Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
606-03-005 ISMRM Abstract

Generalization of low-field 3D MRI acceleration via the CIRIM network across knee, spine and brain

Primary: Physics & Engineering - Low-Field MRI
Secondary: Acquisition & Reconstruction - Image Reconstruction: AI
606-03-005 · Low Field MRI Applications · Thursday, 14 May, 4:00 PM–5:50 PM · Auditorium 2
Keywords: Low-Field MRI Deep learning reconstruction AI-Accelerated MRI Spine Knee imaging
Accepted
Desirée M van den Berg 1, Rosario Varriale2, Fabrizio Ferrando2, Paolo Traverso2, Luca Balbi2, Rita Pasini2, Giacomo Belgiorno2, Stefano Zampini2, Matthan W Caan3, Gustav J Strijkers1
1Biomedical Engineering & Physics, Amsterdam University Medical Center, University of Amsterdam, Amsterdam, Netherlands
2Department of MRI R&D, Esaote S.p.A, Genua, Italy
3Biomedical Engineering and Physics, Amsterdam University Medical Center, University of Amsterdam, Amsterdam, Netherlands
Presenting Author: Desirée M van den Berg

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References

1. Thomas Campbell Arnold, Colbey W Freeman, Brian Litt, and Joel M Stein. Low-field mri: clinical promise and challenges. Journal of Magnetic Resonance Imaging 57(1):25–44, 2023.
2. Dimitrios Karkalousos, S Noteboom, HE Hulst, Franciscus M Vos, and Matthan WA Caan. Assessment of data consistency through cascades of independently recurrent inference machines for fast and robust accelerated mri reconstruction. Physics in Medicine & Biology, 67(12):124001, 2022.
3. 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.
4. Shimron, E., Shan, S., Grover, J., Koonjoo, N., Shen, S., Boele, T., ... & Waddington, D. E. (2024). Accelerating low-field mri: Compressed sensing and ai for fast noise-robust imaging. arXiv preprint arXiv:2411.06704.
5. Lyu, M., Mei, L., Huang, S., Liu, S., Li, Y., Yang, K., ... & Wu, E. X. (2023). M4Raw: A multi-contrast, multi-repetition, multi-channel MRI k-space dataset for low-field MRI research. Scientific Data, 10(1), 264.
6. Liebrand, L. C., Karkalousos, D., Poirion, É., Emmer, B. J., Roosendaal, S. D., Marquering, H. A., ... & Caan, M. W. (2025). Deep learning for efficient reconstruction of highly accelerated 3D FLAIR MRI in neurological deficits. Magnetic Resonance Materials in Physics, Biology and Medicine, 38(1), 1-12.
7. Ghodrati, V., Shao, J., Bydder, M., Zhou, Z., Yin, W., Nguyen, K. L., ... & Hu, P. (2019). MR image reconstruction using deep learning: evaluation of network structure and loss functions. Quantitative imaging in medicine and surgery, 9(9), 1516.
8. van den Berg, D., Varriale, R., Ferrando, F., Traverso, P., Balbi, L., Strijkers, G., & Caan, M. Up to four times accelerated musculoskeletal MRI at 0.4 T using the CIRIM-network.

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/606-03-005