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 l0 and l1 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.

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compound regularization model fixed wavelet l0 and l1 norm undersampled kspace data reconstruct wavelet approximate sparsity nonlocal total variation visual quality compressive sensing quantitative nonsmooth convex optimization variable splitting and augmented lagrangian algorithm restore periodic textures and local geometric information mr image reconstruction methods

Chengzhi Deng,Wei Tian,Zhaoming Wu,Saifeng Hu,Shengqian Wang,.Approximate Sparsity and Nonlocal Total Variation Based Compressive MR Image Reconstruction. 2014 (),.

Chengzhi Deng,Wei Tian,Zhaoming Wu,Saifeng Hu,Shengqian Wang,"Approximate Sparsity and Nonlocal Total Variation Based Compressive MR Image Reconstruction" 2014

Chengzhi Deng,Wei Tian,Zhaoming Wu,Saifeng Hu,Shengqian Wang,"Approximate Sparsity and Nonlocal Total Variation Based Compressive MR Image Reconstruction"