ADN: Artifact Disentanglement Network for Unsupervised Metal Artifact Reduction

doi:10.1109/TMI.2019.2933425

	ADN: Artifact Disentanglement Network for Unsupervised Metal Artifact Reduction
	Liao, Haofu 1; Lin, Wei-An 2; Zhou, S. Kevin 3,4; Luo, Jiebo 1
	2020-03-01
发表期刊	IEEE TRANSACTIONS ON MEDICAL IMAGING
ISSN	0278-0062
卷号	39 期号:3 页码:634-643
摘要	Current deep neural network based approaches to computed tomography (CT) metal artifact reduction (MAR) are supervised methods that rely on synthesized metal artifacts for training. However, as synthesized data may not accurately simulate the underlying physical mechanisms of CT imaging, the supervised methods often generalize poorly to clinical applications. To address this problem, we propose, to the best of our knowledge, the first unsupervised learning approach to MAR. Specifically, we introduce a novel artifact disentanglement network that disentangles the metal artifacts from CT images in the latent space. It supports different forms of generations (artifact reduction, artifact transfer, and self-reconstruction, etc.) with specialized loss functions to obviate the need for supervision with synthesized data. Extensive experiments show that when applied to a synthesized dataset, our method addresses metal artifacts significantly better than the existing unsupervised models designed for natural image-to-image translation problems, and achieves comparable performance to existing supervised models for MAR. When applied to clinical datasets, our method demonstrates better generalization ability over the supervised models. The source code of this paper is publicly available at https:// github.com/liaohaofu/adn.
关键词	Metals Computed tomography Decoding Mars X-ray imaging Image reconstruction Training Image enhancement restoration (noise and artifact reduction) neural network X-ray imaging computed tomography
DOI	10.1109/TMI.2019.2933425
收录类别	SCI
语种	英语
资助项目	NSF[1722847] ; Morris K. Udall Center of Excellence in Parkinson's Disease Research by NIH
WOS研究方向	Computer Science ; Engineering ; Imaging Science & Photographic Technology ; Radiology, Nuclear Medicine & Medical Imaging
WOS类目	Computer Science, Interdisciplinary Applications ; Engineering, Biomedical ; Engineering, Electrical & Electronic ; Imaging Science & Photographic Technology ; Radiology, Nuclear Medicine & Medical Imaging
WOS记录号	WOS:000525262100009
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计	被引频次：111[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/2XEOYT63/14258
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Liao, Haofu
作者单位	1.Univ Rochester, Dept Comp Sci, Rochester, NY 14627 USA 2.Univ Maryland Coll Pk, Dept Elect & Comp Engn, College Pk, MD 20742 USA 3.Chinese Acad Sci, Inst Comp Technol, Beijing 100190, Peoples R China 4.Peng Cheng Lab, Shenzhen, Peoples R China
推荐引用方式 GB/T 7714	Liao, Haofu,Lin, Wei-An,Zhou, S. Kevin,et al. ADN: Artifact Disentanglement Network for Unsupervised Metal Artifact Reduction[J]. IEEE TRANSACTIONS ON MEDICAL IMAGING,2020,39(3):634-643.
APA	Liao, Haofu,Lin, Wei-An,Zhou, S. Kevin,&Luo, Jiebo.(2020).ADN: Artifact Disentanglement Network for Unsupervised Metal Artifact Reduction.IEEE TRANSACTIONS ON MEDICAL IMAGING,39(3),634-643.
MLA	Liao, Haofu,et al."ADN: Artifact Disentanglement Network for Unsupervised Metal Artifact Reduction".IEEE TRANSACTIONS ON MEDICAL IMAGING 39.3(2020):634-643.