A FEATURE-GUIDED DUAL-DOMAIN NETWORK FOR CT METAL ARTIFACT REDUCTION

doi:10.1109/ISBI56570.2024.10635638

	A FEATURE-GUIDED DUAL-DOMAIN NETWORK FOR CT METAL ARTIFACT REDUCTION
	Huamin Wang1 ; Zhe Wang1 ; Shuo Yang1 ; Xiao Bai1 ; JiaYi Wu1 ; Yang Lv 2; Guohua Cao1
	2024
会议录名称	21ST IEEE INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING (ISBI)
ISSN	1945-7928
发表状态	已发表
DOI	10.1109/ISBI56570.2024.10635638
摘要	Metal artifact reduction (MAR) in CT is a classical problem. Recently, deep learning-based (DL) methods have achieved promising results for MAR tasks, especially those methods based on dual-domain networks. However, current dual-domain networks seldom employ the inherent features of metal artifact during MAR, leaving room for further improvements. Here, we propose a dual-domain network guided by inherent metal artifact characteristics for MAR. Our method utilizes a feature extractor to obtain several artifact priors. Then, those priors were employed to guide a prior network to generate an accurate prior sinogram, which can subsequently guide the following dual-domain network. Our experimental results demonstrate the promise of the artifact feature-guided dual-domain network for the MAR in CT.
会议录编者/会议主办者	AI2D Center ; et al. ; Therapanacea ; Thermo Fisher Scientific ; United Imaging Intelligence ; Verasonics
关键词	Computer vision Feature extraction 'current Classical problems CT Deep learning Dual domain Feature extractor Learning-based methods Metal artifact feature Metal artifact reduction Metal artifacts
会议名称	21st IEEE International Symposium on Biomedical Imaging, ISBI 2024
会议地点	Athens, Greece
会议日期	27-30 May 2024
URL	查看原文
收录类别	EI
语种	英语
出版者	IEEE Computer Society
EI入藏号	20243717024495
EI主题词	Deep learning
EISSN	1945-8452
EI分类号	1101.2 ; 1101.2.1 ; 1106.8
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/372803
专题	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	Yang Lv; Guohua Cao
作者单位	1.School of Biomedical Engineering & State Key Laboratory of Advanced Medical Materials and Devices, ShanghaiTech University, Shanghai, China 2.United Imaging Healthcare Co., Ltd., Shanghai, China
第一作者单位	上海科技大学
通讯作者单位	上海科技大学
第一作者的第一单位	上海科技大学
推荐引用方式 GB/T 7714	Huamin Wang,Zhe Wang,Shuo Yang,et al. A FEATURE-GUIDED DUAL-DOMAIN NETWORK FOR CT METAL ARTIFACT REDUCTION[C]//AI2D Center, et al., Therapanacea, Thermo Fisher Scientific, United Imaging Intelligence, Verasonics:IEEE Computer Society,2024.