LoCI-DiffCom: Longitudinal Consistency-Informed Diffusion Model for 3D Infant Brain Image Completion

doi:10.1007/978-3-031-72069-7_24

	LoCI-DiffCom: Longitudinal Consistency-Informed Diffusion Model for 3D Infant Brain Image Completion
	Zihao Zhu; Tianli Tao; Yitian Tao; Haowen Deng; Xinyi Cai; Gaofeng Wu; Kaidong Wang; Haifeng Tang; Lixuan Zhu; Zhuoyang Gu; Dinggang Shen; Han Zhang
	2024
会议录名称	MEDICAL IMAGE COMPUTING AND COMPUTER ASSISTED INTERVENTION - MICCAI 2024, PT II (IF:0.402[JCR-2005],0.000[5-Year])
ISSN	0302-9743
卷号	15002
发表状态	已发表
DOI	10.1007/978-3-031-72069-7_24
摘要	The infant brain undergoes rapid development in the first few years after birth. Compared to cross-sectional studies, longitudinal studies can depict the trajectories of infants' brain development with higher accuracy, statistical power and flexibility. However, the collection of infant longitudinal magnetic resonance (MR) data suffers a notorious dropout problem, resulting in incomplete datasets with missing time points. This limitation significantly impedes subsequent neuroscience and clinical modeling. Yet, existing deep generative models are facing difficulties in missing brain image completion, due to sparse data and the nonlinear, dramatic contrast/geometric variations in the developing brain. We propose LoCI-DiffCom, a novel Longitudinal Consistency-Informed Diffusion model for infant brain image Completion, which integrates the images from preceding and subsequent time points to guide a diffusion model for generating high-fidelity missing data. Our designed LoCI module can work on highly sparse sequences, relying solely on data from two temporal points. Despite wide separation and diversity between age time points, our approach can extract individualized developmental features while ensuring context-aware consistency. Our experiments on a large infant brain MR dataset demonstrate its effectiveness with consistent performance on missing infant brain MR completion even in big gap scenarios, aiding in better delineation of early developmental trajectories.
关键词	Medical image generation Infant brain development Diffusion model Magnetic resonance imaging (MRI)
会议名称	27th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI)
出版地	GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND
会议地点	Palmeraie Conf Ctr,Marrakesh,MOROCCO
会议日期	OCT 06-10, 2024
URL	查看原文
收录类别	CPCI-S
语种	英语
资助项目	STI 2030-Major Projects[2022ZD0209000] ; Shanghai Pilot Program for Basic Research - Chinese Academy of Science, Shanghai Branch[JCYJ-SHFY-2022-014] ; Key Program of the Xiamen Medical and Health[3502Z20234013]
WOS研究方向	Computer Science ; Neurosciences & Neurology ; Radiology, Nuclear Medicine & Medical Imaging
WOS类目	Computer Science, Artificial Intelligence ; Computer Science, Theory & Methods ; Neuroimaging ; Radiology, Nuclear Medicine & Medical Imaging
WOS记录号	WOS:001342225800024
出版者	SPRINGER INTERNATIONAL PUBLISHING AG
EISSN	1611-3349
文献类型	会议论文
条目标识符	https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/421416
专题	生物医学工程学院_硕士生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_沈定刚组生物医学工程学院_PI研究组_王乾组生物医学工程学院_PI研究组_张寒组生物医学工程学院_硕士生生物医学工程学院_硕士生生物医学工程学院_硕士生
共同第一作者	Tianli Tao; Yitian Tao
通讯作者	Han Zhang
作者单位	ShanghaiTech University
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	Zihao Zhu,Tianli Tao,Yitian Tao,et al. LoCI-DiffCom: Longitudinal Consistency-Informed Diffusion Model for 3D Infant Brain Image Completion[C]. GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND:SPRINGER INTERNATIONAL PUBLISHING AG,2024.