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dc.contributor.authorXiaobo Quzh_CN
dc.contributor.author屈小波zh_CN
dc.contributor.authorWeiru Zhangzh_CN
dc.contributor.author张苇如
dc.contributor.authorDi Guo
dc.contributor.author郭迪
dc.contributor.authorCongbo Cai
dc.contributor.author蔡聪波
dc.contributor.authorShuhui Cai
dc.contributor.author蔡淑惠
dc.contributor.authorZhong Chen
dc.contributor.author陈忠
dc.date.accessioned2011-04-26T08:23:19Z
dc.date.available2011-04-26T08:23:19Z
dc.date.issued2010-08-09zh_CN
dc.identifier.citationXiaobo Qu, Weiru Zhang, Di Guo, Congbo Cai, Shuhui Cai, Zhong Chen.Iterative thresholding compressed sensing MRI based on contourlet transform, Inverse Problems in Science and Engineering, 18(6):737-758, 2010.zh_CN
dc.identifier.urihttps://dspace.xmu.edu.cn/handle/2288/8277
dc.description.abstractReducing the acquisition time is important for clinical magnetic resonance imaging (MRI). Compressed sensing has recently emerged as a theoretical foundation for the reconstruction of magnetic resonance images from undersampled k-space measurements, assuming those images are sparse in a certain transform domain. However, most real-world signals are compressible rather than exactly sparse. For example, the commonly used two-dimensional wavelet for compressed sensing MRI (CS-MRI) does not sparsely represent curves and edges. In this article, we introduce a geometric image transform, the contourlet, to overcome this shortage. In addition, the improved redundancy provided by the contourlet can successfully suppress the pseudo-Gibbs phenomenon, a tiresome artefact produced by undersampling of k-space, around the singularities of images. For numerical calculation, a simple but effective iterative thresholding algorithm is employed to solve l1 norm optimization for CS-MRI. Considering the recovered information and image features, we introduce three objective criteria, which are the peak signal-to-noise ratio (PSNR), mutual information and transferred edge information, to evaluate the performance of different image transforms. Simulation results demonstrate that contourlet-based CS-MRI can better reconstruct the curves and edges than traditional wavelet-based methods, especially at low k-space sampling ratezh_CN
dc.description.sponsorshipThis work was partially supported by NNSF of China under Grants (10774125, 10875101, and 10605019). Xiaobo Qu and Di Guo would like to acknowledge the fellowship of Postgraduates Overseas Study Program for Building High-Level Universities from the Chinese Scholarship Council.zh_CN
dc.language.isoen_USzh_CN
dc.publisherInverse Problems in Science and Engineeringzh_CN
dc.subjectCompressed Sensing (压缩感知)zh_CN
dc.subjectMRI (磁共振成像)zh_CN
dc.subjectSparsifying Transforms (稀疏变换)zh_CN
dc.subjectContourlet (轮廓波)zh_CN
dc.subjectWavelet (小波变换)zh_CN
dc.titleIterative thresholding compressed sensing MRI based on contourlet transformzh_CN
dc.title.alternative基于Contourlet变换的压缩感知MRIzh_CN
dc.typeArticlezh_CN


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