Iterative feature refinement for accurate undersampled MR image reconstruction | |
Wang, Shanshan; Liu, Jianbo; Liu, Qiegen; Ying, Leslie; Liu, Xin; Zheng, Hairong; Liang, Dong | |
刊名 | PHYSICS IN MEDICINE AND BIOLOGY
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2016 | |
英文摘要 | Accelerating MR scan is of great significance for clinical, research and advanced applications, and one main effort to achieve this is the utilization of compressed sensing (CS) theory. Nevertheless, the existing CSMRI approaches still have limitations such as fine structure loss or high computational complexity. This paper proposes a novel iterative feature refinement (IFR) module for accurate MR image reconstruction fromundersampled K-space data. Integrating IFR with CSMRI which is equipped with fixed transforms, we develop an IFR-CS method to restore meaningful structures and details that are originally discarded without introducing too much additional complexity. Specifically, the proposed IFR-CS is realized with three iterativesteps, namely sparsity-promoting denoising, feature refinement and Tikhonov regularization. Experimental results on both simulated and in vivo MR datasets have shown that the proposed module has a strong capability to capture image details, and that IFR-CS is comparable and even superior to other state-of-the-art reconstruction approaches. |
收录类别 | SCI |
原文出处 | http://iopscience.iop.org/article/10.1088/0031-9155/61/9/3291/meta |
语种 | 英语 |
内容类型 | 期刊论文 |
源URL | [http://ir.siat.ac.cn:8080/handle/172644/10454] ![]() |
专题 | 深圳先进技术研究院_医工所 |
作者单位 | PHYSICS IN MEDICINE AND BIOLOGY |
推荐引用方式 GB/T 7714 | Wang, Shanshan,Liu, Jianbo,Liu, Qiegen,et al. Iterative feature refinement for accurate undersampled MR image reconstruction[J]. PHYSICS IN MEDICINE AND BIOLOGY,2016. |
APA | Wang, Shanshan.,Liu, Jianbo.,Liu, Qiegen.,Ying, Leslie.,Liu, Xin.,...&Liang, Dong.(2016).Iterative feature refinement for accurate undersampled MR image reconstruction.PHYSICS IN MEDICINE AND BIOLOGY. |
MLA | Wang, Shanshan,et al."Iterative feature refinement for accurate undersampled MR image reconstruction".PHYSICS IN MEDICINE AND BIOLOGY (2016). |
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