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2015
IEEE

Patch-based nonlocal dynamic MRI reconstruction with low-rank prior

4 years 6 months ago
Patch-based nonlocal dynamic MRI reconstruction with low-rank prior
—Compressed sensing utilizes the sparsity of Magnetic resonance (MR) images to obtain accurate reconstructions from undersampled k-space data. In this paper, a novel nonlocal dynamic MRI reconstruction method with low-rank regularization is developed to exploit the spatiotemporal structural sparsity of a MRI sequence. The nonlocal prior and low rank prior are combined organically by grouping similar patches in both spatial and temporal domain. The low-rank regularization can be approximated by nuclear norm minimization solved by a singular value thresholding (SVT) method with adaptive thresholds estimation. The objective function is divided into several sub-problems that are easier to solve by alternative direction multiplier method (ADMM). Extensive experiments show that the new method outperforms commonly used classical dynamic MRI reconstruction algorithms.
Liyan Sun, Jinchu Chen, Xiao-Ping Zhang, Xinghao D
Added 14 Apr 2016
Updated 14 Apr 2016
Type Journal
Year 2015
Where MMSP
Authors Liyan Sun, Jinchu Chen, Xiao-Ping Zhang, Xinghao Ding
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