Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
664-04-014 ISMRM Abstract

Diffusion-Style Noisy MRI Reconstruction via Stochastic MAP Estimation with an Implicit Denoiser Prior

Primary: Acquisition & Reconstruction - Image Reconstruction: AI
Secondary: Acquisition & Reconstruction - AI methods
664-04-014 · Image Reconstruction: Self-Supervised and Implicit · Thursday, 14 May, 2:35 PM–3:30 PM · Digital Posters Row E
Keywords: Deep learning reconstruction Regularization by denoising diffusion models Interpretable AI
Accepted
Nikola P Janjusevic 1,2, Amirhossein Khalilian-Gourtani3, Yao Wang4, Li Feng1,2
1The Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University Grossman School of Medicine, New York, United States of America
2Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, United States of America
3Department of Neuroscience, New York University Grossman School of Medicine, New York, United States of America
4Department of Electrical and Computer Engineering, New York University Tandon School of Engineering, New York, United States of America
Presenting Author: Nikola P Janjusevic

Synopsis

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References

1. G. Daras, H. Chung et al., “A survey on diffusion models for inverse problems,” arXiv preprint arXiv:2410.00083, 2024
2. Z. Kadkhodaie and E. Simoncelli, “Stochastic solutions for linear inverse problems using the prior implicit in a denoiser,” Advances in Neural Information Processing Systems, vol. 34, pp. 13 242–13 254, 2021.
3. N. Janjušević, A. Khalilian-Gourtani, Y. Wang, and Li Feng, "Learned Primal Dual Splitting for Self-Supervised Noise-Adaptive MRI Reconstruction," 2025 IEEE 22nd International Symposium on Biomedical Imaging (ISBI), Houston, TX, USA, 2025, pp. 1-4, doi: 10.1109/ISBI60581.2025.10980799. [doi]
4. J. Song, A. Vahdat, M. Mardani, J. Kautz, "Pseudoinverse-Guided Diffusion Models for Inverse Problems," in International Conference on Learning Representations (2023).
5. N. Janjušević, J. Chen, L. Ginocchio, M. Bruno, Y. Huang, Y. Wang, H. Chandarana, and Li Feng, Self-Supervised Noise Adaptive MRI Denoising via Repetition to Repetition (Rep2Rep) Learning, accepted to Magnetic Resonance in Medicine, 2025.
6. H. Chung, S. Lee, and J. C. Ye, ‘Decomposed Diffusion Sampler for Accelerating Large-Scale Inverse Problems’, in The Twelfth International Conference on Learning Representations, 2024.
7. Uecker Martin, Lai Peng, Murphy Mark J., et al. ESPIRiT—an eigenvalue approach to autocalibrating parallel MRI: Where SENSE meets GRAPPA. Magnetic Resonance in Medicine. 2013;71(3):990–1001.

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/664-04-014