Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
560-01-014 ISMRM Abstract

Contrast Enhancement in Susceptibility-Weighted Imaging using a 3D Wasserstein-GAN with conditional Multi-Echo Input

Primary: Contrast Mechanisms - Susceptibility/QSM
Secondary: Analysis Methods - Image Synthesis and Translation
560-01-014 · New Developments in QSM I · Wednesday, 13 May, 8:20 AM–9:15 AM · Digital Posters Row A
Keywords: Multiple Sclerosis SWI Contrast-enhancement Generative adversarial network Generative AI
Accepted
Daniel Eichleitner 1,2,3, Guenther Grabner1, Sutatip Pittayapong1,4, Christian Menard1, Wolfgang Bogner2,5, Georg Langs2,3, Assunta Dal-Bianco6,7, Beata Bachrata1
1Department of Medical Engineering, Carinthia University of Applied Sciences, Klagenfurt, Austria
2Medical University of Vienna, Vienna, Austria
3Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Vienna, Austria
4Division of Neuropathology and Neurochemistry, Department of Neurology, Medical University of Vienna, Vienna, Austria
5Christian Doppler Laboratory for MR Imaging Biomarkers (BIOMAK), Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria
6Department of Neurology, Medical University of Vienna, Vienna, Austria
7Comprehensive Center for Clinical Neurosciences and Mental Health, Medical University of Vienna, Vienna, Austria
Presenting Author: Daniel Eichleitner

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References

1. Haacke, E. Mark; Reichenbach, Jürgen R. (2011): Susceptibility Weighted Imaging in MRI: Wiley. DOI: https://doi.org/10.1002/9780470905203 [doi]
2. Ladd, Mark E.; Quick, Harald H.; Bock, Michael; Berger, Moritz; Breithaupt, Mathies; Nagel, Armin M. et al. (2018): Magnetresonanztomographie und -spektroskopie. In: Wolfgang Schlegel, Christian P. Karger und Oliver Jäkel (Hg.): Medizinische Physik. Berlin, Heidelberg: Springer Berlin Heidelberg, S. 205–283. DOI: https://doi.org/10.1007/978-3-662-54801-1_9 [doi]
3. Müller-Franzes, Gustav; Huck, Luisa; Tayebi Arasteh, Soroosh; Khader, Firas; Han, Tianyu; Schulz, Volkmar et al. (2023): Using Machine Learning to Reduce the Need for Contrast Agents in Breast MRI through Synthetic Images. In: Radiology 307 (3), e222211. DOI: https://doi.org/10.1148/radiol.222211 [doi]
4. Genc, Ozan; Morrison, Melanie A.; Villanueva-Meyer, Javier E.; Burns, Brian; Hess, Christopher P.; Banerjee, Suchandrima; Lupo, Janine M. (2023): DeepSWI: Using Deep Learning to Enhance Susceptibility Contrast on T2*-Weighted MRI. In: Journal of magnetic resonance imaging : JMRI 58 (4), S. 1200–1210. DOI: https://doi.org/10.1002/jmri.28622 [doi]
5. Zhang, Dong; Duan, Caohui; Anazodo, Udunna; Wang, Z. Jane; Lou, Xin (2024): Self-supervised anatomical continuity enhancement network for 7T SWI synthesis from 3T SWI. In: Medical image analysis 95, S. 103184. DOI: https://doi.org/10.1016/j.media.2024.103184 [doi]
6. Li, Sui; Deng, Xingguang; Li, Qiwei; Zhen, Zhiming; Han, Luyi; Chen, Kang et al. (2025): Synthetizing SWI from 3T to 7T by generative diffusion network for deep medullary veins visualization. In: NeuroImage 320, S. 121475. DOI: https://doi.org/10.1016/j.neuroimage.2025.121475 [doi]
7. Eckstein, Korbinian; Bachrata, Beata; Hangel, Gilbert; Widhalm, Georg; Enzinger, Christian; Barth, Markus et al. (2021): Improved susceptibility weighted imaging at ultra-high field using bipolar multi-echo acquisition and optimized image processing: CLEAR-SWI. In: NeuroImage 237, S. 118175. DOI:https://doi.org/10.1016/j.neuroimage.2021.118175 [doi]
8. Wang, Zhou; Bovik, Alan Conrad; Sheikh, Hamid Rahim; Simoncelli, Eero P. (2004): Image quality assessment: from error visibility to structural similarity. In: IEEE transactions on image processing : a publication of the IEEE Signal Processing Society 13 (4), S. 600–612. DOI: https://doi.org/10.1109/TIP.2003.819861 [doi]
9. Wang, Z.; Simoncelli, E. P.; Bovik, A. C. (2003): Multiscale structural similarity for image quality assessment. In: The Thrity-Seventh Asilomar Conference on Signals, Systems & Computers, 2003. Conference Record of the 37th Asilomar Conference on Signals, Systems and Computers. Pacific Grove, CA, USA, 9-12 Nov. 2003: IEEE, S. 1398–1402. DOI: htps://doi.org/t10.1109/ACSSC.2003.1292216
10. Zhang, Richard; Isola, Phillip; Efros, Alexei A.; Shechtman, Eli; Wang, Oliver (2018): The Unreasonable Effectiveness of Deep Features as a Perceptual Metric. DOI: htps://doi.org/10.1109/CVPR.2018.00068 [doi]

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/560-01-014