Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
352-02-015 / 352-02-015 ISMRM Abstract

Deep learning spatial prediction of longitudinal WMH progression in ~ 1 year with pCASL, FLAIR, and MPRAGE

Accepted
Sang Hun Chung 1, Elizabeth Joe2, Tianrui Zhao1,3, Yining He1,3, Vasilis Z Marmarelis4, Helena Chui2, Lirong Yan1,3
1Department of Radiology, Northwestern University Feinberg School of Medicine, Chicago, United States of America
2Department of Neurology, Keck School of Medicine, University of Southern California, Los Angeles, United States of America
3Biomedical Engineering, Northwestern University, Chicago, United States of America
4Alfred E. Mann Department of Biomedical Engineering, University of Southern California, Los Angeles, United States of America
Presenting Author: Sang Hun Chung

Synopsis

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References

1. Debette, Stéphanie, and HS20660506 Markus. "The clinical importance of white matter hyperintensities on brain magnetic resonance imaging: systematic review and meta-analysis." Bmj 341 (2010).
2. Prins, Niels D., and Philip Scheltens. "White matter hyperintensities, cognitive impairment and dementia: an update." Nature Reviews Neurology 11.3 (2015): 157-165.
3. Rachmadi, Muhammad Febrian, et al. "Automatic spatial estimation of white matter hyperintensities evolution in brain MRI using disease evolution predictor deep neural networks." Medical image analysis 63 (2020): 101712.
4. Rachmadi, Muhammad Febrian, et al. "Prediction of white matter hyperintensities evolution one-year post-stroke from a single-point brain MRI and stroke lesions information." Scientific Reports 15.1 (2025): 1208.
5. Thammasart, Siriluk, et al. "Associations between cerebral blood flow and progression of white matter hyperintensities." Frontiers in neuroimaging 3 (2025): 1463311.
6. Promjunyakul, N., et al. "Characterizing the white matter hyperintensity penumbra with cerebral blood flow measures." NeuroImage: Clinical 8 (2015): 224-229.
7. Promjunyakul, Nutta-on, et al. "Comparison of cerebral blood flow and structural penumbras in relation to white matter hyperintensities: a multi-modal magnetic resonance imaging study." Journal of cerebral blood flow & metabolism 36.9 (2016): 1528-1536.
8. Lesions were segmented by the lesion prediction algorithm (Schmidt, 2017, Chapter 6.1) as implemented in the LST toolbox version 3.0.0 (www.statistical-modelling.de/lst.html) for SPM.
9. Wellcome Trust Centre for Neuroimaging. (2014). Statistical Parametric Mapping (SPM12) [Computer software]. University College London. Retrieved from https://www.fil.ion.ucl.ac.uk/spm/
10. Isensee, F., Jaeger, P. F., Kohl, S. A., Petersen, J., & Maier-Hein, K. H. (2021). nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nature methods, 18(2), 203-211.
11. Dice, L. R. (1945). Measures of the amount of ecologic association between species. Ecology, 26(3), 297–302. https://doi.org/10.2307/1932409 [doi]
12. Hausdorff, F. (1914). Grundzüge der Mengenlehre. Leipzig: Veit.
13. Jaccard, P. (1912). The distribution of the flora in the alpine zone. New Phytologist, 11(2), 37–50. https://doi.org/10.1111/j.1469-8137.1912.tb05611.x [doi]

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