3D Point-Based Multi-Modal Context Clusters GAN for Low-Dose PET Image Denoising

doi:10.1109/TCSVT.2024.3398686

	3D Point-Based Multi-Modal Context Clusters GAN for Low-Dose PET Image Denoising
	Cui, Jiaqi 1; Wang, Yan 1; Zhou, Luping 2; Fei, Yuchen 1; Zhou, Jiliu 1; Shen, Dinggang3,4
	2024-10-01
发表期刊	IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY (IF:8.3[JCR-2023],7.1[5-Year])
ISSN	1051-8215
EISSN	1558-2205
卷号	34 期号:10
发表状态	已发表
DOI	10.1109/TCSVT.2024.3398686
摘要	To obtain high-quality Positron emission tomography (PET) images while minimizing radiation hazards, various methods have been developed to acquire standard-dose PET (SPET) images from low-dose PET (LPET) images. Recent efforts mainly focus on improving the denoising quality by utilizing multi-modal inputs. However, these methods exhibit certain limitations. First, they neglect the varied significance of each modality in denoising. Second, they rely on inflexible voxel-based representations, failing to explicitly preserve intricate structures and contexts in images. To alleviate these problems, we propose a 3D Point-based Multi-modal Context Clusters GAN, namely PMC2-GAN, for obtaining high-quality SPET images from LPET and magnetic resonance imaging (MRI) images. Specifically, we transform the 3D image into unorganized points to flexibly and precisely express its complex structure. Moreover, a self-context clusters (Self-CC) block is devised to explore fine-grained contextual relationships of the image from the perspective of points. Additionally, considering the diverse importance of different modalities, we introduce a cross-context clusters (Cross-CC) block, which prioritizes PET as the primary modality while regarding MRI as the auxiliary one, to effectively integrate the knowledge from the two modalities. Overall, built on the smart integration of Self- and Cross-CC blocks, our PMC2-GAN follows GAN architecture. Extensive experiments validate our superiority.
关键词	Magnetic resonance imaging Positron emission tomography Noise reduction Three-dimensional displays Point cloud compression Image denoising Task analysis Positron emission topography (PET) low-dose PET denoising multi-modality point-based representation context clusters generative adversarial network (GAN)
URL	查看原文
收录类别	EI ; SCI
语种	英语
资助项目	National Natural Science Foundation of China (NSFC)["62371325","62071314"] ; Sichuan Science and Technology Program["2023YFG0263","2023YFG0025","2023NSFSC0497"]
WOS研究方向	Engineering
WOS类目	Engineering, Electrical & Electronic
WOS记录号	WOS:001346503100064
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
来源库	IEEE
引用统计	正在获取...
文献类型	期刊论文
条目标识符	https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/372872
专题	生物医学工程学院生物医学工程学院_PI研究组_沈定刚组
通讯作者	Wang, Yan
作者单位	1.Sichuan Univ, Sch Comp Sci, Chengdu 610065, Peoples R China 2.Univ Sydney, Sch Elect & Informat Engn, Sydney, NSW 2006, Australia 3.ShanghaiTech Univ, Sch Biomed Engn, Shanghai 201210, Peoples R China 4.Shanghai United Imaging Intelligence Co Ltd, Shanghai 201807, Peoples R China
推荐引用方式 GB/T 7714	Cui, Jiaqi,Wang, Yan,Zhou, Luping,et al. 3D Point-Based Multi-Modal Context Clusters GAN for Low-Dose PET Image Denoising[J]. IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY,2024,34(10).
APA	Cui, Jiaqi,Wang, Yan,Zhou, Luping,Fei, Yuchen,Zhou, Jiliu,&Shen, Dinggang.(2024).3D Point-Based Multi-Modal Context Clusters GAN for Low-Dose PET Image Denoising.IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY,34(10).
MLA	Cui, Jiaqi,et al."3D Point-Based Multi-Modal Context Clusters GAN for Low-Dose PET Image Denoising".IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 34.10(2024).