Approximate Sparsity and Nonlocal Total Variation Based Compressive MR Image Reconstruction

作者：Deng Chengzhi^*; Wang Shengqian; Tian Wei; Wu Zhaoming; Hu Saifeng

来源：Mathematical Problems in Engineering, 2014, 2014: 137616.

DOI：10.1155/2014/137616

Recent developments in compressive sensing (CS) show that it is possible to accurately reconstruct the magnetic resonance (MR) image from undersampled k-space data by solving nonsmooth convex optimization problems, which therefore significantly reduce the scanning time. In this paper, we propose a new MR image reconstruction method based on a compound regularization model associated with the nonlocal total variation (NLTV) and the wavelet approximate sparsity. Nonlocal total variation can restore periodic textures and local geometric information better than total variation. The wavelet approximate sparsity achieves more accurate sparse reconstruction than fixed wavelet l(0) and l(1) norm. Furthermore, a variable splitting and augmented Lagrangian algorithm is presented to solve the proposed minimization problem. Experimental results on MR image reconstruction demonstrate that the proposed method outperforms many existing MR image reconstruction methods both in quantitative and in visual quality assessment.

• 出版日期2014
• 单位南昌理工学院; 南昌工程学院