Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
503-03-005 ISMRM Abstract

PSIRNet: Deep Learning–Based Free-Breathing Rapid-Acquisition Late Enhancement Imaging

Accepted
Arda Atalik 1,2,3,4, Hui Xue1, Daniel K Sodickson3,4, Michael S Hansen1, Peter Kellman1
1Health Futures, Microsoft Research, Redmond, United States of America
2Center for Data Science, New York University, New York, United States of America
3The Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, United States of America
4The Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University Grossman School of Medicine, New York, United States of America
Presenting Author: Arda Atalik

Synopsis

Motivation:
Goals:
Approach:
Results:
Full abstract & presentation

The full text, figures, and any recorded presentation for this abstract are not shown here. Log in if you are a member or registered attendee with access.

Full abstracts, figures, and presentations for Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition are available to registered attendees. This content becomes freely available to the public roughly two years after the meeting.

To request or purchase access, contact the ISMRM Central Office at info@ismrm.org.

Log in

References

1. Kellman, P., & Arai, A. E. (2012). Cardiac imaging techniques for physicians: late enhancement. Journal of Magnetic Resonance Imaging, 36(3), 529–542. https://doi.org/10.1002/jmri.23605 [doi]
2. Kellman, P., Arai, A. E., McVeigh, E. R., & Aletras, A. H. (2002). Phase‐sensitive inversion recovery for detecting myocardial infarction using gadolinium‐delayed hyperenhancement. Magnetic Resonance in Medicine, 47(2), 372–383. https://doi.org/10.1002/mrm.10051 [doi]
3. Ledesma‐Carbayo, M. J., Kellman, P., Arai, A. E., & McVeigh, E. R. (2007). Motion corrected free‐breathing delayed‐enhancement imaging of myocardial infarction using nonrigid registration. Journal of Magnetic Resonance Imaging, 26(1), 184–190. https://doi.org/10.1002/jmri.20957 [doi]
4. Kellman, P., Xue, H., & Hansen, M. S. (2016). Free-breathing late enhancement imaging: phase sensitive inversion recovery (PSIR) with respiratory motion corrected (MOCO) averaging. Magnetom Flash, 66, 65–73.
5. van der Velde, N., Hassing, H. C., Bakker, B. J., Wielopolski, P. A., Lebel, R. M., Janich, M. A., ... & Hirsch, A. (2021). Improvement of late gadolinium enhancement image quality using a deep learning–based reconstruction algorithm and its influence on myocardial scar quantification. European Radiology, 31(6), 3846–3855. https://doi.org/10.1007/s00330-020-07461-w [doi]
6. Muscogiuri, G., Martini, C., Gatti, M., Dell'Aversana, S., Ricci, F., Guglielmo, M., ... & Pontone, G. (2021). Feasibility of late gadolinium enhancement (LGE) in ischemic cardiomyopathy using 2D-multisegment LGE combined with artificial intelligence reconstruction deep learning noise reduction algorithm. International Journal of Cardiology, 343, 164–170. https://doi.org/10.1016/j.ijcard.2021.09.012 [doi]
7. Yaman, B., Shenoy, C., Deng, Z., Moeller, S., El-Rewaidy, H., Nezafat, R., & Akçakaya, M. (2021, April). Self-supervised physics-guided deep learning reconstruction for high-resolution 3D LGE CMR. In 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI) (pp. 100–104). IEEE. https://doi.org/10.1109/ISBI48211.2021.9434054 [doi]
8. Kellman, P., Xue, H., Olivieri, L. J., Cross, R. R., Grant, E. K., Fontana, M., ... & Hansen, M. S. (2016). Dark blood late enhancement imaging. Journal of Cardiovascular Magnetic Resonance, 18(1), 77. https://doi.org/10.1186/s12968-016-0297-3 [doi]
9. Rashid, S., Rapacchi, S., Vaseghi, M., Tung, R., Shivkumar, K., Finn, J. P., & Hu, P. (2014). Improved late gadolinium enhancement MR imaging for patients with implanted cardiac devices. Radiology, 270(1), 269–274. https://doi.org/10.1148/radiol.13130942 [doi]
10. Microsoft. (2025). Tyger: Remote signal processing (Version 0.13.5) [Computer software]. GitHub. https://github.com/microsoft/tyger/releases/tag/v0.13.5
11. Hansen, M. S., & Sørensen, T. S. (2013). Gadgetron: an open source framework for medical image reconstruction. Magnetic Resonance in Medicine, 69(6), 1768–1776. https://doi.org/10.1002/mrm.24389 [doi]
12. Landweber, L. (1951). An Iteration Formula for Fredholm Integral Equations of the First Kind. American Journal of Mathematics, 73(3), 615–624. https://doi.org/10.2307/2372313 [doi]
13. Hammernik, K., Klatzer, T., Kobler, E., Recht, M. P., Sodickson, D. K., Pock, T., & Knoll, F. (2018). Learning a variational network for reconstruction of accelerated MRI data. Magnetic Resonance in Medicine, 79(6), 3055–3071. https://doi.org/10.1002/mrm.26977 [doi]
14. Ronneberger, O., Fischer, P., & Brox, T. (2015, October). U-net: Convolutional networks for biomedical image segmentation. In International Conference on Medical image computing and computer-assisted intervention (pp. 234-241). Cham: Springer international publishing. https://doi.org/10.1007/978-3-319-24574-4_28 [doi]
15. Wang, Z., Bovik, A. C., Sheikh, H. R., & Simoncelli, E. P. (2004). Image quality assessment: from error visibility to structural similarity. IEEE Transactions on Image Processing, 13(4), 600–612. https://doi.org/10.1109/TIP.2003.819861 [doi]

Cite this abstract