Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
364-04-005 ISMRM Abstract

Consistency Model Priors for Fast and Accurate Generative MR Reconstruction

Accepted
Merve Gulle1,2, Junno Yun1,2, Yasar Utku Alcalar1,2, Mehmet Akcakaya 1,2
1Electrical & Computer Engineering, University of Minnesota, Minneapolis, United States of America
2Center for Magnetic Resonance Research (CMRR), University of Minnesota, Minneapolis, United States of America
Presenting Author: Mehmet Akcakaya

Synopsis

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References

1. Lustig M, Donoho D, Pauly JM. "Sparse MRI: The application of compressed sensing for rapid MR imaging". Magn Reson Med. 2007;58(6):1182–1195. doi:10.1002/mrm.21391 [doi]
2. Knoll F, Hammernik K, Zhang C, Moeller S, Pock T, Sodickson DK, Akcakaya M. "Deep-learning methods for parallel magnetic resonance imaging reconstruction: A survey of the current approaches, trends, and issues". IEEE Signal Processing Magazine. 2020;37(1):128-40. doi:10.1109/MSP.2019.2950640 [doi]
3. Hosseini SAH, Yaman B, Moeller S, Hong M, Akçakaya M. "Dense recurrent neural networks for accelerated MRI: History-cognizant unrolling of optimization algorithms". IEEE J Sel Top Signal Process. 2020;14(6):1280–1291. doi:10.1109/JSTSP.2020.3003170 [doi]
4. Aggarwal HK, Mani MP, Jacob M. "MoDL: Model-based deep learning architecture for inverse problems". IEEE Trans Med Imaging. 2018;38(2):394–405. doi:10.1109/TMI.2018.2865356 [doi]
5. Ahmad R, Bouman CA, Buzzard GT, Chan S, Liu S, Reehorst ET, Schniter P. "Plug-and-play methods for magnetic resonance imaging: Using denoisers for image recovery". IEEE Signal Processing Magazine. 2020;37(1):105-16. doi:10.1109/MSP.2019.2949470 [doi]
6. Yaman B, Hosseini SAH, Akçakaya M. "Zero-shot self-supervised learning for MRI reconstruction". In: Proceedings of the International Conference on Learning Representations (ICLR); 2022.
7. Dhariwal P, Nichol A. "Diffusion models beat GANs on image synthesis". In: Advances in Neural Information Processing Systems (NeurIPS); 2021;34:8780-94.
8. Ho J, Jain A, Abbeel P. "Denoising diffusion probabilistic models". In: Advances in Neural Information Processing Systems (NeurIPS); 2020;33:6840–6851.
9. Jalal A, Arvinte M, Daras G, Price E, Dimakis AG, Tamir J. "Robust compressed sensing MRI with deep generative priors". In: Advances in Neural Information Processing Systems (NeurIPS); 2021;34:14938-54.
10. Chung H, Lee S, Ye JC. "Decomposed diffusion sampler for accelerating large-scale inverse problems". In: Proceedings of the International Conference on Learning Representations (ICLR); 2024.
11. Webber G, Reader AJ. "Diffusion models for medical image reconstruction". BJR Artificial Intelligence. 2024;1(1):ubae013. doi:10.1093/bjrai/ubae013 [doi]
12. Daras G, Chung H, Lai CH, Mitsufuji Y, Ye JC, Milanfar P, Dimakis AG, Delbracio M. "A survey on diffusion models for inverse problems". arXiv preprint arXiv:2410.00083. 2024 Sep 30. arXiv preprint arXiv:2410.00083. 2024. doi:10.48550/arXiv.2410.00083 [doi]
13. Song J, Vahdat A, Mardani M, Kautz J. "Pseudoinverse-guided diffusion models for inverse problems". In: Proceedings of the International Conference on Learning Representations (ICLR); 2023.
14. Song Y, Dhariwal P, Chen M, Sutskever I. "Consistency models". In: Proceedings of the International Conference on Machine Learning (ICML); 2023.
15. Gülle M, Yun J, Alçalar YU, Akçakaya M. "Consistency Models as Plug-and-Play Priors for Inverse Problems". arXiv preprint arXiv:2509.22736. 2025. doi:10.48550/arXiv.2509.22736 [doi]
16. Zhao J, Song B, Shen L. "Cosign: Few-step guidance of consistency model to solve general inverse problems". In: Proceedings of the European Conference on Computer Vision (ECCV) 2024.
17. Garber T, Tirer T. "Zero-Shot Image Restoration Using Few-Step Guidance of Consistency Models (and Beyond)". In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2025, pp. 2398-2407.
18. Romano Y, Elad M, Milanfar P. "The little engine that could: Regularization by denoising (RED)". SIAM J Imaging Sci. 2017;10(4):1804–1844. doi:10.1137/16M1102884 [doi]
19. Reehorst ET, Schniter P. "Regularization by denoising: Clarifications and new interpretations". IEEE Trans Comput Imaging. 2018;5(1):52–67. doi:10.1109/TCI.2018.2880326 [doi]
20. Knoll F, Hammernik K, Zhang C, Moeller S, Pock T, Sodickson DK, et al. "fastMRI: A publicly available raw k-space and DICOM dataset of knee images for accelerated MR image reconstruction using machine learning". Radiol Artif Intell. 2020;2(1):e190007. doi:10.1148/ryai.2020190007 [doi]
21. Chung H, Kim J, McCann MT, Klasky ML, Ye JC. "Diffusion posterior sampling for general noisy inverse problems". In: Proceedings of the International Conference on Learning Representations (ICLR); 2023.
22. Atchadé YF, Fort G, Moulines E. "On perturbed proximal gradient algorithms". J Mach Learn Res. 2017;18(10):1–33.
23. H. Chung and J. C. Ye. "Score-based diffusion models for accelerated MRI". Medical Image Analysis, vol. 80, p. 102479, 2022. doi:10.1016/j.media.2022.102479 [doi]
24. Karras T, Aittala M, Aila T, Laine S. "Elucidating the design space of diffusion-based generative models". In: Advances in Neural Information Processing Systems (NeurIPS); 2022;35:26565–26577.

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