Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
561-01-009 ISMRM Abstract

Unsupervised Image Harmonization of Multi-Parametric Maps of the Brain

Primary: Analysis Methods - Image Synthesis and Translation
Secondary: Neuro
561-01-009 · Generative Models for Image Synthesis · Wednesday, 13 May, 8:20 AM–9:15 AM · Digital Posters Row B
Keywords: Neuro Deep learning Harmonization Quantitative Imanging Multi-Parameter-Mapping
Accepted
Ann P Laube 1,2,3, Christian Stehning4, Joachim E Weber3,5,6,7, Matthias Endres3,5,8,9,10, Katharina Schönrath6, Ira Rohrpasser-Napierkowski6, Tobias Leutritz11, Nikolaus Weiskopf11,12,13, Jeanette Schulz-Menger3,14,15, Kersten Villringer5, Anja Hennemuth1,2,3,16,17,18
1Institute of Computer-assisted Cardiovascular Medicine, Deutsches Herzzentrum der Charité (DHZC), Berlin, Germany
2Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
3Partner Site Berlin, DZHK (German Centre for Cardiovascular Research), Berlin, Germany
4Philips Clinical Science DACH, Hamburg, Germany
5Center for Stroke Research Berlin (CSB), Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
6Berlin Institute of Health at Charité – Universitätsmedizin Berlin, Berlin, Germany
7Klinik und Hochschulambulanz für Neurologie, Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
8Department of Neurology and Experimental Neurology, Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
9Partner Site Berlin, German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany
10Excellence Cluster NeuroCure, Berlin, Germany
11Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
12Felix Bloch Institute for Solid State Physics, Faculty of Physics and Earth System Sciences, Leipzig University, Leipzig, Germany
13Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
14ECRC Experimental and Clinical Research Center, Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
15Department of Cardiology and Nephrology, Helios Klinikum Berlin Buch, Berlin, Germany
16Fraunhofer Institute for Digital Medicine MEVIS, Bremen, Germany
17Department of Diagnostic and Interventional Radiology and Nuclear Medicine, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
18on behalf of the BeLOVE Study Group, Germany
Presenting Author: Ann P Laube

Synopsis

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References

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2. Weber JE, Ahmadi M, Boldt LH, et al. Protocol of the Berlin Long-Term Observation of Vascular Events (BeLOVE): A prospective cohort study with deep phenotyping and long-Term follow up of cardiovascular high-risk patients. BMJ Open. 2023;13(10). doi:10.1136/bmjopen-2023-076415. [doi]
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4. Zuo L, Dewey BE, Liu Y, et al. Unsupervised MR harmonization by learning disentangled representations using information bottleneck theory. NeuroImage. 2021;243. doi:10.1016/j.neuroimage.2021.118569. [doi]
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6. Leutritz T, Seif M, Helms G, et al. Multiparameter mapping of relaxation (R1, R2*), proton density and magnetization transfer saturation at 3 T: A multicenter dual-vendor reproducibility and repeatability study. Human Brain Mapping. 2020;41(15):4232-4247. doi:10.1002/hbm.25122. [doi]
7. Isensee F, Schell M, Pflueger I, et al. Automated brain extraction of multisequence MRI using artificial neural networks. Human Brain Mapping. 2019;40(17):4952-4964. doi:10.1002/hbm.24750. [doi]
8. Isensee F, Jaeger PF, Kohl SAA, et al. nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nature Methods. 2021;18(2):203-211. doi:10.1038/s41592-020-01008-z. [doi]
9. Marcus DS, Wang TH, Parker J, et al. Open Access Series of Imaging Studies (OASIS): Cross-sectional MRI Data in Young, Middle Aged, Nondemented, and Demented Older Adults. J Cogn Neurosci. 2007;19(9):1498–1507. doi:10.1162/jocn.2007.19.9.1498. [doi]

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http://echo.ismrm.org/p/ISMRM2026/561-01-009