Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
364-04-008 ISMRM Abstract

MRI Reconstruction using Diffusion with Iterative Colored Renoising (DDfire)

Primary: Acquisition & Reconstruction - Image Reconstruction: AI
Secondary: Acquisition & Reconstruction - AI methods
364-04-008 · Image Reconstruction Using Generative Models · Monday, 11 May, 2:45 PM–3:40 PM · Digital Posters Row E
Keywords: AI/ML Image Reconstruction Machine Learning/Artificial Intelligence AI-Accelerated MRI Generative diffusion model Generative AI
Accepted
Matthew C Bendel1, Philip Schniter1, Rizwan Ahmad2
1Electrical and Computer Engineering, The Ohio State University, Columbus, United States of America
2Department of Biomedical Engineering, The Ohio State University, Columbus, United States of America
Presenting Author: Syed Murtaza Arshad

Synopsis

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References

1. M. Bendel, R. Ahmad, and P. Schniter, “Solving inverse problems using diffusion with iterative colored renoising,” Trans. on Mach. Learn., Aug. 2025.
2. J. Song, C. Meng, and S. Ermon, “Denoising diffusion implicit models,” in Proc. Intl. Conf. Learn. Rep., 2021.
3. T. Karras, M. Aittala, T. Aila, and S. Laine, “Elucidating the design space of diffusion-based generative models,” in Proc. Neural Info. Process. Syst. Conf., vol. 35, pp. 26565–26577, 2022.
4. J. Zbontar, F. Knoll, A. Sriram, M. J. Muckley, M. Bruno, A. Defazio, M. Parente, K. J. Geras, J. Katsnelson, H. Chandarana, Z. Zhang, M. Drozdzal, A. Romero, M. Rabbat, P. Vincent, J. Pinkerton, D. Wang, N. Yakubova, E. Owens, C. L. Zitnick, M. P. Recht, D. K. Sodickson, and Y. W. Lui, “fastMRI: An open dataset and benchmarks for accelerated MRI,” arXiv:1811.08839, 2018.
5. R. Sanda, A. Aali, A. Johnston, E. Reis, J. Singh, G. Wetzstein, and S. Fridovich-Keil, “Padis-mri: Patch-based diffusion for data-efficient, radiologist-preferred mri reconstruction.” https://github.com/voilalab/PaDIS-MRI, 2025. Accessed: 2025-10-07.
6. R. Zhang, P. Isola, A. A. Efros, E. Shechtman, and O. Wang, “The unreasonable effectiveness of deep features as a perceptual metric,” in Proc. IEEE Conf. Comp. Vision Pattern Recog., pp. 586–595, 2018.
7. A. Sriram, J. Zbontar, T. Murrell, A. Defazio, C. L. Zitnick, N. Yakubova, F. Knoll, and P. Johnson, “End-to-end variational networks for accelerated MRI reconstruction,” in Proc. Intl. Conf. Med. Image Comput. Comput. Assist. Intervent., pp. 64–73, 2020.
8. M. Bendel, R. Ahmad, and P. Schniter, “A regularized conditional GAN for posterior sampling in inverse problems,” in Proc. Neural Info. Process. Syst. Conf., 2023.
9. M. Bendel, R. Ahmad, and P. Schniter, “pcaGAN: Improving posterior-sampling cGANs via principal component regularization,” in Proc. Neural Info. Process. Syst. Conf., 2024.
10. K. Ding, K. Ma, S. Wang, and E. P. Simoncelli, “Image quality assessment: Unifying structure and texture similarity,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 44, no. 5, pp. 2567–2581, 2020.
11. M. Heusel, H. Ramsauer, T. Unterthiner, B. Nessler, and S. Hochreiter, “GANs trained by a two time-scale update rule converge to a local Nash equilibrium,” in Proc. Neural Info. Process. Syst. Conf., vol. 30, 2017.
12. M. Soloveitchik, T. Diskin, E. Morin, and A. Wiesel, “Conditional Frechet inception distance,” arXiv:2103.11521, 2021.

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/364-04-008