Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026
· Cape Town, South Africa
603-01-006
ISMRM Abstract
Cognitive Score Correlations with a Motion Robust Small Brain Vessel Segmentation Deep Learning Model
Primary:
Neuro - Blood Vessels
Secondary:
Analysis Methods - Segmentation and Detection
603-01-006 · Dementia Not Related to Alzheimer's Disease
· Thursday, 14 May, 8:30 AM–10:20 AM · Auditorium 1
Keywords:Cerebral small vessel diseaseVoxel-wise analysisCognitive FunctionDeep Learning Segmentation
Accepted
Steve A Mendoza1, Zidong Yang1,2, Chenyang Zhao1,2, Jesse Lamas3, Kay Jann1,4,5, Michael G Harrington6, John Ringman6, Xuejuan Jiang6, Yonggang Shi4, Danny J Wang 1,2,5
1University of Southern California, Los Angeles, United States of America
2Laboratory of FMRI Technology (LOFT), Mark & Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, United States of America
3Laboratory of FMRI Technology (LOFT), USC Mark & Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, United States of America
4USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, United States of America
5USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, United States of America
6Department of Neurology, Keck School of Medicine, University of Southern California, Los Angeles, United States of America
Presenting Author: Danny J Wang
Synopsis
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1. Heiss WD, Brainin M, Schwarz A. Cerebral Small-Vessel Disease. Textbook of Stroke Medicine. Published online April 18, 2019:202-212. doi:https://doi.org/10.1017/9781108659574.012 [doi]
2. Ma SJ, Mona Sharifi Sarabi, Yan L, et al. Characterization of lenticulostriate arteries with high resolution black-blood T1-weighted turbo spin echo with variable flip angles at 3 and 7 Tesla. 2019;199:184-193. doi:https://doi.org/10.1016/j.neuroimage.2019.05.065 [doi]
3. Sarabi MS, Ma SJ, Jann K, Ringman JM, Wang DJJ, Shi Y. Vessel density mapping of small cerebral vessels on 3D high resolution black blood MRI. NeuroImage. 2024;286:120504.doi:https://doi.org/10.1016/j.neuroimage.2023.120504 [doi]
4. Isensee F, Jaeger PF, Kohl SAA, Petersen J, Maier-Hein KH. nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nature Methods. 2020;18(2):203-211. doi:https://doi.org/10.1038/s41592-020-01008-z [doi]
5. Duffy BA, Zhao L, Sepehrband F, et al. Retrospective motion artifact correction of structural MRI images using deep learning improves the quality of cortical surface reconstructions. NeuroImage. 2021;230:117756. doi:https://doi.org/10.1016/j.neuroimage.2021.117756 [doi]
6. Jerman T, Pernuš F, Likar B, Špiclin Ž. Enhancement of Vascular Structures in 3D and 2D Angiographic Images. IEEE Transactions on Medical Imaging. 2016;35(9):2107-2118. doi:https://doi.org/10.1109/TMI.2016.2550102 [doi]
7. Buades A, Coll B, Morel JM . A Non-Local Algorithm for Image Denoising. 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05). Published online 2005. doi:https://doi.org/10.1109/cvpr.2005.38 [doi]
8. Hoopes A, Mora JS, Dalca AV, Fischl B, Hoffmann M. SynthStrip: skull-stripping for any brain image. NeuroImage. 2022;260:119474-119474. doi:https://doi.org/10.1016/j.neuroimage.2022.119474 [doi]
9. Avants B, Tustison NJ, Song G. Advanced Normalization Tools: V1.0. The Insight Journal. Published online July 29, 2009. doi:https://doi.org/10.54294/uvnhin [doi]
10. Friston KJ, Holmes AP, Worsley KJ, Poline JP ., Frith CD, Frackowiak RSJ. Statistical parametric maps in functional imaging: A general linear approach. Human Brain Mapping. 1994;2(4):189-210. doi:https://doi.org/10.1002/hbm.460020402 [doi]
11. Jia J, Staring M, Stoel BC. Seg-metrics: a Python package to compute segmentation metrics. arXiv.org. Published 2024. Accessed October 27th, 2025. https://arxiv.org/abs/2403.07884
12. Zhou L, Wu H, Luo G, Zhou H. Deep learning-based 3D cerebrovascular segmentation workflow on bright and black blood sequences magnetic resonance angiography. Insights into Imaging. 2024;15(1). doi:https://doi.org/10.1186/s13244-024-01657-0 [doi]