Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
303-03-001 ISMRM Abstract

Gadolinium-free MRI using artificial intelligence in glioma: a clinically-oriented benchmark study

Primary: Neuro - Tumors
Secondary: Analysis Methods - Image Synthesis and Translation
303-03-001 · Advanced Tumor Imaging: Bridging Research and Clinical Practice · Monday, 11 May, 4:10 PM–6:00 PM · Auditorium 1
Keywords: Segmentation Clinical application Brain Tumor Segmentation Image synthesis Gadolinium-Free
Accepted
Ivar Wamelink 1,2,3, Rajeev A Essed1,2,3, Stefan de Vries1,3, Ellis Donders1,3,4, Frederik Barkhof3,5,6, Alle Meije Wink1,3, Vera C Keil1,2,3, Stefano Trebeschi1,7,8
1Radiology and Nuclear Medicine, Amsterdam University Medical Center, University of Amsterdam, Amsterdam, Netherlands
2Imaging and Biomarkers, Cancer Center Amsterdam, Amsterdam, Netherlands
3Brain Imaging, Amsterdam Neuroscience, Amsterdam, Netherlands
4MS Center Amsterdam, Amsterdam, Netherlands
5Department of Radiology & Nuclear Medicine, Amsterdam University Medical Center, University of Amsterdam, Amsterdam, Netherlands
6Queen Square Institute of Neurology and Centre for Medical Image Computing, University College London, London, United Kingdom
7GROW Research Institute for Oncology and Reproduction, Maastricht University, Maastricht, Netherlands
8Radiology, the Netherlands Cancer Institute, Amsterdam, Netherlands
Presenting Author: Ivar Wamelink

