Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
530-04-002 ISMRM Abstract

Self-supervised deep learning reconstruction with k-space motion correction for accelerated First-Pass Perfusion Cardiac MRI

Primary: Acquisition & Reconstruction - Image Reconstruction: AI
Secondary: Acquisition & Reconstruction - Motion Correction: Non-Neuro
530-04-002 · Perfusion and Coronary Assessment · Wednesday, 13 May, 4:00 PM–5:50 PM · Meeting Room 2.60
Keywords: First pass perfusion Free-Breathing Self-supervised learning Undersampled MRI Reconstruction Cardiovascular magnetic resonance imaging
Accepted
Elisa Moya-Sáez 1,2, Elena Martín-González3, Rosa M Menchón-Lara1,4, Javier Sánchez-González5, Carlos Real6, Carlos Galán-Arriola6, Rita G Nunes7, Borja Ibañez6, Carlos Alberola-López1,2, Teresa M Correia8,9
1Image Processing Laboratory, Universidad de Valladolid, Valladolid, Spain
2Health Research Institute of Valladolid (IBioVALL), Valladolid, Spain
3Sycai Technologies SL, Barcelona, Spain
4Universidad Politécnica de Cartagena member of European University of Technology EUT+, Cartagena, Spain
5Philips Healthcare, Madrid, Spain
6Spanish National Centre for Cardiovascular Research (CNIC), Madrid, Spain
7Institute for Systems and Robotics – Lisboa and Department of Bioengineering, Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Portugal
8Quantitative Bio-Imaging Lab, CCMAR, Faro, Portugal
9School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
Presenting Author: Elisa Moya-Sáez

Synopsis

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References

1. Kotecha T., et al. Automated pixel-wise quantitative myocardial perfusion mapping by CMR to detect obstructive coronary artery disease and coronary microvascular dysfunction: validation against invasive coronary physiology. JACC Cardiovasc. Imaging. 2019;12(10):1958–1969. doi: 10.1016/j.jcmg.2018.12.022 [doi]
2. Chiribiri A, Arai AE, DiBella E, et al. SCMR Expert Consensus Statement on Quantitative Myocardial Perfusion Cardiovascular Magnetic Resonance Imaging. J Cardiovasc Magn Reson. 2025;101940. doi: 10.1016/j.jocmr.2025.101940 [doi]
3. Otazo R., et al. Combination of compressed sensing and parallel imaging for highly accelerated first-pass cardiac perfusion MRI. Magn Reson Med. 2010;64(3):767–776. doi:
4. Lingala SG, et al. Accelerated dynamic MRI exploiting sparsity and low-rank structure: k-t SLR. IEEE Trans Med Imaging. 2011;30(5):1042-54. doi: 10.1109/TMI.2010.2100850. [doi]
5. Martín-González, et al. Physics-informed self-supervised deep learning reconstruction for accelerated first-pass perfusion cardiac MRI. In MLMIR 2021;86-95. doi: 10.1007/978-3-030-88552-6_9 [doi]
6. Moya-Sáez E, et al. K-CC-MoCo: a fast respiratory motion correction in coil-compressed K-space for highly accelerated first-pass perfusion cardiac MRI. In: Proceedings of the Annual Meeting of the International Society for Magnetic Resonance in Medicine (ISMRM); 2025. doi:10.58530/2025/0029 [doi]
7. Van Vaals JJ., et al. “Keyhole” method for accelerating imaging of contrast agent uptake. J. Magn. Reson. Imaging 1993;3(4):671-675. doi: 10.1002/jmri.1880030419 [doi]
8. Wissmann L., et al. MRXCAT: realistic numerical phantoms for cardiovascular magnetic resonance. J Cardiovasc Magn Reson. 2014;16,63. doi: 10.1186/s12968-014-0063-3 [doi]
9. Kim, D., et al. Region‐optimized virtual (ROVir) coils: Localization and/or suppression of spatial regions using sensor‐domain beamforming. Magn Reson Med. 2021;86(1),197-212. doi: 10.1002/mrm.28706 [doi]
10. Blumenthal M., et al. Self‐supervised learning for improved calibrationless radial MRI with NLINV‐Net. Magn Reson Med. 2024;92(6):2447-2463. doi: 10.1002/mrm.30234 [doi]
11. Ronneberger O., et al. U-net: Convolutional networks for biomedical image segmentation. In: MICCAI 2015;234–241. doi: 10.1007/978-3-319-24574-4_28 [doi]
12. Menchón-Lara R.M., et al. Efficient convolution-based pairwise elastic image registration on three multimodal similarity metrics. Signal Processing 2023;202:108771. doi: 10.1016/j.sigpro.2022.108771 [doi]

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/530-04-002