Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
452-03-016 / 452-03-016 ISMRM Abstract

S²-MoCo: A Fully Self-Supervised Subject-Specific k-Space Framework for Physically Consistent Motion Detection and Correction

Primary: Acquisition & Reconstruction - Motion Correction: Neuro
Secondary: Acquisition & Reconstruction - Artifacts and Correction Strategies
452-03-016 · Next-Generation MRI Image Enhancement · Tuesday, 12 May, 4:00 PM–5:36 PM · Power Pitch Theatre 2
452-03-016 · Next-Generation MRI Image Enhancement · Tuesday, 12 May, 4:00 PM–5:36 PM · Power Pitch Theatre 2
Keywords: Motion Correction Implicit neural representation Self-supervised learning Motion Detection Subject-specific MRI
Accepted
Siyun Jung 1,2, Kyu-Jin Jung1,2, Giulia Debiasi3, Chunlei Liu2,4, Dong-Hyun Kim1
1Department of Electrical and Electronic Engineering, Yonsei University, Seoul, Korea, Republic of
2Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, United States of America
3Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, United States of America
4Helen Wills Neuroscience Institute, University of California, Berkeley, United States of America
Presenting Author: Siyun Jung

Synopsis

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References

1. Chen Z, Pawar K, Ekanayake M, Pain C, Zhong S, Egan GF. Deep learning for image enhancement and correction in magnetic resonance imaging—state-of-the-art and challenges. J Digit Imaging. 2023;36(1):204-230. doi: 10.1007/s10278-022-00721-9 [doi]
2. Al-Haj Hemidi Z, Weihsbach C, Heinrich MP. IM-MoCo: self-supervised MRI motion correction using motion-guided implicit neural representations. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. Cham: Springer Nature Switzerland; 2024:382–392. doi: 10.1007/978-3-031-72104-5_37 [doi]
3. Dabrowski O, Falcone J-L, Klauser A, Songeon J, Kocher M, Courvoisier S. SISMIK for brain MRI: deep-learning-based motion estimation and model-based motion correction in k-space. IEEE Trans Med Imaging. 2024.doi: 10.1109/TMI.2024.3446450 [doi]
4. Vyas K, Li Y, Rho J, Pan Z, Jacobs D, Funkhouser T. Learning transferable features for implicit neural representations. Adv Neural Inf Process Syst. 2024;37:42268–42291. https://doi.org/10.48550/arXiv.2409.09566 [doi]
5. Dabrowski O, Falcone J-L, Klauser A, Songeon J, Kocher M, Courvoisier S. Choreography controlled (ChoCo) brain MRI artifact generation for labeled motion-corrupted datasets. Phys Med. 2022;102:79–87. doi: 10.1016/j.ejmp.2022.09.005 [doi]

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/452-03-016