Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
364-04-015 ISMRM Abstract

Weakly Supervised Deep Learning for Non-Cartesian 7T MP2RAGE MRI Reconstruction

Primary: Acquisition & Reconstruction - Image Reconstruction: AI
Secondary: Acquisition & Reconstruction - AI methods
364-04-015 · Image Reconstruction Using Generative Models · Monday, 11 May, 2:45 PM–3:40 PM · Digital Posters Row E
Keywords: AI/ML Image Reconstruction MP2RAGE Non-Cartesian MRI Deep unrolling network
Accepted
Asma Tanabene 1,2,3, Chaithya Giliyar Radhakrishna1,3, Aurélien Massire2, Mahmoud Mostapha4, Mariappan Nadar4, Philippe Ciuciu1,3
1MIND, Inria, Palaiseau, France
2Siemens Healthcare SAS, Courbevoie, France
3Neurospin, CEA Paris Saclay, France
4Digital Technology and Innovation, Siemens Healthineers, Princeton, United States of America
Presenting Author: Asma Tanabene

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. Lustig, Michael, David Donoho, and John M. Pauly. "Sparse MRI: The application of compressed sensing for rapid MR imaging." Magnetic Resonance in Medicine: An Official Journal of the International Society for Magnetic Resonance in Medicine 58.6 (2007): 1182-1195.
2. Wright, Katherine L., et al. "Non‐Cartesian parallel imaging reconstruction." Journal of Magnetic Resonance Imaging 40.5 (2014): 1022-1040.
3. Ramzi, Zaccharie, et al. "NC-PDNet: A density-compensated unrolled network for 2D and 3D non-Cartesian MRI reconstruction." IEEE Transactions on Medical Imaging 41.7 (2022): 1625-1638.
4. Marques, José P., et al. "MP2RAGE, a self bias-field corrected sequence for improved segmentation and T1-mapping at high field." Neuroimage 49.2 (2010): 1271-1281.
5. Bertrait, Maxime, Chaithya Giliyar Radhakrishna, and Philippe Ciuciu. "gGRAPPA: A Flexible, GPU-Accelerated Python Package for Fast and Efficient generalized GRAPPA Reconstruction." (2025).
6. Giliyar Radhakrishna, Chaithya, et al. "Improving spreading projection algorithm for rapid k‐space sampling trajectories through minimized off‐resonance effects and gridding of low frequencies." Magnetic Resonance in Medicine 90.3 (2023): 1069-1085.
7. Radhakrishna, Chaithya Giliyar, et al. "Bringing GRAPPA to non-Cartesian MRI through SPARKLING: An application to MPRAGE anatomical MRI." (2025).
8. El Gueddari, Loubna, et al. "Calibration-less multi-coil compressed sensing magnetic resonance image reconstruction based on OSCAR regularization." Journal of Imaging 7.3 (2021): 58.
9. Comby, Pierre-Antoine, et al. "MRI-NUFFT: An open source Python package to make non-Cartesian MR Imaging easier." 2024 ISMRM & ISMRT Annual Meeting (ISMRM, 2024). doi. Vol. 10. 2024.

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/364-04-015