Keywords: Image Reconstruction, RelaxometryA nonconvex low rank regularization (NLR) was proposed to accelerate parameter mapping in the k-p domain. The NLR uses weighted nuclear norm minimization (WNNM) to obtain an optimized solution by differently penalizing singular values, in comparison to traditional low rank methods. The performance of the proposed algorithm was demonstrated for T2 mapping of the kidney. Our study demonstrated that the proposed algorithm outperformed k-p domain-based compressed sensing and L&S algorithms.
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