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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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