SWISTA-Nets: Subband-adaptive wavelet iterative shrinkage thresholding networks for image reconstruction
文献类型：期刊论文
| 作者 | Lu, Binchun2; Fu, Lidan1,3; Pan, Yixuan2; Dong, Yonggui2 |
| 刊名 | COMPUTERIZED MEDICAL IMAGING AND GRAPHICS
 |
| 出版日期 | 2024-04-01 |
| 卷号 | 113页码:12 |
| 关键词 | Image reconstruction Inverse problem Deep learning Iterative shrinkage-thresholding algorithm Electromagnetic tomography Sparse-view CT |
| ISSN号 | 0895-6111 |
| DOI | 10.1016/j.compmedimag.2024.102345 |
| 通讯作者 | Dong, Yonggui(dongyg@mail.tsinghua.edu.cn) |
| 英文摘要 | Robust and interpretable image reconstruction is central to imageology applications in clinical practice. Prevalent deep networks, with strong learning ability to extract implicit information from data manifold, are still lack of prior knowledge introduced from mathematics or physics, leading to instability, poor structure interpretability and high computation cost. As to this issue, we propose two prior knowledge -driven networks to combine the good interpretability of mathematical methods and the powerful learnability of deep learning methods. Incorporating different kinds of prior knowledge, we propose subband-adaptive wavelet iterative shrinkage thresholding networks (SWISTA-Nets), where almost every network module is in one-to-one correspondence with each step involved in the iterative algorithm. By end -to -end training of proposed SWISTANets, implicit information can be extracted from training data and guide the tuning process of key parameters that possess mathematical definition. The inverse problems associated with two medical imaging modalities, i.e., electromagnetic tomography and X-ray computational tomography are applied to validate the proposed networks. Both visual and quantitative results indicate that the SWISTA-Nets outperform mathematical methods and state-of-the-art prior knowledge -driven networks, especially with fewer training parameters, interpretable network structures and well robustness. We assume that our analysis will support further investigation of prior knowledge -driven networks in the field of ill -posed image reconstruction. |
| WOS关键词 | INVERSE PROBLEMS ; NEURAL-NETWORK ; ALGORITHM ; DOMAIN ; CT |
| 资助项目 | National Natural Science Founda-tion of China[62071269] |
| WOS研究方向 | Engineering ; Radiology, Nuclear Medicine & Medical Imaging |
| 语种 | 英语 |
| WOS记录号 | WOS:001178669200001 |
| 出版者 | PERGAMON-ELSEVIER SCIENCE LTD |
| 资助机构 | National Natural Science Founda-tion of China |
| 源URL | [http://ir.ia.ac.cn/handle/173211/56969]  |
| 专题 | 自动化研究所_中国科学院分子影像重点实验室 |
| 通讯作者 | Dong, Yonggui |
| 作者单位 | 1.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China 2.Tsinghua Univ, Dept Precis Instrument, Beijing 100084, Peoples R China 3.Chinese Acad Sci, Inst Automat, CAS Key Lab Mol Imaging, Beijing Key Lab Mol Imaging, Beijing 100190, Peoples R China |
| 推荐引用方式 GB/T 7714 | Lu, Binchun,Fu, Lidan,Pan, Yixuan,et al. SWISTA-Nets: Subband-adaptive wavelet iterative shrinkage thresholding networks for image reconstruction[J]. COMPUTERIZED MEDICAL IMAGING AND GRAPHICS,2024,113:12. |
| APA | Lu, Binchun,Fu, Lidan,Pan, Yixuan,&Dong, Yonggui.(2024).SWISTA-Nets: Subband-adaptive wavelet iterative shrinkage thresholding networks for image reconstruction.COMPUTERIZED MEDICAL IMAGING AND GRAPHICS,113,12. |
| MLA | Lu, Binchun,et al."SWISTA-Nets: Subband-adaptive wavelet iterative shrinkage thresholding networks for image reconstruction".COMPUTERIZED MEDICAL IMAGING AND GRAPHICS 113(2024):12. |
入库方式： OAI收割
来源：自动化研究所
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