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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