Two-Layer Tight Frame Sparsifying Model for Compressed Sensing Magnetic Resonance Imaging.
文献类型：期刊论文
| 作者 | Wang, Shanshan; Liu, Jianbo; Peng, Xi; Dong, Pei; Liu, Qiegen; Liang, Dong |
| 刊名 | BIOMED RESEARCH INTERNATIONAL
 |
| 出版日期 | 2016 |
| 英文摘要 | Compressed sensing magnetic resonance imaging (CSMRI) employs image sparsity to reconstruct MR images from incoherently undersampled K-space data. Existing CSMRI approaches have exploited analysis transform, synthesis dictionary, and their variants to trigger image sparsity. Nevertheless, the accuracy, efficiency, or acceleration rate of existing CSMRI methods can still be improved due to either lack of adaptability, high complexity of the training, or insufficient sparsity promotion. To properly balance the three factors, this paper proposes a two-layer tight frame sparsifying (TRIMS) model for CSMRI by sparsifying the image with a product of a fixed tight frame and an adaptively learned tight frame. The two-layer sparsifying and adaptive learning nature of TRIMS has enabled accurate MR reconstruction from highly undersampled data with efficiency. To solve the reconstruction problem, a three-level Bregman numerical algorithm is developed. The proposed approach has been compared to three state-of-the-art methods over scanned physical phantom and in vivo MR datasets and encouraging performances have been achieved. |
| 收录类别 | SCI |
| 原文出处 | https://www.hindawi.com/journals/bmri/2016/2860643/abs/ |
| 语种 | 英语 |
| 源URL | [http://ir.siat.ac.cn:8080/handle/172644/10483]  |
| 专题 | 深圳先进技术研究院_医工所 |
| 作者单位 | BIOMED RESEARCH INTERNATIONAL |
| 推荐引用方式 GB/T 7714 | Wang, Shanshan,Liu, Jianbo,Peng, Xi,et al. Two-Layer Tight Frame Sparsifying Model for Compressed Sensing Magnetic Resonance Imaging.[J]. BIOMED RESEARCH INTERNATIONAL,2016. |
| APA | Wang, Shanshan,Liu, Jianbo,Peng, Xi,Dong, Pei,Liu, Qiegen,&Liang, Dong.(2016).Two-Layer Tight Frame Sparsifying Model for Compressed Sensing Magnetic Resonance Imaging..BIOMED RESEARCH INTERNATIONAL. |
| MLA | Wang, Shanshan,et al."Two-Layer Tight Frame Sparsifying Model for Compressed Sensing Magnetic Resonance Imaging.".BIOMED RESEARCH INTERNATIONAL (2016). |
入库方式： OAI收割
来源：深圳先进技术研究院
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