期刊
MAGNETIC RESONANCE IN MEDICINE
卷 73, 期 3, 页码 1125-1136出版社
WILEY
DOI: 10.1002/mrm.25240
关键词
compressed sensing; low-rank matrix completion; sparsity; dynamic MRI
资金
- National Institutes of Health [R01-EB000447]
- Direct For Computer & Info Scie & Enginr
- Division of Computing and Communication Foundations [0963835] Funding Source: National Science Foundation
PurposeTo apply the low-rank plus sparse (L+S) matrix decomposition model to reconstruct undersampled dynamic MRI as a superposition of background and dynamic components in various problems of clinical interest. Theory and MethodsThe L+S model is natural to represent dynamic MRI data. Incoherence between k-t space (acquisition) and the singular vectors of L and the sparse domain of S is required to reconstruct undersampled data. Incoherence between L and S is required for robust separation of background and dynamic components. Multicoil L+S reconstruction is formulated using a convex optimization approach, where the nuclear norm is used to enforce low rank in L and the l(1) norm is used to enforce sparsity in S. Feasibility of the L+S reconstruction was tested in several dynamic MRI experiments with true acceleration, including cardiac perfusion, cardiac cine, time-resolved angiography, and abdominal and breast perfusion using Cartesian and radial sampling. ResultsThe L+S model increased compressibility of dynamic MRI data and thus enabled high-acceleration factors. The inherent background separation improved background suppression performance compared to conventional data subtraction, which is sensitive to motion. ConclusionThe high acceleration and background separation enabled by L+S promises to enhance spatial and temporal resolution and to enable background suppression without the need of subtraction or modeling. Magn Reson Med 73:1125-1136, 2015. (c) 2014 Wiley Periodicals, Inc.
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