Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
364-04-016 ISMRM Abstract

Patch-Based Diffusion Inverse Solver for T2-Weighted Prostate Imaging Reconstruction

Primary: Acquisition & Reconstruction - Image Reconstruction: AI
Secondary: Body - Prostate
364-04-016 · Image Reconstruction Using Generative Models · Monday, 11 May, 2:45 PM–3:40 PM · Digital Posters Row E
Keywords: AI/ML Image Reconstruction Prostate 0.55T Generative AI Diffusion models
Accepted
Hongze Yu 1, Jason Hu1, Hero K Hussain2, Michael J Jaroszewicz2, Vikas Gulani2, Jeffrey A Fessler1,2,3, Yun Jiang2,3
1Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, United States of America
2Department of Radiology, University of Michigan, Ann Arbor, United States of America
3Department of Biomedical Engineering, University of Michigan, Ann Arbor, United States of America
Presenting Author: Hongze Yu

Synopsis

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References

1. Y. Song and S. Ermon, "Generative Modeling by Estimating Gradients of the Data Distribution," in Advances in Neural Information Processing Systems, vol. 32, 2019.
2. J. Ho, A. Jain, and P. Abbeel, "Denoising Diffusion Probabilistic Models," in Advances in Neural Information Processing Systems, vol. 33, pp. 6840-6851, 2020.
3. H. Chung, J. Kim, M. T. Mccann, M. L. Klasky, and J. C. Ye, "Diffusion Posterior Sampling for General Noisy Inverse Problems," in Proceedings of the International Conference on Learning Representations, 2023.
4. T. Karras, M. Aittala, T. Aila, and S. Laine, "Elucidating the Design Space of Diffusion-Based Generative Models," in Proceedings of the Conference on Neural Information Processing Systems, 2022.
5. C. Cao et al., "High-Frequency Space Diffusion Model for Accelerated MRI," IEEE Transactions on Medical Imaging, vol. 43, no. 5, pp. 1853-1865, May 2024. https://doi.org/10.1109/TMI.2024.3351702 [doi]
6. R. Sanda, A. Aali, A. Johnston, E. Reis, J. Singh, G. Wetzstein, and S. Fridovich-Keil, "Patch-Based Diffusion for Data-Efficient, Radiologist-Preferred MRI Reconstruction," arXiv preprint arXiv:2509.21531, 2025.
7. J. Hu, B. Song, X. Xu, L. Shen, and J. A. Fessler, "Learning Image Priors Through Patch-Based Diffusion Models for Solving Inverse Problems," in Advances in Neural Information Processing Systems, vol. 37, 2024.
8. M. A. Griswold, P. M. Jakob, R. M. Heidemann, M. Nittka, V. Jellus, J. Wang, B. Kiefer, and A. Haase, "Generalized autocalibrating partially parallel acquisitions (GRAPPA)," Magnetic Resonance in Medicine, vol. 47, no. 6, pp. 1202-1210, 2002. https://doi.org/10.1002/mrm.10171 [doi]
9. M. Lustig, D. Donoho, and J. M. Pauly, "Sparse MRI: The application of compressed sensing for rapid MR imaging," Magnetic Resonance in Medicine, vol. 58, no. 6, pp. 1182-1195, 2007. https://doi.org/10.1002/mrm.21391 [doi]
10. R. Tibrewala, T. Dutt, A. Tong, et al., "FastMRI Prostate: A public, biparametric MRI dataset to advance machine learning for prostate cancer imaging," Scientific Data, vol. 11, p. 404, 2024. https://doi.org/10.1038/s41597-024-03252-w [doi]

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/364-04-016