Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
570-03-188 ISMRM Abstract

UniCMR: Enhancing Reconstruction Generalization for Cross-Center Cardiac MR via A Cascaded Network and DFU Module

Primary: Acquisition & Reconstruction - Image Reconstruction: AI
Secondary: Acquisition & Reconstruction - AI methods
570-03-188 · AI Methods · Wednesday, 13 May, 9:15 AM–10:10 AM · Traditional Posters | Exhibition Hall
Keywords: Deep learning Domain adaptation MRI Reconstruction Un- rolled Model
Accepted
Yipin Deng1, Yuyang Li1, Zijian Zhou 1, Yining Wang2, Yuan Feng3,4,5, Peng Hu1,6,7
1School of Biomedical Engineering, ShanghaiTech University, Shanghai, China
2Department of Radiology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College. Address: No.1, Shuaifuyuan, Dongcheng District, Beijing 100730, China
3School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
4Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai 200240, China
5National Engineering Research Center of Advanced Magnetic Resonance Technologies for Diagnosis and Therapy (NERC-AMRT), Shanghai Jiao Tong University, Shanghai 200240, China
6Shanghai Clinical Research and Trial Center, ShanghaiTech University, shanghai, China
7State Key Laboratory of Advanced Medical Materials and Devices, ShanghaiTech University, Shanghai, China
Presenting Author: Zijian Zhou

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References

1. Guo P, Wang P, Zhou J, Jiang S, Patel VM. Multi-institutional collaborations for improving deep learning-based magnetic resonance image reconstruction using federated learning. InProceedings of the IEEE/CVF conference on computer vision and pattern recognition 2021 (pp. 2423-2432). PMID: 35444379 [pmid]
2. Kushol R, Wilman AH, Kalra S, Yang YH. DSMRI: domain shift analyzer for multi-center MRI datasets. Diagnostics. 2023 Sep 14;13(18):2947. https://doi.org/10.3390/diagnostics13182947 [doi]
3. Huang Y, Khodabakhshi Z, Gomaa A, Schmidt M, Fietkau R, Guckenberger M, Andratschke N, Bert C, Tanadini-Lang S, Putz F. Multicenter privacy-preserving model training for deep learning brain metastases autosegmentation. Radiotherapy and Oncology. 2024 Sep 1;198:110419. https://doi.org/10.1016/j.radonc.2024.110419 [doi]
4. Kuang S, Woodruff HC, Granzier R, van Nijnatten TJ, Lobbes MB, Smidt ML, Lambin P, Mehrkanoon S. MSCDA: Multi-level semantic-guided contrast improves unsupervised domain adaptation for breast MRI segmentation in small datasets. Neural Networks. 2023 Aug 1;165:119-34. https://doi.org/10.1016/j.neunet.2023.05.014 [doi]
5. Zhang Z, Chen Y, Yu H, Wang Z, Wang S, Fan F, Shan H, Zhang Y. Uniada: Domain unifying and adapting network for generalizable medical image segmentation. IEEE Transactions on Medical Imaging. 2024 Dec 26. doi: 10.1109/TMI.2024.3523319 [doi]
6. Sriram A, Zbontar J, Murrell T, Defazio A, Zitnick CL, Yakubova N, Knoll F, Johnson P. End-to-end variational networks for accelerated MRI reconstruction. InInternational conference on medical image computing and computer-assisted intervention 2020 Sep 29 (pp. 64-73). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-030-59713-9_7 [doi]
7. Li X, Dai Y, Ge Y, Liu J, Shan Y, Duan LY. Uncertainty modeling for out-of-distribution generalization. arXiv preprint arXiv:2202.03958. 2022 Feb 8. https://doi.org/10.48550/arXiv.2202.03958 [doi]
8. D. Xu. CMRxRecon2025. 2025. doi: 10.21227/b6xs-gv29. url: https://dx.doi.org/10.21227/b6xs-gv29. [doi]
9. Uecker M, Ong F, Tamir JI, Bahri D, Virtue P, Cheng JY, Zhang T, Lustig M. Berkeley advanced reconstruction toolbox. InProc. Intl. Soc. Mag. Reson. Med 2015 May 30 (Vol. 23, No. 2486, p. 9).
10. Ronneberger O, Fischer P, Brox T. U-net: Convolutional networks for biomedical image segmentation. InInternational Conference on Medical image computing and computer-assisted intervention 2015 Oct 5 (pp. 234-241). Cham: Springer international publishing. https://doi.org/10.1007/978-3-319-24574-4_28 [doi]
11. Xin B, Ye M, Axel L, Metaxas DN. Rethinking deep unrolled model for accelerated MRI reconstruction. InEuropean Conference on Computer Vision 2024 Sep 29 (pp. 164-181). Cham: Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-73226-3_10 [doi]

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http://echo.ismrm.org/p/ISMRM2026/570-03-188