Chinese Journal of Magnetic Resonance >
Research Progress on Diffusion Magnetic Resonance Imaging Noise Reduction Methods Based on Deep Learning
Received date: 2026-01-06
Online published: 2026-08-11
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.
MA Suchao , WANG Yuanjun . Research Progress on Diffusion Magnetic Resonance Imaging Noise Reduction Methods Based on Deep Learning[J]. Chinese Journal of Magnetic Resonance, 2026 , 43(3) : 367 -388 . DOI: 10.11938/cjmr20263197
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