Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
452-03-011 / 452-03-011 ISMRM Abstract

SNRDiff: SNR-Aware Diffusion Networks for Detail-Enhanced MRI Denoising

Primary: Analysis Methods - Image Enhancement
Secondary: Acquisition & Reconstruction - Image Reconstruction: AI
452-03-011 · Next-Generation MRI Image Enhancement · Tuesday, 12 May, 4:00 PM–5:36 PM · Power Pitch Theatre 2
452-03-011 · Next-Generation MRI Image Enhancement · Tuesday, 12 May, 4:00 PM–5:36 PM · Power Pitch Theatre 2
Keywords: Deep learning MRI Image Denoising Diffusion model
Accepted
Xinyu Chen1,2, Zhengyong Huang1,3, Ning Jiang1,3, Onur Afacan4,5, Ali Gholipour6, Simon K Warfield4,5, Yao Sui 1,3,7
1National Institute of Health Data Science, Peking University, Beijing, China
2School of Statistics, Beijing Normal University, Beijing, China
3Institute of Medical Technology, Peking University Health Science Center, Beijing, China
4Harvard Medical School, Boston, United States of America
5Department of Radiology, Boston Children's Hospital, Boston, United States of America
6School of Medicine, University of California, Irvine, United States of America
7Institute for Artificial Intelligence, Peking University, Beijing, China
Presenting Author: Yao Sui

Synopsis

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References

1. Tu, J., Shi, Y., & Lam, F. (2025). Score-based self-supervised mri denoising. arXiv preprint arXiv:2505.05631.
2. Froment, J. (2014). Parameter-free fast pixelwise non-local means denoising. Image Processing On Line, 4, 300-326.
3. Mäkinen, Y., Azzari, L., & Foi, A. (2020). Collaborative filtering of correlated noise: Exact transform-domain variance for improved shrinkage and patch matching. IEEE Transactions on Image Processing, 29, 8339-8354.
4. Krull, A., Buchholz, T. O., & Jug, F. (2019). Noise2void-learning denoising from single noisy images. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 2129-2137).
5. Zamir, S. W., Arora, A., Khan, S., Hayat, M., Khan, F. S., & Yang, M. H. (2022). Restormer: Efficient transformer for high-resolution image restoration. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 5728-5739).
6. Xiang, T., Yurt, M., Syed, A. B., Setsompop, K., & Chaudhari, A. (2023). DDM $^ 2$: Self-supervised diffusion MRI denoising with generative diffusion models. arXiv preprint arXiv:2302.03018.
7. Ho, J., Jain, A., & Abbeel, P. (2020). Denoising diffusion probabilistic models. Advances in neural information processing systems, 33, 6840-6851.
8. Jiang, H., Imran, M., Zhang, T., Zhou, Y., Liang, M., Gong, K., & Shao, W. (2025). Fast-DDPM: Fast denoising diffusion probabilistic models for medical image-to-image generation. IEEE Journal of Biomedical and Health Informatics.
9. Li, T., Feng, H., Wang, L., Zhu, L., Xiong, Z., & Huang, H. (2024). Stimulating diffusion model for image denoising via adaptive embedding and ensembling. IEEE Transactions on Pattern Analysis and Machine Intelligence.
10. Zaca, D., Hasson, U., Minati, L., & Jovicich, J. (2018). Method for retrospective estimation of natural head movement during structural MRI. Journal of Magnetic Resonance Imaging, 48(4), 927-937.
11. Lyu, M., Mei, L., Huang, S., Liu, S., Li, Y., Yang, K., ... & Wu, E. X. (2023). M4Raw: A multi-contrast, multi-repetition, multi-channel MRI k-space dataset for low-field MRI research. Scientific Data, 10(1), 264.

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http://echo.ismrm.org/p/ISMRM2026/452-03-011