Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
507-03-008 ISMRM Abstract

Quantitative 7 T CEST Imaging of Metabolic Integrity in Multiple Sclerosis: Aging and Lesion effects

Primary: Contrast Mechanisms - CEST / APT / NOE
Secondary: Neuro - Multiple Sclerosis
507-03-008 · New Frontiers in CEST MRI · Wednesday, 13 May, 1:40 PM–3:30 PM · Meeting Room 1.40
Keywords: Data Analysis Neurodegeneration Biomarkers High-Field MRI Quantitative Imaging
Accepted
Sebastián Navarrete1, Milena Capiglioni 2,3, Stefanie Marti4, Lukas Pirpamer3, Richard McKinley3, Robert Hoepner4, León A Betancourt, Piotr Radojewski2,3
1Faculty of Engineering, Universidad de Concepción, Concepción, Chile
2Translational Imaging Center (TIC), Sitem-Insel, Bern, Switzerland
3Institute for Diagnostic and Interventional Neuroradiology, Support Center for Advanced Neuroimaging, University of Bern, Bern, Switzerland
4Department of Neurology, Inselspital, Bern University Hospital and University of Bern, Bern, Switzerland
Presenting Author: Milena Capiglioni

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References

1. Mennecke, A. et al. (2023). 7 tricks for 7 T CEST: Improving the reproducibility of multipool evaluation provides insights into the effects of age and the early stages of Parkinson’s disease. NMR in Biomedicine, 36(6), e4717. https://doi.org/10.1002/nbm.4717. [doi]
2. Jacobs, P. S. et al. (2024). Diffuse nuclear Overhauser effect MRI contrast changes detected in multiple sclerosis subjects at 7T. Brain Communications, 7(1). https://doi.org/10.1093/braincomms/fcaf043 [doi]
3. Zaiss, M., Ehses, P., & Scheffler, K. (2018). Snapshot-CEST: Optimizing spiral-centric-reordered gradient echo acquisition for fast and robust 3D CEST MRI at 9.4 T. NMR in Biomedicine, 31(4), e3879. https://doi.org/10.1002/nbm.3879 [doi]
4. Rajput, J. R. et al. (2023). Physics-informed conditional autoencoder approach for robust metabolic CEST MRI at 7T. In H. Greenspan et al. (Eds.), Medical image computing and computer assisted intervention – MICCAI 2023 (Lecture Notes in Computer Science, Vol. 14227, pp. 454-464). Springer. https://doi.org/10.1007/978-3-031-43993-3_44 [doi]
5. Penny, W. D., Friston, K. J., Ashburner, J. T., Kiebel, S. J., & Nichols, T. E.. (2011). Statistical parametric mapping: the analysis of functional brain images. Elsevier.
6. McKinley, R. et al. (2020). Automatic detection of lesion load change in Multiple Sclerosis using convolutional neural networks with segmentation confidence. NeuroImage: Clinical, 25, 102104. https://doi.org/10.1016/j.nicl.2019.102104 [doi]
7. Dereskewicz, E. et al. (2025). FLAMeS: A Robust Deep Learning Model for Automated Multiple Sclerosis Lesion Segmentation (p. 2025.05.19.25327707). medRxiv. https://doi.org/10.1101/2025.05.19.25327707 [doi]

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http://echo.ismrm.org/p/ISMRM2026/507-03-008