波谱学杂志2026，Vol.43Issue(3)：367-388,22.DOI:10.11938/cjmr20263197
基于深度学习的扩散磁共振成像降噪方法研究进展
Research Progress on Diffusion Magnetic Resonance Imaging Noise Reduction Methods Based on Deep Learning
摘要
Abstract
Diffusion magnetic resonance imaging(dMRI)serves as an essential technique for imaging brain microstructure,exhibiting unique superiority in visualizing white matter fiber tracts.During the acquisition of diffusion-weighted images,multiple factors including signal attenuation,long echo time,and system noise lead to low signal-to-noise ratios(SNRs).This defect impairs the estimation of microstructure-related diffusion parameters,highlighting the importance of effective denoising for improving image quality and quantitative accuracy.This paper firstly introduces the fundamental imaging principles of dMRI and its noise statistical characteristics.Subsequently,it systematically reviews research progress in dMRI denoising methods,with a focus on deep learning-based approaches.The advantages and limitations of these methods are analyzed by evaluating their preservation of microstructural features during denoising.Common denoising evaluation metrics are summarized and compared with the performance of classical denoising methods.Finally,key challenges in current dMRI denoising research are summarized,and future directions are discussed.关键词
扩散加权成像/扩散张量成像/图像降噪/深度学习Key words
diffusion-weighted imaging(DWI)/diffusion tensor imaging(DTI)/image denoising/deep learning分类
数理科学引用本文复制引用
马素超,王远军..基于深度学习的扩散磁共振成像降噪方法研究进展[J].波谱学杂志,2026,43(3):367-388,22.基金项目
上海市自然科学基金资助项目(18ZR1426900). （18ZR1426900）
