Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
605-01-005 ISMRM Abstract

Enabling Motion Correction for Ultra-High Field Submillimeter MP(2)RAGE and Quantitative T1 Mapping with SAMER

Primary: Acquisition & Reconstruction - Motion Correction: Neuro
Secondary: Acquisition & Reconstruction - Image Reconstruction: AI
605-01-005 · Motion Correction Across the Lifespan · Thursday, 14 May, 8:30 AM–10:20 AM · Ballroom West
Keywords: Motion Correction Deep learning reconstruction High-Resolution MRI Ultra-high field (UHF) MRI
Accepted
Jocelyn Philippe 1,2,3, Natalia Pato Montemayor1,2,3, Ludovica Romanin1, Patrick Liebig4, Daniel M Polak4, Bryan Clifford5, YANTU HUANG6, Daniel Nicolas Splitthoff4, Lina Bacha1,2,3, Tommaso Di Noto1,2,3, Bénédicte Maréchal1,2,3, Marcel Dominik Nickel4, Tobias Kober7, Robin Heidemann4, Jean-Philippe Thiran2,3, Tom Hilbert1,2,3, Thomas Yu1,2,3, Gian Franco Piredda1
1Swiss Innovation Hub, Siemens Healthineers International AG, Lausanne, Switzerland
2Department of Radiology, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland
3LTS5, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
4Siemens Healthineers GmbH, Forchheim, Germany
5Siemens Medical Solutions, Boston, United States of America
6Magnetic resonance, Siemens Shenzhen Magnetic Resonance Ltd., Shenzhen, China
7Research & Clinical Translation, Magnetic Resonance, Siemens Healthineers AG, Erlangen, Germany
Presenting Author: Jocelyn Philippe

Synopsis

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References

1. Balchandani P, Naidich T. Ultra-high-field MR neuroimaging. American Journal of Neuroradiology. 2015;36(7):1204-1215. doi:10.3174/ajnr.A4180 [doi]
2. Radojewski P, Piredda GF, Bonanno G, et al. Assessment of the available evidence for the use of 7‐TESLA (T) magnetic resonance imaging (MRI) in neurological and musculoskeletal disorders, with comparison to 3‐T and 1.5‐T MRI: A systematic scoping review. Euro J of Neurology. 2025;32(1). doi:10.1111/ene.16557 [doi]
3. Ladd ME, Bachert P, Meyerspeer M, et al. Pros and cons of ultra-high-field MRI/MRS for human application. Progress in Nuclear Magnetic Resonance Spectroscopy. 2018;109:1-50. doi:10.1016/j.pnmrs.2018.06.001 [doi]
4. Gallichan D, Marques JP, Gruetter R. Retrospective correction of involuntary microscopic head movement using highly accelerated fat image navigators (3D FatNavs) at 7T. Magnetic Resonance in Med. 2016;75(3):1030-1039. doi:10.1002/mrm.25670 [doi]
5. Polak D, Hossbach J, Splitthoff DN, et al. Motion guidance lines for robust data consistency–based retrospective motion correction in 2D and 3D MRI. Magnetic Resonance in Med. 2023;89(5):1777-1790. doi:10.1002/mrm.29534 [doi]
6. Huang Y, Polak D, Yu T, et al. Retrospective Motion Correction at Ultra-High Field using SAMER and Reflected Power. In: Proceedings of the 34th Annual Meeting of ISMRM. 2025.
7. Fujita S, Polak D, Nickel D, et al. Motion-Informed 3D Deep Learning Reconstruction in Patients with Cognitive Impairment. AJNR Am J Neuroradiol. Published online October 8, 2025:ajnr.A8977. doi:10.3174/ajnr.A8977 [doi]
8. Polak D, Nickel D, Deck J, et al. Integrating scout and guidance line-based retrospective motion correction into a 3D deep learning reconstruction for fast and robust brain MRI. In: Proceedings of the 33rd Annual Meeting of ISMRM. 2024.
9. Liu Z, Patel V, Zhou X, et al. Deep-Learning Reconstruction for 7T MP2RAGE and SPACE MRI: Improving Image Quality at High Acceleration Factors. AJNR Am J Neuroradiol. Published online May 20, 2025:ajnr.A8841. doi:10.3174/ajnr.A8841 [doi]
10. Batson J, Royer L. Noise2Self: Blind Denoising by Self-Supervision. arXiv. Preprint posted online 2019. doi:10.48550/ARXIV.1901.11365 [doi]
11. Yaman B, Hosseini SAH, Moeller S, Ellermann J, Uğurbil K, Akçakaya M. Self‐supervised learning of physics‐guided reconstruction neural networks without fully sampled reference data. Magnetic Resonance in Med. 2020;84(6):3172-3191. doi:10.1002/mrm.28378 [doi]
12. Papp D, Jaescke S, Rieger SW, Clare S, Hess AT. Simultaneous detection of cardiac, respiratory, and rigid body head motion using the scattering of a parallel transmit RF coil at 7T. In: Proceedings of the 27th Annual Meeting of ISMRM. 2018. https://archive.ismrm.org/2018/1168.html
13. Marques JP, Kober T, Krueger G, Van Der Zwaag W, Van De Moortele PF, Gruetter R. MP2RAGE, a self bias-field corrected sequence for improved segmentation and T1-mapping at high field. NeuroImage. 2010;49(2):1271-1281. doi:10.1016/j.neuroimage.2009.10.002 [doi]
14. Bridson R. Fast Poisson disk sampling in arbitrary dimensions. In: ACM SIGGRAPH 2007 Sketches. ACM; 2007:22. doi:10.1145/1278780.1278807 [doi]
15. Bacha L, Di Noto T, Venkategowda PB, et al. Advancing Brain Morphometry at 7T: A Pilot Study on Epilepsy Patients. In: Proceedings of the 34th Annual Meeting of ISMRM. 2025.
16. Venkategowda PB, Di Noto T, Prabhu M K, et al. Establishing Brain Reference Ranges in Indian Population: Comparison with Westerners and Validation for Parkinsonism Differential Diagnosis. In: Proceedings of the 34th Annual Meeting of ISMRM. 2025

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http://echo.ismrm.org/p/ISMRM2026/605-01-005