Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026
· Cape Town, South Africa
470-02-086
ISMRM Abstract
White matter hyperintensity burden as a predictor of white matter damage
Primary:
Neuro - White Matter
Secondary:
Neuro - Stroke and Cerebrovascular Disorders
470-02-086 · Decoding Stroke with Imaging and AI: New Frontiers in Cerebrovascular Research
· Tuesday, 12 May, 8:20 AM–9:15 AM · Traditional Posters | Exhibition Hall
Keywords:Diffusion MRIWhite matter hyperintensityMicroscopic AnisotropyCerebral Small Vessel Disease (CSVD)
Accepted
Hyeong-Geol Shin 1,2, Sarvin Sasannia1,2, Mykola Matsyuk1, Shimeng Wang1, Jinwei Zhang1, Xu Li1,2, Filip Szczepankiewicz3, Jerry Prince1, Richard Leigh1, Linda Knutsson1,3,4, Peter C van Zijl1,2, Paul Nyquist1
1Johns Hopkins University, Baltimore, United States of America
2Kennedy Krieger Institute, Baltimore, United States of America
3Lund University, Lund, Sweden
4F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, United States of America
Presenting Author: Hyeong-Geol Shin
Synopsis
Motivation:
Goals:
Approach:
Results:
Full abstract & presentation
The full text, figures, and any recorded presentation for this abstract are not shown here. Log in if you are a member or registered attendee with access.
Full abstracts, figures, and presentations for Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition are available to registered attendees. This content becomes freely available to the public roughly two years after the meeting.
To request or purchase access, contact the ISMRM Central Office at info@ismrm.org.
1. Wardlaw JM, Smith EE, Biessels GJ, et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. Lancet Neurol. 2013;12(8):822-838. doi:10.1016/s1474-4422(13)70124-8 [doi]
2. Debette S, Markus HS. The clinical importance of white matter hyperintensities on brain magnetic resonance imaging: systematic review and meta-analysis. BMJ. 2010;341(jul26 1):c3666. doi:10.1136/bmj.c3666 [doi]
3. Dewey BE, Xu X, Knutsson L, et al. MTT and Blood-Brain Barrier Disruption within Asymptomatic Vascular WM Lesions. Am J Neuroradiol. 2021;42(8):1396-1402. doi:10.3174/ajnr.a7165 [doi]
4. Andersen KW, Lasič S, Lundell H, et al. Disentangling white-matter damage from physiological fibre orientation dispersion in multiple sclerosis. Brain Commun. 2020;2(2):fcaa077. doi:10.1093/braincomms/fcaa077 [doi]
5. Maillard P, Carmichael O, Harvey D, et al. FLAIR and Diffusion MRI Signals Are Independent Predictors of White Matter Hyperintensities. Am J Neuroradiol. 2013;34(1):54-61. doi:10.3174/ajnr.a3146 [doi]
6. Szczepankiewicz F, Lasič S, Westen D van, et al. Quantification of microscopic diffusion anisotropy disentangles effects of orientation dispersion from microstructure: Applications in healthy volunteers and in brain tumors. Neuroimage. 2015;104:241-252. doi:10.1016/j.neuroimage.2014.09.057 [doi]
7. Westin CF, Szczepankiewicz F, Pasternak O, et al. Measurement Tensors in Diffusion MRI: Generalizing the Concept of Diffusion Encoding. Lect Notes Comput Sci. 2014;17(Pt 3):209-216. doi:10.1007/978-3-319-10443-0_27 [doi]
8. Szczepankiewicz F, Westin CF, Nilsson M. Maxwell-compensated design of asymmetric gradient waveforms for tensor-valued diffusion encoding. Magn Reson Med. 2019;82(4):1424-1437. doi:10.1002/mrm.27828 [doi]
9. Tournier JD, Smith R, Raffelt D, et al. MRtrix3: A fast, flexible and open software framework for medical image processing and visualisation. Neuroimage. 2019;202:116137. doi:10.1016/j.neuroimage.2019.116137 [doi]
10. Veraart J, Fieremans E, Novikov DS. Diffusion MRI noise mapping using random matrix theory. Magn Reson Med. 2016;76(5):1582-1593. doi:10.1002/mrm.26059 [doi]
11. Veraart J, Novikov DS, Christiaens D, Ades-aron B, Sijbers J, Fieremans E. Denoising of diffusion MRI using random matrix theory. Neuroimage. 2016;142:394-406. doi:10.1016/j.neuroimage.2016.08.016 [doi]
12. Smith SM, Jenkinson M, Woolrich MW, et al. Advances in functional and structural MR image analysis and implementation as FSL. Neuroimage. 2004;23:S208-S219. doi:10.1016/j.neuroimage.2004.07.051 [doi]
13. Andersson JLR, Skare S, Ashburner J. How to correct susceptibility distortions in spin-echo echo-planar images: application to diffusion tensor imaging. Neuroimage. 2003;20(2):870-888. doi:10.1016/s1053-8119(03)00336-7 [doi]
14. Taoka T, Kawai H, Nakane T, et al. Application of histogram analysis for the evaluation of vascular permeability in glioma by the K2 parameter obtained with the dynamic susceptibility contrast method: Comparisons with Ktrans obtained with the dynamic contrast enhance method and cerebral blood volume. Magn Reson Imaging. 2016;34(7):896-901. doi:10.1016/j.mri.2016.04.020 [doi]
16. Zhao C, Dewey BE, Pham DL, Calabresi PA, Reich DS, Prince JL. SMORE: A Self-Supervised Anti-Aliasing and Super-Resolution Algorithm for MRI Using Deep Learning. IEEE Trans Méd Imaging. 2021;40(3):805-817. doi:10.1109/tmi.2020.3037187 [doi]
17. Zuo L, Liu Y, Xue Y, et al. HACA3: A unified approach for multi-site MR image harmonization. Comput Méd Imaging Graph. 2023;109:102285. doi:10.1016/j.compmedimag.2023.102285 [doi]
18. Zhang J, Zuo L, Dewey BE, et al. Harmonization-enriched domain adaptation with light fine-tuning for multiple sclerosis lesion segmentation. Méd Imaging 2024: Clin Biomed Imaging. 2024;12930:129302I-129302I - 7. doi:10.1117/12.3011291 [doi]