409-03-010 ISMRM Abstract

Mapping Personalized Thalamic Microstructural Pathology in Multiple Sclerosis Using Quantitative MRI-Based Normative Modeling

Primary: Neuro - Multiple Sclerosis

Secondary: Acquisition & Reconstruction - Quantitative Imaging: relaxometry, multi-parametric, MR Fingerprinting, and synthetic MRI

409-03-010 · Quantitative Imaging: Applications · Tuesday, 12 May, 1:40 PM–3:30 PM · Meeting Room 2.40

Keywords: Multiple Sclerosis Ageing Normative modeling Thalamus Quantitative Magnetic Resonance Imaging

Accepted

Xinjie Chen ^1,2,3,4,5, Mario Ocampo-Pineda^1,2,3, Po-Jui Lu^1,2,3, Alessandro Cagol^1,2,3,6, Matthias Weigel^1,2,3,7, Kwok-Shing Chan⁸, Marcel Zwiers⁵, Jens Kuhle^2,3, Ludwig Kappos^2,3, Lester Melie-Garcia^1,2,3, José P Marques⁵, Cristina Granziera^1,2,3

¹Translational Imaging in Neurology (ThINk) Basel, Department of Biomedical Engineering, Faculty of Medicine, University Hospital Basel and University of Basel, Basel, Switzerland

²Department of Neurology, University Hospital Basel and University of Basel, Basel, Switzerland., Switzerland

³Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Basel, Switzerland, Switzerland

⁴Department of Pediatric Neurology and Developmental Medicine, University Children’s Hospital Basel (UKBB), Switzerland

⁵Donders Centre for Cognitive Neuroimaging, Radboud University, Nijmegen, Netherlands

⁶Department of Health Sciences, University of Genova, Genova, Italy

⁷University Hospital Basel, Basel, Switzerland

⁸Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Charlestown, United States of America

Presenting Author: Xinjie Chen

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References

1. Granziera C, Wuerfel J, Barkhof F, et al. Quantitative magnetic resonance imaging towards clinical application in multiple sclerosis. Brain. 2021;144(5):1296-1311. doi:https://doi.org/10.1093/brain/awab029 [doi]

2. Chen X, Schädelin S, Lu PJ, et al. Personalized maps of T1 relaxometry abnormalities provide correlates of disability in multiple sclerosis patients. NeuroImage: Clinical. 2023;37:103349.

3. Cagol A, Ocampo-Pineda M, Lu PJ, et al. Advanced Quantitative MRI Unveils Microstructural Thalamic Changes Reflecting Disease Progression in Multiple Sclerosis. Neurol Neuroimmunol Neuroinflamm. 2024;11(6):e200299. doi:10.1212/NXI.0000000000200299 [doi]

4. Jansen MG, Zwiers MP, Marques JP, et al. The Advanced BRain Imaging on ageing and Memory (ABRIM) data collection: Study design, data processing, and rationale. PLoS One. 2024;19(6):e0306006. doi:10.1371/journal.pone.0306006 [doi]

5. INsIDER-RAP: ImagiNg the Interplay between Axonal DamagE and Repair in Multiple Sclerosis – Remyelinating lesions And Pathways. Retrieved 29 October 2025, from https://data.snf.ch/grants/grant/206151

6. Chen X, Ocampo-Pineda M, Lu PJ, et al. Unveiling Normative Trajectories of Lifespan Brain Maturation Using Quantitative MRI. arXiv. Preprint posted online November 1, 2024. doi:10.48550/arXiv.2411.00661 [doi]

7. Fischl B. FreeSurfer. Neuroimage. 2012;62(2):774-781.

8. Pomponio R, Erus G, Habes M, et al. Harmonization of large MRI datasets for the analysis of brain imaging patterns throughout the lifespan. NeuroImage. 2020;208:116450. doi:10.1016/j.neuroimage.2019.116450 [doi]

9. Chan KS, Zwiers MP, Jansen MG, et al. Normative trajectories of R1, R2* and magnetic susceptibility in basal ganglia on healthy ageing. Imaging Neuroscience. Published online 2024. Accessed January 10, 2025. https://direct.mit.edu/imag/article/doi/10.1162/imag_a_00456/127408

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/409-03-010