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.

• Type: Journal
• Year: 2015
• Where: MMSP
• Authors: Liyan Sun, Jinchu Chen, Xiao-Ping Zhang, Xinghao Ding
• Keywords: MMSP 2015, Multimedia