Synopsis

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References

1. Wamelink IJHG, Azizova A, Booth TC, Mutsaerts HJMM, Ogunleye A, Mankad K, et al. Brain tumor imaging without gadolinium-based contrast agents: Feasible or fantasy? Radiology. 2024;310: e230793. doi:10.1148/radiol.230793 [doi]
2. Morana G, Cugini C, Scatto G, Zanato R, Fusaro M, Dorigo A. Use of contrast agents in oncological imaging: magnetic resonance imaging. Cancer Imaging. 2013;13: 350–359. doi:10.1102/1470-7330.2013.9018 [doi]
3. Weller M, van den Bent M, Preusser M, Le Rhun E, Tonn JC, Minniti G, et al. EANO guidelines on the diagnosis and treatment of diffuse gliomas of adulthood. Nat Rev Clin Oncol. 2020;18: 170–186. doi:10.1038/s41571-020-00447-z [doi]
4. FDA Drug Safety Communication: FDA warns that gadolinium-based contrast agents (GBCAs) are retained in the body; requires new class warnings. 2018 [cited 1 Oct 2021]. Available: https://www.fda.gov/Drugs/DrugSafety/ucm589213.htm
5. Guo BJ, Yang ZL, Zhang LJ. Gadolinium deposition in brain: Current scientific evidence and future perspectives. Front Mol Neurosci. 2018;11: 335. doi:10.3389/fnmol.2018.00335 [doi]
6. Gulani V, Calamante F, Shellock FG, Kanal E, Reeder SB, International Society for Magnetic Resonance in Medicine. Gadolinium deposition in the brain: summary of evidence and recommendations. Lancet Neurol. 2017;16: 564–570. doi:10.1016/S1474-4422(17)30158-8 [doi]
7. Gadolinium-containing contrast agents - referral. In: European Medicines Agency (EMA) [Internet]. 18 Mar 2016 [cited 16 May 2025]. Available: https://www.ema.europa.eu/en/medicines/human/referrals/gadolinium-containing-contrast-agents#:~:text=Gadolinium%20contrast%20agents%20are%20given,small%20amounts%20cause%20any%20harm
8. MRI Contrast Media Agents Market Size, Share & Trends Analysis Report By Product (Paramagnetic Agents, Superparamagnetic Agents), By Application, By End-use, By Type, By Region, And Segment Forecasts, 2023 - 2030. In: Grand View Research [Internet]. Available: https://www.grandviewresearch.com/industry-analysis/mri-contrast-media-agents-market-report
9. Krohn LM, Klimpel F, Béziat P, Bau M. Impacts of COVID-19 and climate change on wastewater-derived substances in urban drinking water: Evidence from gadolinium-based contrast agents in tap water from Berlin, Germany. Water Res. 2024;259: 121847. doi:10.1016/j.watres.2024.121847 [doi]
10. Moya-Sáez E, de Luis-García R, Alberola-López C. Toward deep learning replacement of gadolinium in neuro-oncology: A review of contrast-enhanced synthetic MRI. Frontiers in Neuroimaging. 2023;2. doi:10.3389/fnimg.2023.1055463 [doi]
11. Pasquini L, Napolitano A, Pignatelli M, Tagliente E, Parrillo C, Nasta F, et al. Synthetic Post-Contrast Imaging through Artificial Intelligence: Clinical Applications of Virtual and Augmented Contrast Media. Pharmaceutics. 2022;14. doi:10.3390/pharmaceutics14112378 [doi]
12. Baid U, Ghodasara S, Mohan S, Bilello M, Calabrese E, Colak E, et al. The RSNA-ASNR-MICCAI BraTS 2021 benchmark on brain tumor segmentation and radiogenomic classification. arXiv [cs.CV]. 2021. Available: http://arxiv.org/abs/2107.02314
13. Menze BH, Jakab A, Bauer S, Kalpathy-Cramer J, Farahani K, Kirby J, et al. The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS). IEEE Trans Med Imaging. 2015;34: 1993–2024. doi:10.1109/TMI.2014.2377694 [doi]
14. Bakas S, Akbari H, Sotiras A, Bilello M, Rozycki M, Kirby JS, et al. Advancing The Cancer Genome Atlas glioma MRI collections with expert segmentation labels and radiomic features. Sci Data. 2017;4: 170117. doi:10.1038/sdata.2017.117 [doi]
15. Calabrese E, Villanueva-Meyer JE, Rudie JD, Rauschecker AM, Baid U, Bakas S, et al. The university of California San Francisco preoperative diffuse glioma MRI dataset. Radiol Artif Intell. 2022;4: e220058. doi:10.1148/ryai.220058 [doi]
16. van der Voort SR, Incekara F, Wijnenga MMJ, Kapsas G, Gahrmann R, Schouten JW, et al. The Erasmus Glioma Database (EGD): Structural MRI scans, WHO 2016 subtypes, and segmentations of 774 patients with glioma. Data Brief. 2021;37: 107191. doi:10.1016/j.dib.2021.107191 [doi]
17. Ivar J.H.G Wamelink, Alle Meije Wink, Niels Verburg, Roeland S Eijgelaar, Emmanouil Koltsakis, Marcus Cakmak, Maarten Balder, Henk J.M.M Mutsaerts, Philip Witt Hamer Frederik Barkhof, Vera C. Keil. IMAGO. Amsterdam Imaging and Clinical Glioma database. 2025. Available: https://catalogue.eucaim.cancerimage.eu/Eucaim/eucaim-ui/#/dataset/imago
