DuDoDR-Net: Dual-domain data consistent recurrent network for simultaneous sparse view and metal artifact reduction in computed tomography
文献类型：期刊论文
| 作者 | Zhou, Bo6; Chen, Xiongchao6; Zhou, S. Kevin2,3,4; Duncan, James S.1,5,6; Liu, Chi5,6 |
| 刊名 | MEDICAL IMAGE ANALYSIS
 |
| 出版日期 | 2022 |
| 卷号 | 75页码:10 |
| 关键词 | Sparse view Metal artifact Computed tomography Recurrent network Dual-domain network Data consistency |
| ISSN号 | 1361-8415 |
| DOI | 10.1016/j.media.2021.102289 |
| 英文摘要 | Sparse-view computed tomography (SVCT) aims to reconstruct a cross-sectional image using a reduced number of x-ray projections. While SVCT can efficiently reduce the radiation dose, the reconstruction suffers from severe streak artifacts, and the artifacts are further amplified with the presence of metallic implants, which could adversely impact the medical diagnosis and other downstream applications. Previous methods have extensively explored either SVCT reconstruction without metallic implants, or full-view CT metal artifact reduction (MAR). The issue of simultaneous sparse-view and metal artifact reduction (SVMAR) remains under-explored, and it is infeasible to directly apply previous SVCT and MAR methods to SVMAR which may yield non-ideal reconstruction quality. In this work, we propose a dual-domain data consistent recurrent network, called DuDoDR-Net, for SVMAR. Our DuDoDR-Net aims to reconstruct an artifact-free image by recurrent image domain and sinogram domain restorations. To ensure the metal free part of acquired projection data is preserved, we also develop the image data consistent layer (iDCL) and sinogram data consistent layer (sDCL) that are interleaved in our recurrent framework. Our experimental results demonstrate that our DuDoDR-Net is able to produce superior artifact-reduced results while preserving the anatomical structures, that outperforming previous SVCT and SVMAR methods, under different sparse-view acquisition settings. (c) 2021 Elsevier B.V. All rights reserved. |
| 资助项目 | National Institutes of Health (NIH)[R01EB025468] |
| WOS研究方向 | Computer Science ; Engineering ; Radiology, Nuclear Medicine & Medical Imaging |
| 语种 | 英语 |
| WOS记录号 | WOS:000718407400005 |
| 出版者 | ELSEVIER |
| 源URL | [http://119.78.100.204/handle/2XEOYT63/18107]  |
| 专题 | 中国科学院计算技术研究所期刊论文_英文 |
| 通讯作者 | Liu, Chi |
| 作者单位 | 1.Yale Univ, Dept Elect Engn, New Haven, CT USA 2.Chinese Acad Sci, Inst Comp Technol, Beijing, Peoples R China 3.Univ Sci & Technol China, Suzhou Inst Adv Res, Suzhou, Peoples R China 4.Univ Sci & Technol China, Sch Biomed Engn, Suzhou, Peoples R China 5.Yale Sch Med, Dept Radiol & Biomed Imaging, New Haven, CT USA 6.Yale Univ, Dept Biomed Engn, New Haven, CT 06520 USA |
| 推荐引用方式 GB/T 7714 | Zhou, Bo,Chen, Xiongchao,Zhou, S. Kevin,et al. DuDoDR-Net: Dual-domain data consistent recurrent network for simultaneous sparse view and metal artifact reduction in computed tomography[J]. MEDICAL IMAGE ANALYSIS,2022,75:10. |
| APA | Zhou, Bo,Chen, Xiongchao,Zhou, S. Kevin,Duncan, James S.,&Liu, Chi.(2022).DuDoDR-Net: Dual-domain data consistent recurrent network for simultaneous sparse view and metal artifact reduction in computed tomography.MEDICAL IMAGE ANALYSIS,75,10. |
| MLA | Zhou, Bo,et al."DuDoDR-Net: Dual-domain data consistent recurrent network for simultaneous sparse view and metal artifact reduction in computed tomography".MEDICAL IMAGE ANALYSIS 75(2022):10. |
入库方式： OAI收割
来源：计算技术研究所
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