脑部扩散磁共振成像超分辨率重建研究进展
收稿日期: 2025-12-29
网络出版日期: 2026-04-10
基金资助
上海市自然科学基金资助项目(18ZR1426900)
Research Progress on Super-resolution Reconstruction of Brain Diffusion Magnetic Resonance Images
Received date: 2025-12-29
Online published: 2026-04-10
扩散磁共振成像（dMRI）被广泛用于研究脑白质微结构与纤维束走向，高角度、多壳层以及高空间分辨率的数据采集通常需要更长的扫描时间. 近年来，深度学习技术被广泛用于dMRI超分辨率重建，从稀疏采样条件下快速扫描采集的图像重建出高分辨率的成像信号，以便更精准地拟合脑微结构成像参数. 本文调研分析了深度学习技术在脑dMRI重建任务中的最新研究进展，按模型的重建指标不同，将重建方法划分为针对基础扩散指标重建、针对高阶微结构指标重建和针对纤维方向分布函数（fODF）重建三类，并详细展开三类方法的实现技术、评价指标及常用公开数据集，最后总结了dMRI超分辨率重建面临的主要挑战及研究动向.
谢心怡 , 王远军 . 脑部扩散磁共振成像超分辨率重建研究进展[J]. 波谱学杂志, 2026 , 43(2) : 223 -240 . DOI: 10.11938/cjmr20253196
Diffusion magnetic resonance imaging (dMRI) is extensively employed to investigate the microstructure and fiber tract orientation of white matter in the brain. However, high angular and multi-shell sampling with high spatial resolution usually requires a prolonged scan time. In recent years, deep learning techniques have been widely adopted for dMRI super-resolution reconstruction, which aims to reconstruct high-resolution imaging signals from rapidly acquired images under sparse sampling conditions, thereby enabling more accurate fitting of brain microstructure imaging parameters. This paper surveys and analyzes the latest research progress in deep learning-based reconstruction of brain dMRI. According to different reconstruction targets, the methods are classified into three categories: reconstruction of basic diffusion metrics, reconstruction of high-order microstructure metrics, and reconstruction of the fiber orientation distribution function (fODF). The implementation techniques, evaluation metrics, and commonly used public datasets for each category are discussed in detail. Finally, the main challenges and research trends in dMRI super-resolution reconstruction are summarized.
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