Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
664-04-011 ISMRM Abstract

Zero-Shot Temporal Annihilation Reconstruction for Myocardial Perfusion Imaging

Primary: Acquisition & Reconstruction - Image Reconstruction: AI
Secondary: Contrast Mechanisms - Perfusion
664-04-011 · Image Reconstruction: Self-Supervised and Implicit · Thursday, 14 May, 2:35 PM–3:30 PM · Digital Posters Row E
Keywords: AI/ML Image Reconstruction Physics-guided deep learning Dynamic MRI Reconstruction Zero shot self supervised learning Myocardial perfusion imaging
Accepted
Alex Macintyre 1,2, Xi Chen1, Debiao Li3,4, Anthony G Christodoulou1,2,4
1Department of Radiological Sciences, David Geffen School of Medicine at UCLA, Los Angeles, United States of America
2Physics and Biology in Medicine Graduate Program, University of California Los Angeles, Los Angeles, United States of America
3Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, United States of America
4Department of Bioengineering, University of California Los Angeles, Los Angeles, United States of America
Presenting Author: Alex Macintyre

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References

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2. J. P. Haldar and K. Setsompop, "Linear Predictability in Magnetic Resonance Imaging Reconstruction: Leveraging Shift-Invariant Fourier Structure for Faster and Better Imaging," in IEEE Signal Processing Magazine, vol. 37, no. 1, pp. 69-82, Jan. 2020, doi: 10.1109/MSP.2019.2949570. [doi]
3. Heckel, R., Jacob, M., Chaudhari, A. et al. Deep learning for accelerated and robust MRI reconstruction. Magn Reson Mater Phy 37, 335–368 (2024). https://doi.org/10.1007/s10334-024-01173-8 [doi]
4. Pramanik A, Aggarwal HK, Jacob M. Deep Generalization of Structured Low-Rank Algorithms (Deep-SLR). IEEE Trans Med Imaging. 2020 Dec;39(12):4186-4197. doi: 10.1109/TMI.2020.3014581 [doi]
5. Cao C, Cui Z-X, Zhu Q, Liu C, Liang D, Zhu Y. Annihilation-Net: Learned annihilation relation for dynamic MR imaging. Med Phys. 2024; 51: 1883–1898. https://doi.org/10.1002/mp.16723 [doi]
6. Y. Han, L. Sunwoo and J. C. Ye, " k -Space Deep Learning for Accelerated MRI," in IEEE Transactions on Medical Imaging, vol. 39, no. 2, pp. 377-386, Feb. 2020, doi: 10.1109/TMI.2019.2927101. [doi]
7. Yaman B, Hosseini SA, Akçakaya M. Zero-shot self-supervised learning for MRI reconstruction. arXiv preprint arXiv:2102.07737. 2021 Feb 15.
8. Chen X, Li D, Christodoulou AG. Temporal structured low-rank reconstruction for first-pass myocardial perfusion imaging. Proc Int Soc Magn Reson Med. 2024:631. doi:10.58530/2024/0631 [doi]
9. Kellman, P., Epstein, F.H. and McVeigh, E.R. (2001), Adaptive sensitivity encoding incorporating temporal filtering (TSENSE)†. Magn. Reson. Med., 45: 846-852. https://doi.org/10.1002/mrm.1113 [doi]
10. Adluru, G., McGann, C., Speier, P., Kholmovski, E.G., Shaaban, A. and DiBella, E.V.R. (2009), Acquisition and reconstruction of undersampled radial data for myocardial perfusion magnetic resonance imaging. J. Magn. Reson. Imaging, 29: 466-473. https://doi.org/10.1002/jmri.21585 [doi]

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http://echo.ismrm.org/p/ISMRM2026/664-04-011