Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
365-02-012 ISMRM Abstract

Deep learning-based motion compensated reconstruction for self-gated cardiac MRA utilizing self-supervised finetuning

Primary: Acquisition & Reconstruction - Image Reconstruction: AI
Secondary: Cardiovascular - Vascular and Vessel Wall
365-02-012 · Post-Processing of Cardiovascular MRI · Monday, 11 May, 9:15 AM–10:10 AM · Digital Posters Row F
Keywords: Deep learning reconstruction Self-supevised learning Motion compensated reconstruction 3D MRA
Accepted
Daniel Amsel 1,2, Robert Stoll2,3, Jens Wetzl2, Daniel Giese2, Majd Helo1,2, Marcel Dominik Nickel2, Michaela Schmidt2, Jens Kübler4, Andreas Lingg4, Patrick Krumm4, Thomas Küstner1
1Medical Image and Data Analysis (MIDAS.lab), Department of Diagnostic and Interventional Radiology, University Hospital Tuebingen, Tuebingen, Germany
2Research & Clinical Translation, Magnetic Resonance, Siemens Healthineers AG, Erlangen, Germany
3Technische Universität Berlin, Berlin, Germany
4Diagnostic and Interventional Radiology, University Hospital Tuebingen, Tuebingen, Germany
Presenting Author: Daniel Amsel

Synopsis

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References

1. Fahlenkamp UL, Lembcke A, Roesler R, et al. ECG-gated imaging of the left atrium and pulmonary veins: Intra-individual comparison of CTA and MRA. Clin Radiol. 2013;68(10):1059-1064. doi:10.1016/j.crad.2013.05.006 [doi]
2. Edelman RR, Koktzoglou I. Noncontrast MR angiography: An update. J Magn Reson Imaging. 2019;49(2):355-373. doi:10.1002/jmri.26288 [doi]
3. Batchelor PG, Atkinson D, Irarrazaval P, Hill DLG, Hajnal J, Larkman D. Matrix description of general motion correction applied to multishot images. Magn Reson Med. 2005;54(5):1273-1280. doi:10.1002/mrm.20656 [doi]
4. Schäffter T, Rasche V, Carlsen IC. Motion compensated projection reconstruction. Magn Reson Med. 1999;41(5):954-963. doi:10.1002/(SICI)1522-2594(199905)41:5%3C954::AID-MRM15%3E3.0.CO;2-J [doi]
5. Bustin A, Rashid I, Cruz G, et al. 3D whole-heart isotropic sub-millimeter resolution coronary magnetic resonance angiography with non-rigid motion-compensated PROST. J Cardiovasc Magn Reson. 2020;22(1):24. doi:10.1186/s12968-020-00611-5 [doi]
6. Qi H, Hajhosseiny R, Cruz G, et al. End-to-end deep learning nonrigid motion-corrected reconstruction for highly accelerated free-breathing coronary MRA. Magn Reson Med. 2021;86(4):1983-1996. doi:10.1002/mrm.28851 [doi]
7. Zeilinger MG, Giese D, Schmidt M, et al. Highly accelerated, Dixon-based non-contrast MR angiography versus high-pitch CT angiography. Radiol Med (Torino). 2024;129(2):268-279. doi:10.1007/s11547-023-01752-0 [doi]
8. Vasanawala S, Murphy M, Alley M, et al. Practical parallel imaging compressed sensing MRI: Summary of two years of experience in accelerating body MRI of pediatric patients. In: 2011 IEEE International Symposium on Biomedical Imaging: From Nano to Macro. 2011:1039-1043. doi:10.1109/ISBI.2011.5872579 [doi]
9. Stoll R, Kolbitsch C, Nickel MD, Schmidt M, Schäffter T, Giese D. Respiratory motion-corrected model-based 3D water-fat MRA at 0.55T. In Proc. ISMRM 2025
10. Feng L, Axel L, Chandarana H, Block KT, Sodickson DK, Otazo R. XD-GRASP: Golden-angle radial MRI with reconstruction of extra motion-state dimensions using compressed sensing. Magn Reson Med. 2016;75(2):775-788. doi:10.1002/mrm.25665 [doi]
11. Klein S, Staring M, Murphy K, Viergever MA, Pluim JPW. elastix: A Toolbox for Intensity-Based Medical Image Registration. IEEE Trans Med Imaging. 2010;29(1):196-205. doi:10.1109/TMI.2009.2035616 [doi]
12. Xu S, Ghoul A, Hammernik K, et al. Self-supervised Motion-Compensated Reconstruction for Cardiac Cine MRI. In: Felsner L, Küstner T, Maier A, et al., eds. Reconstruction and Imaging Motion Estimation, and Graphs in Biomedical Image Analysis. Springer Nature Switzerland; 2026:97-107. doi:10.1007/978-3-032-06103-4_10 [doi]
13. Millard C, Chiew M. A Theoretical Framework for Self-Supervised MR Image Reconstruction Using Sub-Sampling via Variable Density Noisier2Noise. IEEE Trans Comput Imaging. 2023;9:707-720. doi:10.1109/TCI.2023.3299212 [doi]
14. 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. Magn Reson Med. 2020;84(6):3172-3191. doi:10.1002/mrm.28378 [doi]

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/365-02-012