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DD-WGAN: Generative Adversarial Networks with Wasserstein Distance and Dual-Domain Discriminators for Low-Dose CT | |
2023-04-18 | |
会议录名称 | 2023 IEEE 20TH INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING (ISBI)
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ISSN | 1945-7928 |
卷号 | 2023-April |
发表状态 | 已发表 |
DOI | 10.1109/ISBI53787.2023.10230641 |
摘要 | X-ray computed tomography (CT) is a mainstream medical imaging modality. The widespread use of CT has made image denoising of low-dose CT (LDCT) images a key issue in medical imaging. Deep learning (DL) methods have been successful in this area over the past few years, but most DL-based dual-domain methods directly filter the sinogram domain data, which is prone to induce new artifacts in the reconstructed image. This paper proposes a new method called DD-WGAN, which has an image domain generator network (IDG-Net) and two discriminator networks, namely the image domain discriminator network (ID-Net) and the sinogram domain discriminator network (SD-Net). We use dual-domain discriminators to balance the data weights of sinogram and image. Experimental results show that the proposed method achieves significantly improved LDCT denoising performance. © 2023 IEEE. |
会议录编者/会议主办者 | Flywheel ; Kitware ; Siemens Healthineers ; UCLouvain |
关键词 | Low-dose CT Image denoising Deep learning Dual-domain |
会议名称 | 20th IEEE International Symposium on Biomedical Imaging, ISBI 2023 |
出版地 | 345 E 47TH ST, NEW YORK, NY 10017 USA |
会议地点 | Cartagena, Colombia |
会议日期 | 18-21 April 2023 |
URL | 查看原文 |
收录类别 | EI ; CPCI-S |
语种 | 英语 |
WOS研究方向 | Computer Science ; Engineering ; Radiology, Nuclear Medicine & Medical Imaging |
WOS类目 | Computer Science, Artificial Intelligence ; Engineering, Biomedical ; Radiology, Nuclear Medicine & Medical Imaging |
WOS记录号 | WOS:001062050500318 |
出版者 | IEEE Computer Society |
EI入藏号 | 20233914806089 |
EI主题词 | Image denoising |
EISSN | 1945-8452 |
EI分类号 | 461.1 Biomedical Engineering ; 461.4 Ergonomics and Human Factors Engineering ; 713.3 Modulators, Demodulators, Limiters, Discriminators, Mixers ; 716.1 Information Theory and Signal Processing ; 723.2 Data Processing and Image Processing ; 723.4 Artificial Intelligence ; 723.5 Computer Applications ; 746 Imaging Techniques |
原始文献类型 | Conference article (CA) |
来源库 | IEEE |
文献类型 | 会议论文 |
条目标识符 | https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/333437 |
专题 | 生物医学工程学院 物质科学与技术学院_博士生 信息科学与技术学院_硕士生 生物医学工程学院_PI研究组_曹国华组 |
通讯作者 | Cao, Guohua |
作者单位 | ShanghaiTech Univ, Sch Biomed Engn, Shanghai, Peoples R China |
第一作者单位 | 生物医学工程学院 |
通讯作者单位 | 生物医学工程学院 |
第一作者的第一单位 | 生物医学工程学院 |
推荐引用方式 GB/T 7714 | Bai, Xiao,Wang, Huamin,Yang, Shuo,et al. DD-WGAN: Generative Adversarial Networks with Wasserstein Distance and Dual-Domain Discriminators for Low-Dose CT[C]//Flywheel, Kitware, Siemens Healthineers, UCLouvain. 345 E 47TH ST, NEW YORK, NY 10017 USA:IEEE Computer Society,2023. |
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