Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
461-03-004 ISMRM Abstract

Motion Parameter Estimation for Brain MRI Using Physics-Based K-Space Simulation and Deep Learning

Primary: Acquisition & Reconstruction - Artifacts and Correction Strategies
Secondary: Acquisition & Reconstruction - Motion Correction: Neuro
461-03-004 · Artifact Correction Strategies in Brain and Body · Tuesday, 12 May, 1:40 PM–2:35 PM · Digital Posters Row B
Keywords: Motion Correction Image Quality Assessment CONVOLUTIONAL NEURAL NETWORK Brain magnetic resonance imaging Physics-based simulation
Accepted
Sena Azamat1, Saritha Unnikrishnan2, Esin Ozturk Isik 3
1Department of Radiology, Basaksehir Cam and Sakura City Hospital, Istanbul, Turkey
2Department of Computing and Electronics Engineering, Atlantic Technological University, Sligo, Ireland
3Institute of Biomedical Engineering, Bogazici University, Istanbul, Turkey
Presenting Author: Esin Ozturk Isik

Synopsis

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References

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2. Hedley M, Yan H. Motion artifact suppression: a review of post-processing techniques. Magn Reson Imaging. 1992;10: 627–635. doi:10.1016/0730-725x(92)90014-q [doi]
3. Godenschweger F, Kägebein U, Stucht D, Yarach U, Sciarra A, Yakupov R, et al. Motion correction in MRI of the brain. Phys Med Biol. 2016;61: R32–R56. doi:10.1088/0031-9155/61/5/r32 [doi]
4. Al-masni MA, Lee S, Yi J, Kim S, Gho S-M, Choi YH, et al. Stacked U-Nets with self-assisted priors towards robust correction of rigid motion artifact in brain MRI. Neuroimage. 2022;259: 119411. doi:10.1016/j.neuroimage.2022.119411 [doi]
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6. Hossbach J, Splitthoff DN, Cauley S, Clifford B, Polak D, Lo W-C, et al. Deep learning-based motion quantification from k-space for fast model-based magnetic resonance imaging motion correction. Med Phys. 2023;50: 2148–2161. doi:10.1002/mp.16119 [doi]
7. Shaw R, Sudre CH, Varsavsky T, Ourselin S, Cardoso MJ. A k-space model of movement artefacts: Application to segmentation augmentation and artefact removal. IEEE Trans Med Imaging. 2020;39: 2881–2892. doi:10.1109/tmi.2020.2972547 [doi]
8. Zbontar J, Knoll F, Sriram A, Murrell T, Huang Z, Muckley MJ, et al. FastMRI: An open dataset and benchmarks for accelerated MRI. arXiv [cs.CV]. 2018. Available: http://arxiv.org/abs/1811.08839
9. Olsson H, Millward JM, Starke L, Gladytz T, Klein T, Fehr J, et al. Simulating rigid head motion artifacts on brain magnitude MRI data-Outcome on image quality and segmentation of the cerebral cortex. PLoS One. 2024;19: e0301132. doi:10.1371/journal.pone.0301132 [doi]

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/461-03-004