Highly accelerated MRI via implicit neural representation guided posterior sampling of diffusion models

doi:10.1016/j.media.2024.103398

	Highly accelerated MRI via implicit neural representation guided posterior sampling of diffusion models
	Chu, Jiayue 1; Du, Chenhe2 ; Lin, Xiyue2 ; Zhang, Xiaoqun 3; Wang, Lihui 4; Zhang, Yuyao2 ; Wei, Hongjiang 1,5
	2025-02
发表期刊	MEDICAL IMAGE ANALYSIS (IF:10.7[JCR-2023],11.9[5-Year])
ISSN	1361-8415
EISSN	1361-8423
卷号	100
DOI	10.1016/j.media.2024.103398
摘要	Reconstructing high-fidelity magnetic resonance (MR) images from under-sampled k-space is a commonly used strategy to reduce scan time. The posterior sampling of diffusion models based on the real measurement data holds significant promise of improved reconstruction accuracy. However, traditional posterior sampling methods often lack effective data consistency guidance, leading to inaccurate and unstable reconstructions. Implicit neural representation (INR) has emerged as a powerful paradigm for solving inverse problems by modeling a signal's attributes as a continuous function of spatial coordinates. In this study, we present a novel posterior sampler for diffusion models using INR, named DiffINR. The INR-based component incorporates both the diffusion prior distribution and the MRI physical model to ensure high data fidelity. DiffINR demonstrates superior performance on in-distribution datasets with remarkable accuracy, even under high acceleration factors (up to R = 12 in single-channel reconstruction). Furthermore, DiffINR exhibits excellent generalizability across various tissue contrasts and anatomical structures with low uncertainty. Overall, DiffINR significantly improves MRI reconstruction in terms of accuracy, generalizability and stability, paving the way for further accelerating MRI acquisition. Notably, our proposed framework can be a generalizable framework to solve inverse problems in other medical imaging tasks. © 2024 Elsevier B.V.
关键词	Diffusion tensor imaging Magnetic resonance Diffusion model High-fidelity Implicit neural representation K-space Model-based OPC MRI acceleration Neural representations Posterior sampling Scan time Under sampled
URL	查看原文
收录类别	EI
语种	英语
出版者	Elsevier B.V.
EI入藏号	20244817441756
EI主题词	Dynamic contrast enhanced MRI
EI分类号	101.1 ; 701.2 Magnetism: Basic Concepts and Phenomena ; 746 Imaging Techniques
原始文献类型	Journal article (JA)
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/455164
专题	信息科学与技术学院信息科学与技术学院_硕士生信息科学与技术学院_博士生信息科学与技术学院_PI研究组_张玉瑶组
通讯作者	Wei, Hongjiang
作者单位	1.School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China; 2.School of Information Science and Technology, ShanghaiTech University, Shanghai, China; 3.Institute of Natural Sciences and School of Mathematical Sciences and MOE-LSC and SJTU-GenSci Joint Laboratory, Shanghai Jiao Tong University, Shanghai, China; 4.Key Laboratory of Intelligent Medical Image Analysis and Precise Diagnosis of Guizhou Province, School of Computer Science and Technology, Guizhou University, Guiyang, China; 5.National Engineering Research Center of Advanced Magnetic Resonance Technologies for Diagnosis and Therapy (NERC-AMRT), Shanghai Jiao Tong University, Shanghai, China
推荐引用方式 GB/T 7714	Chu, Jiayue,Du, Chenhe,Lin, Xiyue,et al. Highly accelerated MRI via implicit neural representation guided posterior sampling of diffusion models[J]. MEDICAL IMAGE ANALYSIS,2025,100.
APA	Chu, Jiayue.,Du, Chenhe.,Lin, Xiyue.,Zhang, Xiaoqun.,Wang, Lihui.,...&Wei, Hongjiang.(2025).Highly accelerated MRI via implicit neural representation guided posterior sampling of diffusion models.MEDICAL IMAGE ANALYSIS,100.
MLA	Chu, Jiayue,et al."Highly accelerated MRI via implicit neural representation guided posterior sampling of diffusion models".MEDICAL IMAGE ANALYSIS 100(2025).