Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
364-04-009 ISMRM Abstract

Dynamic MRI Reconstruction via Spatial RED-Diffusion Priors and Temporal Optical-Flow Regularization

Primary: Acquisition & Reconstruction - Image Reconstruction: AI
Secondary: Acquisition & Reconstruction - Subsampling: Parallel imaging, compressed sensing and low-rank modeling
364-04-009 · Image Reconstruction Using Generative Models · Monday, 11 May, 2:45 PM–3:40 PM · Digital Posters Row E
Keywords: Implicit neural representation Dynamic MRI Reconstruction Diffusion model Optical flow Regularization
Accepted
Qijun Chen1, Baoqing Li, Yihang Zhou 2,3,4,5, Zhuoxu Cui2,4,5, Luying Gui
1School of Mathematics and Statistics, Nanjing University of Science and Technology, NanJing, China
2Research Center for Medical AI, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
3Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
4University of Chinese Academy of Sciences, Beijing, China
5State Key Laboratory of Biomedical Imaging Science and System, Shenzhen, China
Presenting Author: Yihang Zhou

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References

1. Molaei, A., Aminimehr, A., Tavakoli, A., Kazerouni, A., Azad, B., Azad, R., & Merhof, D. (2023). Implicit neural representation in medical imaging: A comparative survey. In Proceedings of the IEEE/CVF International Conference on Computer Vision (pp. 2381-2391). https://doi.org/10.1109/iccvw60793.2023.00252 [doi]
2. Baik, D., & Yoo, J. (2025, September). Dynamic-aware spatio-temporal representation learning for dynamic MRI reconstruction. In International Conference on Medical Image Computing and Computer-Assisted Intervention (pp. 174-184). https://doi.org/10.1007/978-3-032-04965-0_17 [doi]
3. Feng, J., Feng, R., Wu, Q., Shen, X., Chen, L., Li, X., ... & Wei, H. (2025). Spatiotemporal implicit neural representation for unsupervised dynamic MRI reconstruction. IEEE Transactions on Medical Imaging. https://doi.org/10.1109/tmi.2025.3526452 [doi]
4. Catalán, T., Courdurier, M., Osses, A., Fotaki, A., Botnar, R., Sahli-Costabal, F., & Prieto, C. (2025). Unsupervised reconstruction of accelerated cardiac cine MRI using neural fields. Computers in Biology and Medicine, 185, 109467. https://doi.org/10.1016/j.compbiomed.2024.109467 [doi]
5. Huang, Q., Yang, D., Qu, H., Yi, J., Wu, P., & Metaxas, D. (2019, May). Dynamic MRI reconstruction with motion-guided network. In International Conference on Medical Imaging with Deep Learning (pp. 275-284). PMLR.
6. Schmoderer, T., Aviles-Rivero, A. I., Corona, V., Debroux, N., & Schönlieb, C. B. (2021). Learning optical flow for fast MRI reconstruction. Inverse Problems, 37(9), 095007. https://doi.org/10.1088/1361-6420/ac164a [doi]
7. Zhao, N., O’Connor, D., Basarab, A., Ruan, D., & Sheng, K. (2019). Motion compensated dynamic MRI reconstruction with local affine optical flow estimation. IEEE Transactions on Biomedical Engineering, 66(11), 3050-3059. https://doi.org/10.1109/tbme.2019.2900037 [doi]
8. Webber, G., & Reader, A. J. (2024). Diffusion models for medical image reconstruction. BJR| Artificial Intelligence, 1(1), ubae013. https://doi.org/10.1093/bjrai/ubae013 [doi]
9. Arefeen, Y., Levac, B., Stoebner, Z., & Tamir, J. I. (2024, October). INFusion: diffusion regularized implicit neural representations for 2D and 3D accelerated MRI reconstruction. In 2024 58th Asilomar Conference on Signals, Systems, and Computers (pp. 1886-1890). IEEE. https://doi.org/10.1093/bjrai/ubae013 [doi]
10. Shan, S., Zhu, M., Lin, Y., & Lu, L. (2025). RED-DiffEq: Regularization by denoising diffusion models for solving inverse PDE problems with application to full waveform inversion. arXiv preprint arXiv:2509.21659.

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/364-04-009