Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
463-01-002 ISMRM Abstract

MoE-Unet: Using Conditional Sparse Activation to Improve the Capacity of Multi-task End-to-End Reconstruction

Primary: Acquisition & Reconstruction - Image Reconstruction: AI
Secondary: Acquisition & Reconstruction - AI methods
463-01-002 · Data-Driven Reconstruction: From Networks to Clinical Practice · Tuesday, 12 May, 8:20 AM–9:15 AM · Digital Posters Row D
Keywords: End-to-End Learning Reconstruction Mixture of Experts
Accepted
Yuyang Li1, Yipin Deng2, Wenlei Shang2, Zijian Zhou 3, Peng Hu2,4,5,6
1School of Biomedical Engineering, ShanghaiTech University, Shanghai, China
2School of Biomedical Engineering, ShanghaiTech University, Shanghai, China
3School of Biomedical Engineering & State Key Laboratory of Advanced Medical Materials and Devices, School of Biomedical Engineering, ShanghaiTech University, Shanghai, China
4Shanghai Clinical Research and Trial Center, ShanghaiTech University, Shanghai, China
5State Key Laboratory of Advanced Medical Materials and Devices, ShanghaiTech University, Shanghai, China
6Advanced Technology Research Institute, Shanghai United Imaging Co., Ltd, Shanghai, China
Presenting Author: Zijian Zhou

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References

1. Dar SUH, Özbey M, Çatlı AB, Çukur T. A Transfer-Learning Approach for Accelerated MRI Using Deep Neural Networks. Magn Reson Med. 2020 Aug;84(2):663-685. doi: 10.1002/mrm.28148. Epub 2020 Jan 3. PMID: 31898840. [doi] [pmid]
2. S. Mu and S. Lin, “A Comprehensive Survey of Mixture-of-Experts: Algorithms, Theory, and Applications,” Apr. 18, 2025, arXiv: arXiv:2503.07137. doi: 10.48550/arXiv.2503.07137. [doi]
3. A. Sriram et al., “End-to-End Variational Networks for Accelerated MRI Reconstruction,” Apr. 15, 2020, arXiv: arXiv:2004.06688. Accessed: Oct. 30, 2024. [Online]. Available: http://arxiv.org/abs/2004.06688
4. B. Xin, M. Ye, L. Axel, and D. N. Metaxas, “Fill the K-Space and Refine the Image: Prompting for Dynamic and Multi-Contrast MRI Reconstruction,” Sep. 25, 2023, arXiv: arXiv:2309.13839. doi: 10.48550/arXiv.2309.13839. [doi]
5. S. Kim, N. Ahn, and K.-A. Sohn, “Restoring spatially-heterogeneous distortions using mixture of experts network,” in Proc. Asian Conf. Comput. Vis. (ACCV), 2020.
6. Z. Wang et al., “MoEDiff-SR: Mixture of Experts-Guided Diffusion Model for Region-Adaptive MRI Super-Resolution,” Apr. 09, 2025, arXiv: arXiv:2504.07308. doi: 10.48550/arXiv.2504.07308. [doi]
7. S. Pavlitska, E. Eisen, and J. M. Zöllner, “Towards Adversarial Robustness of Model-Level Mixture-of-Experts Architectures for Semantic Segmentation,” Dec. 16, 2024, arXiv: arXiv:2412.11608. doi: 10.48550/arXiv.2412.11608. [doi]
8. J. Zbontar et al., “fastMRI: An open dataset and benchmarks for accelerated MRI,” arXiv preprint arXiv:1811.08839, 2018.

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http://echo.ismrm.org/p/ISMRM2026/463-01-002