Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
564-03-004 ISMRM Abstract

Harnessing Vascular Phenotypes for Cerebrovascular Age Estimation with a Hybrid SVM-GCN Ensemble Model

Primary: Brain Function and fMRI
Secondary: Analysis Methods
564-03-004 · fMRI: Development and Aging · Wednesday, 13 May, 1:40 PM–2:35 PM · Digital Posters Row E
Keywords: Deep learning Vascular imaging Morphological features Graph Neural Networks
Accepted
Junhong Liu 1, Qing Li1, Haoyang Zhang1, Yan Li2, Yajing Zhang3, Mo Yang1, Zian Wang4, Wang Liao1, Chengyan Wang1
1Human Phenome Institute, Fudan University, Shanghai, China
2Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
3Science & Technology, Beijing, China
4School of Computer Science, Fudan University, Shanghai, China
Presenting Author: Junhong Liu

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References

1. ZIMMERMAN B, RYPMA B, GRATTON G, et al. Age‐related changes in cerebrovascular health and their effects on neural function and cognition: A comprehensive review [J]. Psychophysiology, 2021, 58(7): e13796.
2. KUMARI L S, SUNDARRAJAN R. A review on brain age prediction models [J]. Brain Research, 2024, 1823: 148668.
3. ISENSEE F, PETERSEN J, KLEIN A, et al. nnu-net: Self-adapting framework for u-net-based medical image segmentation [J]. arXiv preprint arXiv:180910486, 2018.
4. DALVIT CARVALHO DA SILVA R, SOLTANZADEH R, FIGLEY C R. Automated coronary artery tracking with a voronoi-based 3d centerline extraction algorithm [J]. Journal of Imaging, 2023, 9(12): 268.
5. WANG C, HUANG Y, LIU C, et al. Diagnosis of clinically significant portal hypertension using CT-and MRI-based vascular model [J]. Radiology, 2023, 307(2): e221648.
6. CORTES C, VAPNIK V. Support-vector networks [J]. Machine learning, 1995, 20(3): 273-97.
7. KIPF T. Semi-supervised classification with graph convolutional networks [J]. arXiv preprint arXiv:160902907, 2016.
8. WANG T, SHAO W, HUANG Z, et al. MOGONET integrates multi-omics data using graph convolutional networks allowing patient classification and biomarker identification [J]. Nature communications, 2021, 12(1): 3445.

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/564-03-004