DuDoDR-Net: Dual-domain data consistent recurrent network for simultaneous sparse view and metal artifact reduction in computed tomography

doi:10.1016/j.media.2021.102289

	DuDoDR-Net: Dual-domain data consistent recurrent network for simultaneous sparse view and metal artifact reduction in computed tomography
	Zhou, Bo 1; Chen, Xiongchao 1; Zhou, S. Kevin 4,5,6; Duncan, James S.1,2,3; Liu, Chi 1,2
	2022
发表期刊	MEDICAL IMAGE ANALYSIS
ISSN	1361-8415
卷号	75 页码:10
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.
关键词	Sparse view Metal artifact Computed tomography Recurrent network Dual-domain network Data consistency
DOI	10.1016/j.media.2021.102289
收录类别	SCI
语种	英语
资助项目	National Institutes of Health (NIH)[R01EB025468]
WOS研究方向	Computer Science ; Engineering ; Radiology, Nuclear Medicine & Medical Imaging
WOS类目	Computer Science, Artificial Intelligence ; Computer Science, Interdisciplinary Applications ; Engineering, Biomedical ; Radiology, Nuclear Medicine & Medical Imaging
WOS记录号	WOS:000718407400005
出版者	ELSEVIER
引用统计	被引频次：50[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/18108
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 Biomed Engn, New Haven, CT 06520 USA 2.Yale Sch Med, Dept Radiol & Biomed Imaging, New Haven, CT USA 3.Yale Univ, Dept Elect Engn, New Haven, CT USA 4.Univ Sci & Technol China, Sch Biomed Engn, Suzhou, Peoples R China 5.Univ Sci & Technol China, Suzhou Inst Adv Res, Suzhou, Peoples R China 6.Chinese Acad Sci, Inst Comp Technol, Beijing, Peoples R China
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.