扩散磁共振成像（diffusion Magnetic Resonance Imaging, dMRI）是一种重要的脑微观结构成像技术，在脑白质纤维束组织成像中具有突出优势. 在扩散加权图像采集过程中，受信号衰减、长回波时间及系统噪声等因素影响，图像信噪比较低，进而对脑微结构扩散模型参数估计产生影响，因此，对信号的有效降噪可提升成像质量和定量分析的准确性. 本文首先介绍了dMRI的基本成像原理及其噪声统计特性；随后系统梳理了dMRI降噪方法的研究进展，并重点分析基于深度学习的降噪方法，结合降噪过程对微结构特性的保持来分析方法的优势和局限性；接着总结了常用的降噪评价指标并与经典降噪方法的性能特点进行了对比分析；最后总结了当前dMRI降噪研究所面临的关键挑战，展望了未来可能的发展方向. 

Diffusion magnetic resonance imaging (dMRI) is a crucial technique for imaging brain microstructure, offering distinct advantages in visualizing white matter fiber tracts. During diffusion-weighted image acquisition, factors such as signal attenuation, long echo times, and system noise contribute to low signal-to-noise ratios. This impacts the estimation of microstructure-related diffusion parameters, highlighting the importance of effective denoising for improving image quality and quantitative accuracy. This paper first introduces the fundamental imaging principles of dMRI and its noise statistical characteristics. Subsequently, it systematically reviews research progress in dMRI denoising methods, focusing 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 characteristics of classical denoising methods. Finally, key challenges in current dMRI denoising research are summarized, and future directions are explored.