Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
660-04-004 ISMRM Abstract

Model-Based Deep Learning MRI Reconstruction: Adversarial Robustness vs. Network Capacity

Primary: Acquisition & Reconstruction - Image Reconstruction: AI
Secondary: Acquisition & Reconstruction - AI methods
660-04-004 · AI: Anything Synthetic or Correcting Artifacts · Thursday, 14 May, 2:35 PM–3:30 PM · Digital Posters Row A
Keywords: Image Reconstruction Deep learning Adversarial attacks Adversarial Robustness Adversarial stability
Accepted
Bill A Bernhardt1, Christoph Kolbitsch 1, Andreas Kofler1
1Physikalisch Technische Bundesanstalt (PTB), Braunschweig and Berlin, Germany
Presenting Author: Christoph Kolbitsch

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. Heckel, R. et al. "Deep learning for accelerated and robust MRI reconstruction". Magn Reson Mater Phy 37, 335–368 (2024). https://doi.org/10.1007/s10334-024-01173-8 [doi]
2. Hossain, M. B. et al. “A Systematic Review and Identification of the Challenges of Deep Learning Techniques for Undersampled Magnetic Reson ance Image Reconstruction”. In: Sensors 24.3 (2024). doi:10.3390/s24030753 [doi]
3. Kofler, A. et al. "The More the Merrier? – On the Number of Trainable Parameters in Iterative Neural Networks for Image Reconstruction". Proc. Intl. Soc. Mag. Reson. Med. 30 (2022) 0051. https://doi.org/10.58530/2022/0051 [doi]
4. Antun, V. et al. “On instabilities of deep learning in image reconstruction and the potential costs of AI”. In: Proceedings of the National Academy of Sciences 117.48 (2020), pp. 30088–30095. doi:10.1073/pnas.1907377117 [doi]
5. Genzel, M. et al. "Solving Inverse Problems With Deep Neural Networks – Robustness Included?". In: IEEE Transactions on Pattern Analysis and Machine Intelligence 45.1 (2022), pp. 1119-1134. doi: 10.1109/TPAMI.2022.3148324 [doi]
6. Kofler, A. et al. "Neural networks-based regularization for large-scale medical image reconstruction". Phys. Med. Biol. 65 135003 (2020). doi:10.1088/1361-6560/ab990e [doi]
7. Ronneberger, O. et al. "U-net: Convolutional networks for biomedical image segmentation". (2015). https://doi.org/10.48550/arXiv.1505.04597 [doi]
8. Aggarwal, H. K. et al. "MoDL: Model-Based Deep Learning Architecture for Inverse Problems". IEEE Trans Med Imaging. 38.2 (2019), pp. 394-405. doi:10.1109/TMI.2018.2865356 [doi]
9. Kingma, D. P. et al. "Adam: A method for stochastic optimization". (2017). https://doi.org/10.48550/arXiv.1412.6980 [doi]
10. Knoll, F. et al. "fastMRI: A Publicly Available Raw k-Space and DICOM Dataset of Knee Images for Accelerated MR Image Reconstruction Using Machine Learning". Radiol Artif Intell. 2.1 (2020). doi:10.1148/ryai.2020190007 [doi]
11. Hammernik, K. et al. "Learning a variational network for reconstruction of accelerated MRI data". Magn Reson Med. 79.6 (2018), pp. 3055-3071. doi:10.1002/mrm.26977 [doi]
12. Sriram, A. et al. "End-to-End Variational Networks for Accelerated MRI Reconstruction". In: Medical Image Computing and Computer Assisted Intervention – MICCAI 2020. Lecture Notes in Computer Science 12262. (2020). https://doi.org/10.1007/978-3-030-59713-9_7 [doi]

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/660-04-004