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Abstract #4315

Improved Nuisance Signal removal for 1H-MRSI Using a Low-Rank Plus Sparse Model with Learned Subspaces

Xinyu Ye1,2, Zepeng Wang2,3, and Fan Lam2,3
1Center for Biomedical Imaging Research, Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China, 2Department of Bioengineering, University of Illinois at Urbana-Champaign, Urbana, IL, United States, Urbana, IL, United States, 3Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL, United States


Removing nuisance signals is an essential step for MRSI. A union-of-subspaces model that uses spatiospectral priors has achieved excellent water/lipid removal performance for 1H-MRSI, but may not be sufficient when the initial water/lipids are too strong and/or when field inhomogeneity is severe. We propose a low-rank plus sparse method for improved nuisance removal. The sparsity term is used to capture residual nuisance failed to be captured by the union-of-subspace model and the low-rank term with learned subspaces protects metabolite signals. Results from in vivo 1H-MRSI data show that the proposed method led to improved nuisance signal removal.

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