approximate sparsity and nonlocal total variation based compressive mr image reconstruction

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ID: 225608
2014
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Abstract
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.
Reference Key
deng2014mathematicalapproximate Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors ;Chengzhi Deng;Shengqian Wang;Wei Tian;Zhaoming Wu;Saifeng Hu
Journal journal of power sources
Year 2014
DOI
10.1155/2014/137616
URL
http://dx.doi.org/10.1155/2014/137616
Keywords
civil engineering (general)mathematics engineering (general)

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