ADN: Artifact Disentanglement Network for Unsupervised Metal Artifact Reduction
文献类型：期刊论文
| 作者 | Liao, Haofu1; Lin, Wei-An2; Zhou, S. Kevin3,4; Luo, Jiebo1 |
| 刊名 | IEEE TRANSACTIONS ON MEDICAL IMAGING
 |
| 出版日期 | 2020-03-01 |
| 卷号 | 39期号:3页码:634-643 |
| 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 |
| ISSN号 | 0278-0062 |
| DOI | 10.1109/TMI.2019.2933425 |
| 英文摘要 | 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 |
| 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 |
| 语种 | 英语 |
| WOS记录号 | WOS:000525262100009 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 源URL | [http://119.78.100.204/handle/2XEOYT63/14257]  |
| 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. |
入库方式： OAI收割
来源：计算技术研究所
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