期刊
MAGNETIC RESONANCE IMAGING
卷 31, 期 9, 页码 1611-1622出版社
ELSEVIER SCIENCE INC
DOI: 10.1016/j.mri.2013.07.010
关键词
Compressed sensing; Accelerated imaging; MRI; Sparse representation; Non-convex optimization; Directional wavelets
资金
- NNSF of China [61201045, 11174239, 10974164]
- Fundamental Research Funds for the Central Universities [2013SH002]
- Key Lab of Digital Signal and Image Processing of Guangdong Province [54600321]
- Scientific Research Foundation for the Introduction of Talent at Xiamen University of Technology [90030606]
Reducing scanning time is significantly important for MRI. Compressed sensing has shown promising results by undersampling the k-space data to speed up imaging. Sparsity of an image plays an important role in compressed sensing MM to reduce the image artifacts. Recently, the method of patch-based directional wavelets (PBDW) which trains geometric directions from undersampled data has been proposed. It has better performance in preserving image edges than conventional sparsifying transforms. However, obvious artifacts are presented in the smooth region when the data are highly undersampled. In addition, the original PBDW-based method does not hold obvious improvement for radial and fully 2D random sampling patterns. In this paper, the PBDW-based MM reconstruction is improved from two aspects: 1) An efficient non-convex minimization algorithm is modified to enhance image quality; 2) PBDW are extended into shift-invariant discrete wavelet domain to enhance the ability of transform on sparsifying piecewise smooth image features. Numerical simulation results on vivo magnetic resonance images demonstrate that the proposed method outperforms the original PBDW in terms of removing artifacts and preserving edges. (C) 2013 Elsevier Inc. All rights reserved.
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