期刊
MEDICAL IMAGE ANALYSIS
卷 27, 期 -, 页码 93-104出版社
ELSEVIER
DOI: 10.1016/j.media.2015.05.012
关键词
Compressed sensing; Graph; Wavelet; MRI; Image reconstruction
类别
资金
- NNSF of China [61201045, 11375147, 61302174]
- Natural Science Foundation of Fujian Province of China [2015J01346]
- Fundamental Research Funds for the Central Universities [2013SH002]
- Open Fund from the Key Lab of Digital Signal and Image Processing of Guangdong Province [2013GDDSIPL-07]
Compressed sensing magnetic resonance imaging has shown great capacity for accelerating magnetic resonance imaging if an image can be sparsely represented. How the image is sparsified seriously affects its reconstruction quality. In the present study, a graph-based redundant wavelet transform is introduced to sparsely represent magnetic resonance images in iterative image reconstructions. With this transform, image patches is viewed as vertices and their differences as edges, and the shortest path on the graph minimizes the total difference of all image patches. Using the l(1) norm regularized formulation of the problem solved by an alternating-direction minimization with continuation algorithm, the experimental results demonstrate that the proposed method outperforms several state-of-the-art reconstruction methods in removing artifacts and achieves fewer reconstruction errors on the tested datasets. (C) 2015 Elsevier B.V. All rights reserved.
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