Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
360-02-010 ISMRM Abstract

Training deep learning based dynamic MR image reconstruction using synthetic fractals

Primary: Acquisition & Reconstruction - Image Reconstruction: AI
Secondary: Acquisition & Reconstruction - Open source software, sequences, and reconstruction algorithms
360-02-010 · Machine Learning for Image Reconstruction · Monday, 11 May, 9:15 AM–10:10 AM · Digital Posters Row A
Keywords: AI/ML Image Reconstruction Real-time MRI Data Scarcity Non-Cartesian MRI Cardiac cine MRI reconstruction
Accepted
Anirudh Raman 1, Olivier Jaubert, Daniel Knight1, Jennifer Steeden1,2, Vivek Muthurangu1
1University College London, London, United Kingdom
2Center for Cardiovascular Imaging, United Kingdom
Presenting Author: Anirudh Raman

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. J. Montalt-Tordera, V. Muthurangu, A. Hauptmann, and J. A. Steeden, “Machine learning in Magnetic Resonance Imaging: Image reconstruction,” Mar. 01, 2021, Associazione Italiana di Fisica Medica. doi: 10.1016/j.ejmp.2021.02.020. [doi]
2. O. Jaubert et al., “Training deep learning based dynamic MR image reconstruction using open-source natural videos,” Sci Rep, vol. 14, no. 1, Dec. 2024, doi: 10.1038/s41598-024-62294-7. [doi]
3. O. Ronneberger, P. Fischer, and T. Brox, “U-Net: Convolutional Networks for Biomedical Image Segmentation,” arXiv e-prints, p. arXiv:1505.04597, May 2015, doi: 10.48550/arXiv.1505.04597. [doi]
4. Jaubert O, Montalt-Tordera J, Knight D, Arridge S, Steeden J, Muthurangu V. HyperSLICE: HyperBand optimized spiral for low-latency interactive cardiac examination. Magn Reson Med. 2024 Jan;91(1):266-279. doi: 10.1002/mrm.29855. Epub 2023 Oct 6. PMID: 37799087; PMCID: PMC10953456. [doi] [pmid]
5. A. Sriram et al., “End-to-End Variational Networks for Accelerated MRI Reconstruction,” arXiv e-prints, p. arXiv:2004.06688, Apr. 2020, doi: 10.48550/arXiv.2004.06688. [doi]
6. J. I. Hamilton, W. Truesdell, M. Galizia, N. Burris, P. Agarwal, and N. Seiberlich, “A low-rank deep image prior reconstruction for free-breathing ungated spiral functional CMR at 0.55 T and 1.5 T,” Magnetic Resonance Materials in Physics, Biology and Medicine, vol. 36, no. 3, pp. 451–464, Jul. 2023, doi: 10.1007/s10334-023-01088-w. [doi]

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/360-02-010