Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
664-04-001 ISMRM Abstract

Complex-conjugate artefacts in real-valued scan-specific neural networks for deep learning MRI Reconstruction

Primary: Acquisition & Reconstruction - AI methods
Secondary: Acquisition & Reconstruction - Artifacts and Correction Strategies
664-04-001 · Image Reconstruction: Self-Supervised and Implicit · Thursday, 14 May, 2:35 PM–3:30 PM · Digital Posters Row E
Keywords: Artifacts correction Self-supervised learning K-space reconstruction Deep learning-based reconstruction (DLR) Complex-valued neural network
Accepted
Wassim Ben Salah 1,2, Sarah McElroy3, Antoine Naegel4, Sebastien Ourselin2, Jonathan Shapey2,5, Christos Bergeles2, Radhouene Neji1,2
1Imaging Physics and Engineering Department, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
2Surgical & Interventional Engineering Research Department, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
3MR Research Collaborations, Siemens Healthcare Limited, Camberley, United Kingdom
4Siemens Healthcare SAS, Courbevoie, France
5Department of Neurosurgery, King's College Hospital NHS Foundation Trust London, London, United Kingdom
Presenting Author: Wassim Ben Salah

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References

1. Griswold, Mark A et al. “Generalized autocalibrating partially parallel acquisitions (GRAPPA).” Magnetic resonance in medicine vol. 47,6 (2002):1202-10. doi:10.1002/mrm.10171 [doi]
2. Hammernik, K., Klatzer, T., Kobler, E., Recht, M.P., Sodickson, D.K., Pock, T. and Knoll, F., 2018. Learning a variational network for reconstruction of accelerated MRI data. Magnetic resonance in medicine, 79(6), pp.3055-3071.
3. Akçakaya, Mehmet et al. “Scan-specific robust artificial-neural-networks for k-space interpolation (RAKI) reconstruction: Database-free deeplearning for fast imaging.” Magnetic resonance in medicine vol. 81,1 (2019): 439-453. doi:10.1002/mrm.27420 [doi]
4. Cole, E., Cheng, J., Pauly, J. and Vasanawala, S., 2021. Analysis of deep complex‐valued convolutional neural networks for MRI reconstruction and phase‐focused applications. Magnetic resonance in medicine, 86(2), pp.1093-1109.
5. Virtue, P.M., 2019. Complex-valued deep learning with applications to magnetic resonance image synthesis. University of California, Berkeley.
6. Daval-Frerot, G., Chen, X., Arberet, S., Mailhé, B. and Nadar, M., 2019. Exploring complex-valued neural networks with trainable activation functions for magnetic resonance imaging. In Int. Soc. Magn. Reson. Med.

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/664-04-001