A Slice‐Low‐Rank Plus Sparse (slice‐L + S) Reconstruction Method for k‐t Undersampled Multiband First‐Pass Myocardial Perfusion MRI
Sign Up to like & getrecommendations! Published in 2022 at "Magnetic Resonance in Medicine"
DOI: 10.1002/mrm.29281
Abstract: The synergistic use of k‐t undersampling and multiband (MB) imaging has the potential to provide extended slice coverage and high spatial resolution for first‐pass perfusion MRI. The low‐rank plus sparse (L + S) model has shown excellent… read more here.
Keywords: rank plus; low rank; first pass; slice ... See more keywords
Deep Low-rank plus Sparse Network for Dynamic MR Imaging
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DOI: 10.1016/j.media.2021.102190
Abstract: In dynamic magnetic resonance (MR) imaging, low-rank plus sparse (L+S) decomposition, or robust principal component analysis (PCA), has achieved stunning performance. However, the selection of the parameters of L+S is empirical, and the acceleration rate… read more here.
Keywords: plus sparse; network; low rank; rank plus ... See more keywords
Iteratively Reweighted Minimax-Concave Penalty Minimization for Accurate Low-rank Plus Sparse Matrix Decomposition.
Sign Up to like & getrecommendations! Published in 2021 at "IEEE transactions on pattern analysis and machine intelligence"
DOI: 10.1109/tpami.2021.3122259
Abstract: Low-rank plus sparse matrix decomposition (LSD) is an important problem in computer vision and machine learning. It has been solved using convex relaxations of the matrix rank and l0-pseudo-norm, which are the nuclear norm and… read more here.
Keywords: low rank; matrix decomposition; rank; plus sparse ... See more keywords