脑部扩散磁共振成像超分辨率重建研究进展OA

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.