Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
664-04-002 ISMRM Abstract

Accelerated Ultra-Low-Field MRI via Zero-Shot Self-Supervised Learning Reconstruction

Accepted
Mart WJ van Straten 1,2, Beatrice Lena1, Chloe Najac1, Ruben B van den Broek1, Peter Börnert1,3, Andrew Webb1, Yiming Dong1
1C.J. Gorter MRI Center, Department of Radiology, Leiden University Medical Center, Leiden, Netherlands
2Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands
3Philips Innovative Technologies, Hamburg, Germany
Presenting Author: Mart WJ van Straten

Synopsis

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References

1. Y. Zhao et al. Whole-body magnetic resonance imaging at 0.05 Tesla. In: Science 384 (2024), eadm7168. doi:10.1126/science.adm7168 [doi]
2. T. O’Reilly, W.M. Teeuwisse, D. de Gans, K. Koolstra, and A.G. Webb. In vivo 3D brain and extremity MRI at 50 mT using a permanent magnet Halbach array. In: Magnetic Resonance in Medicine 85.1 (2021), pp. 495–505. doi:10.1002/mrm.28396 [doi]
3. W.T. Kimberly, A.J. Sorby-Adams, A.G. Webb et al. Brain imaging with portable low-field MRI. In: Nature Reviews Bioengineering 1.9 (2023). doi:10.1038/s44222-023-00100-1 [doi]
4. N. Koonjoo et al. Boosting the signal-to-noise of low-field MRI with deep learning image reconstruction. In: Scientific Reports 11 (2021), 8248. doi:10.1038/s41598-021-87482-7 [doi]
5. M. Lustig, D. Donoho, and J.M. Pauly. Sparse MRI: The application of compressed sensing for rapid MR imaging. In: Magnetic Resonance in Medicine 58 (2007), pp. 1182–1195. doi:10.1002/mrm.21391 [doi]
6. B. Yaman et al. Zero-shot self-supervised learning for MRI reconstruction. In: Proceedings of the International Conference on Learning Representations (2021). doi:10.48550/arXiv.2102.07737 [doi]
7. H.K. Aggarwal, M.P. Mani, and M. Jacob. MoDL: Model-Based Deep Learning Architecture for Inverse Problems. In: IEEE Transactions on Medical Imaging 38.2 (2019), pp. 394–405. doi:10.1109/TMI.2018.2865356 [doi]
8. B. Yaman et al. Self-supervised learning of physics-guided reconstruction neural networks without fully sampled reference data. In: Magnetic Resonance in Medicine 84 (2020), pp. 3172–3191. doi:10.1002/mrm.28378 [doi]
9. A. Vaswani et al. Attention is all you need. In: Advances in Neural Information Processing Systems (2017), pp. 5998–6008. doi: 10.48550/arXiv.1706.03762 [doi]
10. C. Najac, R. van den Broek, T. O'Reilly, A.G. Webb, B. Lena. Evaluating repeatability of In-Vivo imaging in multiple locations using a portable Halbach-based 46 mT scanner. ISMRM; Honolulu2025.

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