Sparse Constrained Reconstruction for Parallel Magnetic Resonance Image Based on Variable Splitting Method
LIU Xiao-Fang1,2,YE Xiu-Zi1,3,ZHANG San-Yuan1,LI Xia2
1.College of Computer Science and Technology,Zhejiang University,Hangzhou 310027 2.Department of Computer Science and Technology,College of Information Engineering,China Jiliang University,Hangzhou 310018 3.College of Mathematics Information Science,Wenzhou University,Wenzhou 325035
Abstract In order to reduce the aliasing artifacts and noise in the reconstructed images due to under-sampling data,a sparse constrained image reconstruction algorithm is proposed for parallel magnetic resonance imaging. In this paper,first-order difference is viewed as the sparse project operator,and a parallel magnetic resonance image reconstruction algorithm restrained by anisotropic total variation minimization is researched. Meanwhile,a solution based on variable splitting method is proposed,and the effectiveness and robustness of the proposed algorithm are analyzing in some specified experimental environments. The results show that the quality of reconstructed images is evidently improved for parallel magnetic resonance imaging by the proposed method at a maximum acceleration factor.
LIU Xiao-Fang,YE Xiu-Zi,ZHANG San-Yuan等. Sparse Constrained Reconstruction for Parallel Magnetic Resonance Image Based on Variable Splitting Method[J]. , 2013, 26(1): 6-13.
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2013, Vol. 26 
