Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
361-05-001 ISMRM Abstract

Physics-guided self-supervised learning for quantitative maps and synthetic imaging in multiple sclerosis detection

Primary: Acquisition & Reconstruction - AI methods
Secondary: Acquisition & Reconstruction - Quantitative Imaging: head and neck
361-05-001 · Quantitative Imaging: Head and Neck I · Monday, 11 May, 4:10 PM–5:05 PM · Digital Posters Row B
Keywords: Multiple Sclerosis Self-supevised learning Double Inversion Recovery (DIR) Physics-informed Deep Learning Quantitative synthetic MRI
Accepted
Bin Zhang 1,2,3, Xingyang Wu3,4, Lixian Zou3,5, Dong Liang3,4,5, Yihang Zhou2,3,4,5, Haifeng Wang1,2,3,4,5
1Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
2University of Chinese Academy of Sciences, Beijing, China
3Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
4Research Center for Medical AI, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
5Key Laboratory for Magnetic Resonance and Multimodality Imaging of Guangdong Province, Shenzhen, China
Presenting Author: Bin Zhang

Synopsis

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References

1. Piet M Bouman, Victor IJ Strijbis, et al., “Artificial double inversion recovery images for (juxta) cortical lesion visualization in multiple sclerosis,” Multiple Sclerosis Journal, vol. 28, no. 4, pp. 541–549, 2022. https://doi.org/10.1177/13524585211029860 [doi]
2. Tom Finck, Hongwei Li, et al., “Deep-learning generated synthetic double inversion recovery images improve multiple sclerosis lesion detection,” Investigative radiology, vol. 55, no. 5, pp. 318–323, 2020. doi:10.1097/RLI.0000000000000640 [doi]
3. Ziga Lesjak et al., “A novel public MR image dataset of multiple sclerosis patients with lesion segmentations based on multi-rater consensus,” Neuroinformatics, vol. 16, pp. 51–63, 2018. doi:10.1007/s12021-017-9348-7 [doi]
4. Ziga Lesjak, Franjo Pernuˇs, et al., “Validation of white-matter lesion change detection methods on a novel publicly available MRI image database,” Neuroinformatics, vol. 14, no. 4, pp.403–420, 2016. doi:10.1007/s12021-016-9301-1 [doi]
5. Md Zahangir Alom, Mahmudul Hasan, et al., “Recurrent residual convolutional neural network based on u-net(r2u-net) for medical image segmentation,” arXiv preprintarXiv:1802.06955, 2018. https://doi.org/10.48550/arXiv.1802.06955 [doi]
6. Phillip Isola, Jun-Yan Zhu, Tinghui Zhou, et al., “Image-to-image translation with conditional adversarial networks,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2017, pp. 1125–1134. doi:10.1109/WIECON-ECE60392.2023.10456447 [doi]
7. Refaat E. Gabr, Khader M. Hasan, et al., “Optimal combination of FLAIR and T2-weighted MRI for improved lesion contrast in multiple sclerosis,” Journal of Magnetic Resonance Imaging, vol. 44, 2016. https://doi.org/10.1002/jmri.25281 [doi]
8. Wang Z, Bovik A, Sheikh H, Simoncelli E. Image quality assessment: from error visibility to structural similarity. IEEE Trans Image Process. 2004;13:600-612. doi:10.1109/TIP.2003.819861 [doi]
9. Qiu S, et al. Physics-guided self-supervised learning for retrospective t1 and t2 mapping from conventional weighted brain mri: Technical developments and initial validation in glioblastoma [J/OL]. Magnetic Resonance in Medicine, 2024, 92(6): 2683-2695. doi:10.1002/mrm.30226. [doi]
10. Gabr R E, Hasan K M, Haque M E, et al. Optimal combination of flair and t2-weighted mri for improved lesion contrast in multiple sclerosis [J]. Journal of Magnetic Resonance Imaging, 2016, 44(5): 1293-1300. doi:10.1002/jmri.25281 [doi]

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/361-05-001