MoCo-Diff: Adaptive Conditional Prior on Diffusion Network for MRI Motion Correction

doi:10.1007/978-3-031-72089-5_39

	MoCo-Diff: Adaptive Conditional Prior on Diffusion Network for MRI Motion Correction
	Feng Li1 ; Zijian Zhou1 ; Yu Fang1 ; Jiangdong Cai1 ; Qian Wang1,2
	2024-10-03
会议录名称	INTERNATIONAL CONFERENCE ON MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION
ISSN	0302-9743
卷号	15006
发表状态	已发表
DOI	10.1007/978-3-031-72089-5_39
摘要	Magnetic Resonance Image (MRI) is a powerful medical imaging modality with non-ionizing radiation. However, due to its long scanning time, patient movement is prone to occur during acquisition. Severe motions can significantly degrade the image quality and make the images non-diagnostic. This paper introduces MoCo-Diff, a novel two-stage deep learning framework designed to correct the motion artifacts in 3D MRI volumes. In the first stage, we exploit a novel attention mechanism using shift window-based transformers in both the in-slice and through-slice directions to effectively remove the motion artifacts. In the second stage, the initially-corrected image serves as the prior for realistic MR image restoration. This stage incorporates the pre-trained Stable Diffusion to leverage its robust generative capability and the ControlUNet to fine-tune the diffusion model with the assistance of the prior. Moreover, we introduce an uncertainty predictor to assess the reliability of the motion-corrected images, which not only visually hints the motion correction errors but also enhances motion correction quality by trimming the prior with dynamic weights. Our experiments illustrate MoCo-Diff's superiority over state-of-the-art approaches in removing motion artifacts and retaining anatomical details across different levels of motion severity. The code is available at https://github.com/fengza/MoCo-Diff.
关键词	Motion correction Prior-conditioned diffusion model Dual branch transformer Magnetic resonance imaging
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[2021ZD0200514] ; National Natural Science Foundation of China[62131015]
WOS研究方向	Computer Science ; Engineering ; Radiology, Nuclear Medicine & Medical Imaging
WOS类目	Computer Science, Artificial Intelligence ; Computer Science, Theory & Methods ; Engineering, Biomedical ; Radiology, Nuclear Medicine & Medical Imaging
WOS记录号	WOS:001342231200039
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/449214
专题	生物医学工程学院_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_王乾组生物医学工程学院_PI研究组_胡鹏组
通讯作者	Qian Wang
作者单位	1.School of Biomedical Engineering & State Key Laboratory of Advanced Medical Materials and Devices, ShanghaiTech University, Shanghai, China 2.Shanghai Clinical Research and Trial Center, 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	Feng Li,Zijian Zhou,Yu Fang,et al. MoCo-Diff: Adaptive Conditional Prior on Diffusion Network for MRI Motion Correction[C]. GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND:SPRINGER INTERNATIONAL PUBLISHING AG,2024.