Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
430-03-005 ISMRM Abstract

UMPIRE-Net: Unrolled Magnitude-Phase Regularization Network for MRI Reconstruction

Primary: Acquisition & Reconstruction - Image Reconstruction: AI
Secondary: Acquisition & Reconstruction - Artifacts and Correction Strategies
430-03-005 · Advanced Image Reconstruction · Tuesday, 12 May, 1:40 PM–3:30 PM · Meeting Room 2.60
Keywords: Physics-driven Deep Learning Partial Fourier Phase regularization Magnitude Regularization Phase variation
Accepted
Mahdi Saberi1,2, Toygan Kilic1,2, Mehmet Akcakaya 1,2
1Department of Electrical and Computer Engineering, University of Minnesota, Minneapolis, United States of America
2Center for Magnetic Resoance Research, University of Minnesota, Minneapolis, United States of America
Presenting Author: Mehmet Akcakaya

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. Hammernik, Kerstin, et al. "Learning a variational network for reconstruction of accelerated MRI data." Magnetic Resonance in Medicine 79.6 (2018): 3055-3071. PMID: 29115689 [pmid]
2. Aggarwal, Hemant K., Merry P. Mani, and Mathews Jacob. "MoDL: Model-based deep learning architecture for inverse problems." IEEE Transactions on Medical Imaging 38.2 (2018): 394-405. PMID: 30106719 [pmid]
3. Hammernik, Kerstin, et al. "Physics-driven deep learning for computational magnetic resonance imaging: Combining physics and machine learning for improved medical imaging." IEEE Signal Processing Magazine 40.1 (2023): 98-114. PMID: 37304755 [pmid]
4. Pruessmann, Klaas P., et al. "SENSE: sensitivity encoding for fast MRI." Magnetic Resonance in Medicine 42.5 (1999): 952-962. PMID: 10542355 [pmid]
5. Lustig, Michael, David Donoho, and John M. Pauly. "Sparse MRI: The application of compressed sensing for rapid MR imaging." Magnetic Resonance in Medicine: 58.6 (2007): 1182-1195. PMID: 17969013 [pmid]
6. Ong, Frank, Joseph Y. Cheng, and Michael Lustig. "General phase regularized reconstruction using phase cycling." Magnetic Resonance in Medicine 80.1 (2018): 112-125. PMID: 29159989 [pmid]
7. Zhao, Feng, et al. "Separate magnitude and phase regularization via compressed sensing." IEEE Transactions on Medical Imaging 31.9 (2012): 1713-1723. PMID: 22552571 [pmid]
8. Lee, Dongwook, et al. "Deep residual learning for accelerated MRI using magnitude and phase networks." IEEE Transactions on Biomedical Engineering 65.9 (2018): 1985-1995. doi: 10.1109/TBME.2018.2821699 [doi]
9. Knoll, Florian, et al. "fastMRI: A publicly available raw k-space and DICOM dataset of knee images for accelerated MR image reconstruction using machine learning." Radiology: Artificial Intelligence 2.1 (2020): e190007. PMID: 32076662 [pmid]
10. Fessler, Jeffrey A. "Optimization methods for magnetic resonance image reconstruction: Key models and optimization algorithms." IEEE Signal Processing Magazine 37.1 (2020): 33-40. PMID: 32317844 [pmid]
11. Kreutz-Delgado K. The complex gradient operator and the CR-calculus. arXiv preprint arXiv:0906.4835. 2009 Jun 26. doi: 10.48550/arXiv.0906.4835 [doi]
12. Yaman, Burhaneddin, et al. "Self‐supervised learning of physics‐guided reconstruction neural networks without fully sampled reference data." Magnetic Resonance in Medicine 84.6 (2020): 3172-3191. doi:10.1002/mrm.28378 [doi]

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/430-03-005