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Mathematical Problems in Engineering
Volume 2015, Article ID 615439, 18 pages
http://dx.doi.org/10.1155/2015/615439
Research Article

Compressed Sensing MRI Reconstruction from Highly Undersampled -Space Data Using Nonsubsampled Shearlet Transform Sparsity Prior

1School of Information Science & Engineering, Lanzhou University, Lanzhou 730000, China
2Department of Radiology, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200092, China

Received 25 September 2014; Revised 12 February 2015; Accepted 20 February 2015

Academic Editor: Alessandro Gasparetto

Copyright © 2015 Min Yuan et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Supplementary Material

The purpose of submitting supplementary material is only to show our work better to the readers. In fact, we have performed a large quantity of experiments on existing data, including real MR images and complex MR images. The experimental results for complex MR images have been demonstrated in the research article. To evaluate the performance of the proposed approach, we performed a large number of experiments on the real-value MR images, including Noncontrast MRA of the Circle of Willis, an Axial T2-weighted MR image of the human brain (from American Radiology Services, http://www3.americanradiology.com/pls/web1/wwimggal.vmg/) and a high resolution phantom. The setup and conditions of the experiment are similar to ones of the research article. The CS data acquisition was simulated by undersampling the 2D discrete Fourier transform of MR images. Sampling schemes used in the experiments include variable density Cartesian sampling, 2D variable density random sampling and pseudo radial sampling pattern. The corresponding reconstruction results have been shown in Figure 1~Figure 4 of supplementary material. The details of the examples can be seen in the legends

  1. Supplementary Material