集成技术2026，Vol.15Issue(2)：76-90,15.DOI:10.12146/j.issn.2095-3135.20250102001
基于解剖结构边缘增强的高清扩散MRI微结构成像
High-Fidelity Diffusion MRI Microstructural Imaging Using Anatomical Structure-Guided Deep Learning with Edge Enhancement
摘要
Abstract
Diffusion magnetic resonance imaging is a crucial non-invasive technique for probing the microstructure of the human brain in vivo.Traditional hand-crafted and model-based tissue microstructure reconstruction methods typically require extensive diffusion gradient sampling,which is time-consuming and limits their clinical applicability.Recent advances in deep learning have shown great potential for microstructure estimation;however,accurately inferring tissue microstructure from clinically feasible diffusion magnetic resonance imaging scans remains challenging without appropriate constraints.In this paper,we propose a scalable and flexible framework compatible with diverse network architectures.By integrating macro-scale anatomical priors and cross-parameter mutual information from multiple diffusion models,together with total variation regularization,our approach achieves high-fidelity and efficient diffusion microstructure imaging.Experimental results demonstrate that the method significantly reduces scan time while maintaining—or even improving—the accuracy of microstructure estimation.关键词
扩散磁共振/微结构成像/深度学习/解剖结构/全变差正则Key words
diffusion magnetic resonance imaging/microstructure imaging/deep learning/anatomical structure/total variation regularization分类
信息技术与安全科学引用本文复制引用
马馨睿,程健,吴若有,肖韬辉,樊文欣,王珊珊..基于解剖结构边缘增强的高清扩散MRI微结构成像[J].集成技术,2026,15(2):76-90,15.基金项目
国家自然科学基金项目(U22A2040) （U22A2040）
深圳市医学研究专项资金项目(B2402047) （B2402047）
深圳市自然科学基金计划青年项目A类(QNXMA20250701100018023) （QNXMA20250701100018023）
广东省磁共振成像与多模系统重点实验室项目(2023B1212060052) （2023B1212060052）
中国科学院青年创新促进会优秀会员项目 This work is supported by National Natural Science Foundation of China(U22A2040),Shenzhen Medical Research Special Fund Project(B2402047),Shenzhen Science and Technology Program(QNXMA20250701100018023),Key Laboratory for Magnetic Resonance and Multimodality Imaging of Guangdong Province(2023B1212060052),and Youth Innovation Promotion Association of the Chinese Academy of Sciences （U22A2040）
