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)
ISSN1945-7928
卷号2023-April
发表状态已发表
DOI10.1109/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
EISSN1945-8452
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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