Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
669-01-008 ISMRM Abstract

Predicting Multiple Sclerosis with Multimodal Deep Learning Integrating Lesion and Normal-Appearing White Matter Information

Primary: Neuro - Multiple Sclerosis
Secondary: Analysis Methods - Classification and Prediction
669-01-008 · Neuroinflammation: Metabolites, Function, and AI · Thursday, 14 May, 8:30 AM–9:25 AM · Digital Posters Row J
Keywords: Multiple Sclerosis Deep learning Deep learning classification Brain MRI Multimodal MRI
Accepted
Jiajian Ma1, Valentin Stepanov2,3,4, Wushuang Rui5, Hsuan-Chih Chen1, Michael Lan2, Jenny Chen2,3,4, Timothy Liu6, Ingrid Littig2, Roshi R Patel2, Matthew Breen2, Matthew D Lee2, Katharina Eikermann-Haerter2, Dmitry S Novikov2,3,4, Kimberly ONeill7, Els Fieremans 2,3,4, Yiqiu Shen2
1Center for Data Science, New York University, New York, United States of America
2Department of Radiology, New York University Grossman School of Medicine, New York, United States of America
3Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, United States of America
4Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University Grossman School of Medicine, New York, United States of America
5Department of Biology, New York University, New York, United States of America
6New York University Grossman School of Medicine, New York, United States of America
7Department of Neurology, New York University Grossman School of Medicine, New York, United States of America
Presenting Author: Els Fieremans

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References

1. Montalban X, Lebrun-Frénay C, Oh J, et al. Diagnosis of multiple sclerosis: 2024 revisions of the McDonald criteria. Lancet Neurol. 2025;24(10):850-865. doi: 10.1016/S1474-4422(25)00270-4 [doi]
2. Cagol A, Cortese R, Barakovic M, et al. Diagnostic performance of cortical lesions and the central vein sign in multiple sclerosis. JAMA Neurol. 2024;81(2):143-153. doi: 10.1001/jamaneurol.2023.4737 [doi]
3. de Kouchkovsky I, Fieremans E, Fleysher L, Herbert J, Grossman RI, Inglese M. Quantification of normal-appearing white matter tract integrity in multiple sclerosis: a diffusion kurtosis imaging study. J Neurol. 2016;263(6):1146-1155. doi: 10.1007/s00415-016-8118-z [doi]
4. Le Bihan D, Mangin JF, Poupon C, et al. Diffusion tensor imaging: concepts and applications. J Magn Reson Imaging. 2001;13(4):534-546. doi: 10.1002/jmri.1076 [doi]
5. Fieremans E, Jensen JH, Helpern JA. White matter characterization with diffusional kurtosis imaging. Neuroimage. 2011;58(1):177-188. doi: 10.1016/j.neuroimage.2011.06.006 [doi]
6. Coelho S, Baete SH, Lemberskiy G, et al. Reproducibility of the Standard Model of diffusion in white matter on clinical MRI systems. Neuroimage. 2022;257(119290):119290. doi: 10.1016/j.neuroimage.2022.119290 [doi]
7. Hatamizadeh A, Nath V, Tang Y, Yang D, Roth H, Xu D. Swin UNETR: Swin transformers for semantic segmentation of brain tumors in MRI images. arXiv [eessIV]. Published online January 4, 2022. http://arxiv.org/abs/2201.01266
8. Wu L, Zhuang J, Chen H. Large-scale 3D medical image pre-training with geometric context priors. arXiv [csCV]. Published online October 13, 2024. http://arxiv.org/abs/2410.09890
9. Ilse M, Tomczak J, Welling M. Attention-based Deep Multiple Instance Learning. In: Dy J, Krause A, eds. Proceedings of the 35th International Conference on Machine Learning. Vol 80. Proceedings of Machine Learning Research. PMLR; 10--15 Jul 2018:2127-2136.

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http://echo.ismrm.org/p/ISMRM2026/669-01-008