Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
464-06-001 ISMRM Abstract

Generative Adversarial Network for Motion Correction in Free-breathing Abdominal T2-Weighted Fast Spin-echo MRI

Primary: Acquisition & Reconstruction - Motion Correction: Non-Neuro
Secondary: Body - Liver
464-06-001 · Novel Diffusion and Motion Correction in Body MRI · Tuesday, 12 May, 4:55 PM–5:50 PM · Digital Posters Row E
Keywords: AI/ML Image Reconstruction Abdominal MRI Motion artifact correction Free-breathing MRI
Accepted
Yi Li 1, ZILONG HUANG1, HAILIN XIONG1, Wing Yat Cheung1, Chenglang Yuan1, Shihui Chen1,2, Liyuan Liang1,2, Xiaorui Xu3, Tianbaige Liu1, QITING WU1, Mei-Lan Chu4, Hsiao‐Wen Chung4, Nan-kuei Chen5, Qi DOU6, Hing-Chiu Chang1,2
1Department of Biomedical Engineering, The Chinese University of Hong Kong, Shatin, Hong Kong
2Multi-Scale Medical Robotics Center, Shatin, Hong Kong
3Department of Diagnostic Radiology, The University of Hong Kong, Hong Kong, China
4Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei, Taiwan
5Biomedical Engineering, University of Arizona, Tucson, United States of America
6Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong, China
Presenting Author: Yi Li

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References

1. Li Y, Liang L, Chen S, et al. Robust Free-breathing Abdominal Fast Spin-Echo MRI Enabled by Improved Repeated k-t-subsampling and Artifact Minimization (iReKAM). In: Honolulu, Hawaii, USA; :4592. doi:10.58530/2025/4592 [doi]
2. Spieker V, Eichhorn H, Hammernik K, et al. Deep Learning for Retrospective Motion Correction in MRI: A Comprehensive Review. IEEE Trans Med Imaging. 2024;43(2):846-859. doi:10.1109/TMI.2023.3323215 [doi]
3. T, Armanious K, Yang J, Yang B, Schick F, Gatidis S. Retrospective correction of motion‐affected MR images using deep learning frameworks. Magnetic Resonance in Med. 2019;82(4):1527-1540. doi:10.1002/mrm.27783 [doi]
4. Chen G, Xie H, Rao X, et al. MRI Motion Correction Through Disentangled CycleGAN Based on Multi-Mask K-Space Subsampling. IEEE Transactions on Medical Imaging. 2025:1-1. doi:10.1109/TMI.2024.3523949 [doi]
5. Zaitsev M, Maclaren J, Herbst M. Motion artifacts in MRI: A complex problem with many partial solutions. Magnetic Resonance Imaging. 2015;42(4):887-901. doi:10.1002/jmri.24850 [doi]
6. Zhang W, Basaran B, Meng Q, et al. MoCoSR: Respiratory Motion Correction and Super-Resolution for 3D Abdominal MRI. In: Greenspan H, Madabhushi A, Mousavi P, et al., eds. Medical Image Computing and Computer Assisted Intervention – MICCAI 2023. Cham: Springer Nature Switzerland; 2023:121-131. doi:10.1007/978-3-031-43999-5_12 [doi]
7. Rao X, Liu K, Xu J, Chen G, Bao Q, Liu C. Motion correction combining Unet++ and swin transformer for magnetic resonance image. In: Fourth International Conference on Image Processing and Intelligent Control (IPIC 2024). Vol 13250. SPIE; 2024:57-62. doi:10.1117/12.3038569 [doi]
8. Maji D, Sigedar P, Singh M. Attention Res-UNet with Guided Decoder for semantic segmentation of brain tumors. Biomedical Signal Processing and Control. 2022;71:103077. doi:10.1016/j.bspc.2021.103077 [doi]
9. Simonyan K, Zisserman A. Very Deep Convolutional Networks for Large-Scale Image Recognition. April 2015. doi:10.48550/arXiv.1409.1556 [doi]
10. Johnson J, Alahi A, Fei-Fei L. Perceptual Losses for Real-Time Style Transfer and Super-Resolution. March 2016. doi:10.48550/arXiv.1603.08155 [doi]
11. McGee KP, Manduca A, Felmlee JP, Riederer SJ, Ehman RL. Image metric-based correction (Autocorrection) of motion effects: Analysis of image metrics. Journal of Magnetic Resonance Imaging. 2000;11(2):174-181. doi:10.1002/(SICI)1522-2586(200002)11:2%3C174::AID-JMRI15%3E3.0.CO;2-3 [doi]

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/464-06-001