Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
568-06-015 ISMRM Abstract

From Simulations to Actual Data - Generalizability and Robustness of Learned Image Reconstruction for Portable Low-Field MRI

Primary: Acquisition & Reconstruction - AI methods
Secondary: Physics & Engineering - Low-Field MRI
568-06-015 · Image Reconstruction II · Wednesday, 13 May, 4:55 PM–5:50 PM · Digital Posters Row I
Keywords: Image Reconstruction Ultra-Low-Field MRI Portable MRI Neural Network AI
Accepted
David Schote 1, Helge Herthum2, Christoph Kolbitsch1, Luca Calatroni3,4, Kostas Papafitsoros5, Andreas Kofler1
1Physikalisch-Technische Bundesanstalt (PTB), Braunschweig and Berlin, Berlin, Germany
2Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
3MaLGa Center, DIBRIS, Universit‘a di Genova, Italy
4MMS, Isti- tuto Italiano di Tecnologia, Genoa, Italy
5School of Mathematical Sciences, Queen Mary University of London, London, United Kingdom
Presenting Author: David Schote

Synopsis

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References

1. Jure Zbontar, Florian Knoll, Anuroop Sriram, Tullie Murrell, Zhengnan Huang, Matthew J Muckley, Aaron Defazio, Ruben Stern, Patricia Johnson, Mary Bruno, et al. fastmri: An open dataset and benchmarks for accelerated mri. arXiv preprint arXiv:1811.08839, 2018.
2. Mohammad Zalbagi Darestani, Jiayu Liu, and Reinhard Heckel. Test-time training can close the natural distribution shift performance gap in deep learning based compressed sensing. In International Conference on Machine Learning, pages 4754–4776. PMLR, 2022.
3. Lukas Winter. Osi2 community. osi2 one mr scanner. open source imaging. https://www.opensourceimaging.org/project/osii-one/ [Accessed on: 2025-07-17], 2025.
4. David Schote, Berk Silemek, Thomas O’Reilly, Frank Seifert, Jan-Lukas Assmy, Christoph Kolbitsch, Andrew G Webb, and Lukas Winter. Nexus: A versatile console for advanced low-field mri. Magnetic Resonance in Medicine, 93(5):2224–2238, 2025.
5. Hemant K. Aggarwal, Merry P. Mani, and Mathews Jacob. MoDL: Model-based deep learning architecture for inverse problems. IEEE Transactions on Medical Imaging, 38(2):394–405, 2018.
6. Andreas Kofler, Fabian Altekrüger, Fatima Antarou Ba, Christoph Kolbitsch, Evangelos Papoutsellis, David Schote, Clemens Sirotenko, Felix Frederik Zimmermann, and Kostas Papafitsoros. Learning regularization parameter-maps for variational image reconstruction using deep neural networks and algorithm unrolling. SIAM Journal on Imaging Sciences, 16(4):2202–2246, 2023.
7. Andreas Kofler, Luca Calatroni, Christoph Kolbitsch, and Kostas Papafitsoros. Learning spatially adaptive L1-norms weights for convolutional synthesis regularization. Proceedings of European Signal Processing Conference (EUSIPCO) 2025. arxiv preprint arXiv:2503.09483, 2025.
8. David O Walsh, Arthur F Gmitro, and Michael W Marcellin. Adaptivereconstruction of phased array mr imagery. Magnetic Resonance in Medicine: An Official Journal of the International Society for Magnetic Resonance in Medicine, 43(5):682–690, 2000.
9. Felix Frederik Zimmermann, Patrick Schuenke, Sherine Brahma, Mara Guastini, Johannes Hammacher, Andreas Kofler, Catarina Kranich Redshaw, Leonid Lunin, Stefan Martin, David Schote, and Christoph Kolbitsch. MRpro - PyTorch-based MR image reconstruction and processing package, February 2025.
10. Frederique Crete, Thierry Dolmiere, Patricia Ladret, and Marina Nicolas. The blur effect: perception and estimation with a new no-reference perceptual blur metric. In Human vision and electronic imaging XII, volume 6492, pages 196–206. SPIE, 2007.

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/568-06-015