Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
364-04-006 ISMRM Abstract

Cross-Modal Dictionary Learning with Diffusion Refiner for Unpaired T1-Guided T2 MRI Reconstruction

Primary: Acquisition & Reconstruction - Image Reconstruction: AI
Secondary: Analysis Methods - Image Enhancement
364-04-006 · Image Reconstruction Using Generative Models · Monday, 11 May, 2:45 PM–3:40 PM · Digital Posters Row E
Keywords: AI/ML Image Reconstruction Deep-learning-based image reconstruction Unpaired reconstruction
Accepted
Jieyi Cai1, Zhengyong Huang1,2, Ning Jiang1,2, Xiaocheng Fang1, Yao Sui 1,2,3
1National Institute of Health Data Science, Peking University, Beijing, China
2Institute of Medical Technology, Peking University Health Science Center, Beijing, China
3Institute for Artificial Intelligence, Peking University, Beijing, China
Presenting Author: Yao Sui

Synopsis

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References

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3. Aggarwal, H. K., Mani, M. P., & Jacob, M. (2018). MoDL: Model-based deep learning architecture for inverse problems. IEEE transactions on medical imaging, 38(2), 394-405.
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5. Koshino, K., Werner, R. A., Pomper, M. G., Bundschuh, R. A., Toriumi, F., Higuchi, T., & Rowe, S. P. (2021). Narrative review of generative adversarial networks in medical and molecular imaging. Annals of Translational Medicine, 9(9), 821.
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7. Ravishankar, S., & Bresler, Y. (2010). MR image reconstruction from highly undersampled k-space data by dictionary learning. IEEE transactions on medical imaging, 30(5), 1028-1041.
8. Song, P., Weizman, L., Mota, J. F., Eldar, Y. C., & Rodrigues, M. R. (2019). Coupled dictionary learning for multi-contrast MRI reconstruction. IEEE transactions on medical imaging, 39(3), 621-633.
9. Lei, P., Fang, F., Zhang, G., & Xu, M. (2023). Deep unfolding convolutional dictionary model for multi-contrast MRI super-resolution and reconstruction. arXiv preprint arXiv:2309.01171.
10. Van Den Oord, A., & Vinyals, O. (2017). Neural discrete representation learning. Advances in neural information processing systems, 30.
11. Glasser, M. F., Sotiropoulos, S. N., Wilson, J. A., Coalson, T. S., Fischl, B., Andersson, J. L., ... & Wu-Minn HCP Consortium. (2013). The minimal preprocessing pipelines for the Human Connectome Project. Neuroimage, 80, 105-124.
12. Zhu, J. Y., Park, T., Isola, P., & Efros, A. A. (2017). Unpaired image-to-image translation using cycle-consistent adversarial networks. In Proceedings of the IEEE international conference on computer vision (pp. 2223-2232).

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http://echo.ismrm.org/p/ISMRM2026/364-04-006