Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
560-04-013 ISMRM Abstract

Extended Phase Graph-Informed Deep Learning for Accelerated and Improved Joint T1 and T2 Cardiac Mapping

Primary: Acquisition & Reconstruction - Quantitative Imaging: relaxometry, multi-parametric, MR Fingerprinting, and synthetic MRI
Secondary: Cardiovascular - Myocardium
560-04-013 · Automated Cardiovascular MRI · Wednesday, 13 May, 2:35 PM–3:30 PM · Digital Posters Row A
Keywords: Quantitative myocardial mapping T1 T2 Mapping Extended Phase Graph Deep-learning-based image reconstruction Myocardial T1-mapping
Accepted
Catarina N Carvalho1,2, Andreia S Gaspar2, Rita G Nunes2, Teresa M Correia 1,3
1Quantitative Bio-Imaging Lab, Center of Marine Sciences (CCMAR), Faro, Portugal
2Institute for Systems and Robotics – Lisboa and Department of Bioengineering, Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Portugal
3School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
Presenting Author: Teresa M Correia

Synopsis

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References

1. C. Wang et al., ‘CMRxRecon: A publicly available k-space dataset and benchmark to advance deep learning for cardiac MRI’, Sci. Data, vol. 11, no. 1, p. 687, June 2024, doi: 10.1038/s41597-024-03525-4. [doi]
2. T. Cao et al., ‘Free‐breathing, non‐ ECG , simultaneous myocardial T1 , T2 , T2 *, and fat‐fraction mapping with motion‐resolved cardiovascular MR multitasking’, Magn. Reson. Med., vol. 88, no. 4, pp. 1748–1763, Oct. 2022, doi: 10.1002/mrm.29351. [doi]
3. A. Phair, G. Cruz, H. Qi, R. M. Botnar, and C. Prieto, ‘Free‐running 3D whole‐heart T1 and T2 mapping and cine MRI using low‐rank reconstruction with non‐rigid cardiac motion correction’, Magn. Reson. Med., vol. 89, no. 1, pp. 217–232, Jan. 2023, doi: 10.1002/mrm.29449. [doi]
4. M. G. Crabb et al., ‘3D joint T1 /T1ρ /T2 mapping and water‐fat imaging for contrast‐agent free myocardial tissue characterization at 1.5T’, Magn. Reson. Med., vol. 93, no. 6, pp. 2297–2310, June 2025, doi: 10.1002/mrm.30397. [doi]
5. C. N. Carvalho, A. S. Gaspar, R. G. Nunes, and T. Correia, ‘Accelerated and Accurate Myocardial T1 Mapping with PENGUIN: combining deep learning with Extended Phase Graph Modeling’, presented at the 2024 ISMRM & ISMRT Annual Meeting & Exhibition, Honolulu, Hawai’i, USA, May 2025. [Online]. Available: https://archive.ismrm.org/2025/1094.html
6. M. Weigel, ‘Extended phase graphs: Dephasing, RF pulses, and echoes ‐ pure and simple’, J. Magn. Reson. Imaging, vol. 41, no. 2, pp. 266–295, Feb. 2015, doi: 10.1002/jmri.24619. [doi]
7. N. D. Gai, C. Stehning, M. Nacif, and D. A. Bluemke, ‘Modified Look‐Locker T1 evaluation using Bloch simulations: Human and phantom validation’, Magn. Reson. Med., vol. 69, no. 2, pp. 329–336, Feb. 2013, doi: 10.1002/mrm.24251. [doi]
8. C. Wang et al., ‘CMRxRecon: A publicly available k-space dataset and benchmark to advance deep learning for cardiac MRI’, Sci. Data, vol. 11, no. 1, p. 687, June 2024, doi: 10.1038/s41597-024-03525-4. [doi]
9. P. Putzky and M. Welling, ‘Recurrent Inference Machines for Solving Inverse Problems’, June 13, 2017, arXiv: arXiv:1706.04008. Accessed: Sept. 04, 2024. [Online]. Available: http://arxiv.org/abs/1706.04008

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http://echo.ismrm.org/p/ISMRM2026/560-04-013