18. Pemberton HG, Wu J, Kommers I, Müller DMJ, Hu Y, Goodkin O, et al. Multi-class glioma segmentation on real-world data with missing MRI sequences: comparison of three deep learning algorithms. Sci Rep. 2023;13: 18911. doi:10.1038/s41598-023-44794-0 [doi]
19. Isensee F, Jaeger PF, Kohl SAA, Petersen J, Maier-Hein KH. nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nat Methods. 2021;18: 203–211. doi:10.1038/s41592-020-01008-z [doi]
20. Azad R, Dehghanmanshadi M, Khosravi N, Cohen-Adad J, Merhof D. Addressing missing modality challenges in MRI images: A comprehensive review. Comput Vis Media (Beijing). 2025;11: 241–268. doi:10.26599/CVM.2025.9450399 [doi]
21. Wen PY, van den Bent M, Youssef G, Cloughesy TF, Ellingson BM, Weller M, et al. RANO 2.0: Update to the Response Assessment in Neuro-Oncology criteria for high- and low-grade gliomas in adults. J Clin Oncol. 2023;41: 5187–5199. doi:10.1200/JCO.23.01059 [doi]
22. Sharma A, Hamarneh G. Missing MRI pulse sequence synthesis using multi-modal generative adversarial network. arXiv [eess.IV]. 2019. Available: http://arxiv.org/abs/1904.12200
23. Osman AFI, Tamam NM. Contrast-enhanced MRI synthesis using dense-dilated residual convolutions based 3D network toward elimination of gadolinium in neuro-oncology. J Appl Clin Med Phys. 2023;24: e14120. doi:10.1002/acm2.14120 [doi]
24. Piening M, Altekrüger F, Steidl G, Hattingen E, Steidl E. Conditional generative models for contrast-enhanced synthesis of T1w and T1 maps in brain MRI. arXiv [eess.IV]. 2024. Available: http://arxiv.org/abs/2410.08894
25. Chen C, Raymond C, Speier W, Jin X, Cloughesy TF, Enzmann D, et al. Synthesizing MR image contrast enhancement using 3D high-resolution ConvNets. IEEE Trans Biomed Eng. 2023;70: 401–412. doi:10.1109/TBME.2022.3192309 [doi]
26. Ruffle JK, Mohinta S, Gray R, Hyare H, Nachev P. Brain tumour segmentation with incomplete imaging data. Brain Commun. 2023;5: fcad118. doi:10.1093/braincomms/fcad118 [doi]
27. Mehta R, Arbel T. RS-net: Regression-segmentation 3D CNN for synthesis of full resolution missing brain MRI in the presence of tumours. arXiv [cs.CV]. 2018. Available: http://arxiv.org/abs/1807.10972
28. Ding Y, Yu X, Yang Y. RFNet: Region-aware fusion network for incomplete multi-modal brain tumor segmentation. 2021 IEEE/CVF International Conference on Computer Vision (ICCV). IEEE; 2021. doi:10.1109/iccv48922.2021.00394 [doi]
29. Azad R, Khosravi N, Merhof D. SMU-Net: Style matching U-Net for brain tumor segmentation with missing modalities. arXiv [cs.CV]. 2022. Available: http://arxiv.org/abs/2204.02961
30. Feng X, Ghimire K, Kim DD, Chandra RS, Zhang H, Peng J, et al. Brain tumor segmentation for multi-modal MRI with missing information. J Digit Imaging. 2023;36: 2075–2087. doi:10.1007/s10278-023-00860-7 [doi]
31. Vadacchino S, Mehta R, Sepahvand NM, Nichyporuk B, Clark JJ, Arbel T. HAD-Net: A hierarchical adversarial knowledge distillation network for improved enhanced tumour segmentation without post-contrast images. arXiv [eess.IV]. 2021. Available: http://arxiv.org/abs/2103.16617
32. Jayachandran Preetha C, Meredig H, Brugnara G, Mahmutoglu MA, Foltyn M, Isensee F, et al. Deep-learning-based synthesis of post-contrast T1-weighted MRI for tumour response assessment in neuro-oncology: a multicentre, retrospective cohort study. Lancet Digit Health. 2021;3: e784–e794. doi:10.1016/S2589-7500(21)00205-3 [doi]
33. Kickingereder P, Isensee F, Tursunova I, Petersen J, Neuberger U, Bonekamp D, et al. Automated quantitative tumour response assessment of MRI in neuro-oncology with artificial neural networks: a multicentre, retrospective study. Lancet Oncol. 2019;20: 728–740. doi:10.1016/S1470-2045(19)30098-1 [doi]
34. Cohen JP, Luck M, Honari S. Distribution matching losses can hallucinate features in medical image translation. Medical Image Computing and Computer Assisted Intervention – MICCAI 2018. Cham: Springer International Publishing; 2018. pp. 529–536. doi:10.1007/978-3-030-00928-1_60 [doi]
35. Zhang R, Pfister T, Li J. Harmonic unpaired image-to-image translation. arXiv [cs.CV]. 2019. Available: http://arxiv.org/abs/1902.09727

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http://echo.ismrm.org/p/ISMRM2026/303-03-001