Abstract: 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.

Key words: diffusion-weighted imaging, diffusion tensor imaging, image denoising, deep learning