeled
as the superposition of a low-rank static background and
temporally sparse dynamic contrast enhancement. In this
setting, we propose a novel algorithm that replaces the
standard nuclear norm with a recently discovered optimal
rank regularizer from random matrix theory. This
regularizer preserves the quality of high
signal-to-noise ratio image features while maintaining
data compressibility, resulting in better qualitative
and quantitative image reconstruction than existing
nuclear norm techniques. We substantiate these claims on
undersampled multicoil cardiac perfusion MRI data.
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