mlsanityData to 7 Oct 2026

Self-Supervised Diffusion MRI Denoising via Iterative and Stable Refinement

arXiv 2501.13514 ↗First posted 23 Jan 2025eess.IV cs.CVcited by 6

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Abstract (arXiv)

Magnetic Resonance Imaging (MRI), including diffusion MRI (dMRI), serves as a ``microscope'' for anatomical structures and routinely mitigates the influence of low signal-to-noise ratio scans by compromising temporal or spatial resolution. However, these compromises fail to meet clinical demands for both efficiency and precision. Consequently, denoising is a vital preprocessing step, particularly for dMRI, where clean data is unavailable. In this paper, we introduce Di-Fusion, a fully self-supervised denoising method that leverages the latter diffusion steps and an adaptive sampling process. Unlike previous approaches, our single-stage framework achieves efficient and stable training without extra noise model training and offers adaptive and controllable results in the sampling process. Our thorough experiments on real and simulated data demonstrate that Di-Fusion achieves state-of-the-art performance in microstructure modeling, tractography tracking, and other downstream tasks. Code is available at https://github.com/FouierL/Di-Fusion.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 42 of its 81 references that are in the map. First posted on arXiv 23 Jan 2025.

Builds on

42 of 81 references.

References · 42

  1. 2020Denoising Diffusion Probabilistic Modelscited by 20,851
  2. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,160
  3. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
  4. 2020Denoising Diffusion Implicit Modelscited by 9,655
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30 more
  1. 2020WaveGrad: Estimating Gradients for Waveform Generationcited by 620
  2. 2018Noise2Noise: Learning Image Restoration without Clean Datacited by 526
  3. 2021Solving Inverse Problems in Medical Imaging with Score-Based Generative Modelscited by 514
  4. 2022Improving Diffusion Models for Inverse Problems using Manifold Constraintscited by 501
  5. 2018fastMRI: An Open Dataset and Benchmarks for Accelerated MRIcited by 464
  6. 2018Noise2Void - Learning Denoising from Single Noisy Imagescited by 359
  7. 2022Cold Diffusion: Inverting Arbitrary Image Transforms Without Noisecited by 275
  8. 2019Noise2Self: Blind Denoising by Self-Supervisioncited by 274
  9. 2023DiffIR: Efficient Diffusion Model for Image Restorationcited by 253
  10. 2022Restoring Vision in Adverse Weather Conditions with Patch-Based Denoising Diffusion Modelscited by 215
  11. 2023Generative Diffusion Prior for Unified Image Restoration and Enhancementcited by 198
  12. 2022Learning Fast Samplers for Diffusion Models by Differentiating Through Sample Qualitycited by 181
  13. 2021Neighbor2Neighbor: Self-Supervised Denoising from Single Noisy Imagescited by 171
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  15. 2019Noisier2Noise: Learning to Denoise from Unpaired Noisy Datacited by 125
  16. 2016Learning in Implicit Generative Modelscited by 124
  17. 2021Noise2Score: Tweedie's Approach to Self-Supervised Image Denoising without Clean Imagescited by 110
  18. 2022MR Image Denoising and Super-Resolution Using Regularized Reverse Diffusioncited by 66
  19. 2023Zero-Shot Noise2Noise: Efficient Image Denoising without any Datacited by 59
  20. 2020Noise2Same: Optimizing A Self-Supervised Bound for Image Denoisingcited by 56
  21. 2021Measuring Robustness in Deep Learning Based Compressive Sensingcited by 53
  22. 2023CoreDiff: Contextual Error-Modulated Generalized Diffusion Model for Low-Dose CT Denoising and Generalizationcited by 51
  23. 2023DDM2: Self-Supervised Diffusion MRI Denoising with Generative Diffusion Modelscited by 50
  24. 2020Patch2Self: Denoising Diffusion MRI with Self-Supervised Learningcited by 28
  25. 2021VORTEX: Physics-Driven Data Augmentations Using Consistency Training for Robust Accelerated MRI Reconstructioncited by 16
  26. 2024DiffusionBlend: Learning 3D Image Prior through Position-aware Diffusion Score Blending for 3D Computed Tomography Reconstructioncited by 13
  27. 2018Complex diffusion-weighted image estimation via matrix recovery under general noise modelscited by 8
  28. 2020Accelerating Prostate Diffusion Weighted MRI using Guided Denoising Convolutional Neural Network: Retrospective Feasibility Studycited by 5
  29. 2015Improving Fiber Alignment in HARDI by Combining Contextual PDE Flow with Constrained Spherical Deconvolutioncited by 2
  30. 2020SNR-enhanced diffusion MRI with structure-preserving low-rank denoising in reproducing kernel Hilbert spacescited by 2

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 3; 2026: 3.

6 papers.

  1. 2025MRI Super-Resolution with Deep Learning: A Comprehensive Surveycited by 7
  2. 2025Score-based Self-supervised MRI Denoisingcited by 6
  3. 2026Improving 2D Diffusion Models for 3D Medical Imaging with Inter-Slice Consistent Stochasticitycited by 3
  4. 2025Deep generative priors for 3D brain analysiscited by 0
  5. 2026An Attention-Based Denoising Model for Diffusion Weighted Imagingcited by 0
  6. 2026TensorLDM: A Component-Wise Latent Diffusion Model for Volumetric DTI Reconstruction from Sparse DWIscited by 0