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[2602.22235] Unsupervised Denoising of Diffusion-Weighted Images with Bias and Variance Corrected Noise Modeling
Summary
This article presents a novel approach for unsupervised denoising of diffusion-weighted images (dMRI) by addressing noise bias and variance through specialized loss functions, enhancing image quality for clinical diagnostics and neuroscience research.
Why It Matters
The study addresses critical challenges in dMRI, particularly the low signal-to-noise ratio that affects image quality. By introducing noise-corrected training objectives, this research enhances the reliability of dMRI analysis, which is essential for accurate clinical diagnostics and neuroscience studies. This advancement could lead to better patient outcomes and deeper insights into brain function.
Key Takeaways
- Introduces noise-corrected training objectives for dMRI denoising.
- Proposes two loss functions to address Rician noise bias and variance.
- Demonstrates improved image quality and diffusion metrics over existing methods.
- Highlights the importance of accounting for non-Gaussian noise in imaging.
- Offers a practical solution for enhancing dMRI analysis in low-SNR conditions.
Quantitative Biology > Quantitative Methods arXiv:2602.22235 (q-bio) [Submitted on 22 Feb 2026] Title:Unsupervised Denoising of Diffusion-Weighted Images with Bias and Variance Corrected Noise Modeling Authors:Jine Xie, Zhicheng Zhang, Yunwei Chen, Yanqiu Feng, Xinyuan Zhang View a PDF of the paper titled Unsupervised Denoising of Diffusion-Weighted Images with Bias and Variance Corrected Noise Modeling, by Jine Xie and 4 other authors View PDF HTML (experimental) Abstract:Diffusion magnetic resonance imaging (dMRI) plays a vital role in both clinical diagnostics and neuroscience research. However, its inherently low signal-to-noise ratio (SNR), especially under high diffusion weighting, significantly degrades image quality and impairs downstream analysis. Recent self-supervised and unsupervised denoising methods offer a practical solution by enhancing image quality without requiring clean references. However, most of these methods do not explicitly account for the non-Gaussian noise characteristics commonly present in dMRI magnitude data during the supervised learning process, potentially leading to systematic bias and heteroscedastic variance, particularly under low-SNR conditions. To overcome this limitation, we introduce noise-corrected training objectives that explicitly model Rician statistics. Specifically, we propose two alternative loss functions: one derived from the first-order moment to remove mean bias, and another from the second-order moment to correct squar...