Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
508-02-006 ISMRM Abstract

An Accelerated Deep Image Prior Reconstruction for Cardiac MR Fingerprinting Using Meta-Learning

Primary: Acquisition & Reconstruction - Quantitative Imaging: relaxometry, multi-parametric, MR Fingerprinting, and synthetic MRI
Secondary: Cardiovascular - Myocardium
508-02-006 · Novel Reconstruction Techniques for Fast Imaging · Wednesday, 13 May, 8:20 AM–10:10 AM · Meeting Room 1.60
Keywords: Deep learning Cardiac Quantitative Parameter Mapping Reconstruction Myocardial tissue characterization
Accepted
Zhongnan Liu1, Calder D Sheagren2, Zexuan Liu3, Nicole Seiberlich2,3, Liyue Shen1, Jesse Hamilton 2,3
1Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, United States of America
2Department of Radiology, University of Michigan, Ann Arbor, United States of America
3Department of Biomedical Engineering, University of Michigan, Ann Arbor, United States of America
Presenting Author: Jesse Hamilton

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References

1. Hamilton JI, Jiang Y, Chen Y, et al. MR fingerprinting for rapid quantification of myocardial T1, T2, and proton spin density. Magnetic Resonance in Medicine. 2017;77(4):1446-1458. doi:10.1002/mrm.26216 [doi]
2. Lima da Cruz G, Bustin A, Jaubert O, Schneider T, Botnar RM, Prieto C. Sparsity and locally low rank regularization for MR fingerprinting. Magnetic Resonance in Medicine. 2019;81(6):3530-3543. doi:10.1002/mrm.27665 [doi]
3. Hamilton JI. A Self-Supervised Deep Learning Reconstruction for Shortening the Breathhold and Acquisition Window in Cardiac Magnetic Resonance Fingerprinting. Front Cardiovasc Med. 2022;9. doi:10.3389/fcvm.2022.928546 [doi]
4. Vettoruzzo A, Bouguelia MR, Vanschoren J, Rögnvaldsson T, Santosh K. Advances and Challenges in Meta-Learning: A Technical Review. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2024;46(7):4763-4779. doi:10.1109/TPAMI.2024.3357847 [doi]
5. Uecker M, Lai P, Murphy MJ, et al. ESPIRiT—an eigenvalue approach to autocalibrating parallel MRI: Where SENSE meets GRAPPA. Magnetic Resonance in Medicine. 2014;71(3):990-1001. doi:10.1002/mrm.24751 [doi]
6. Hamilton JI, Seiberlich N. Machine Learning for Rapid Magnetic Resonance Fingerprinting Tissue Property Quantification. Proceedings of the IEEE. 2020;108(1):69-85. doi:10.1109/JPROC.2019.2936998 [doi]
7. Nichol A, Achiam J, Schulman J. On First-Order Meta-Learning Algorithms. arXiv. Preprint posted online October 22, 2018. doi:10.48550/arXiv.1803.02999 [doi]
8. Jiang Y, Ma D, Seiberlich N, Gulani V, Griswold MA. MR fingerprinting using fast imaging with steady state precession (FISP) with spiral readout. Magnetic Resonance in Medicine. 2015;74(6):1621-1631. doi:10.1002/mrm.25559 [doi]
9. McGivney DF, Pierre E, Ma D, et al. SVD Compression for Magnetic Resonance Fingerprinting in the Time Domain. IEEE Transactions on Medical Imaging. 2014;33(12):2311-2322. doi:10.1109/TMI.2014.2337321 [doi]

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/508-02-006