Quantitative Cerebrovascular Analysis for Improved Prediction of Post-Stroke Complications
These findings demonstrate that cerebrovascular structure-an often overlooked yet physiologically fundamental determinant of ischemic injury and reperfusion dynamics-provides significant predictive information that is not captured by standard clinical or visual imaging assessments. Automated vascular segmentation and collateral grading techniques enable rapid and objective integration of cerebrovascular metrics into prognostic models, offering a scalable tool for precision risk stratification, supporting earlier intervention, targeted monitoring, and improved post-EVT management.
Authors
- Laith Altaweel
- Zelalem Bahiru
- Jonathon P. Fanning (ORCID: https://orcid.org/0000-0002-1675-0522)
- Kaveh Laksari (ORCID: https://orcid.org/0000-0001-5985-5378)
- Aditi Deshpande (ORCID: https://orcid.org/0000-0001-7657-2812)
- Seajin Yi
- Tahddeus Leiphart
- Jing Wang
Institutions
- University of California, Riverside (US)
- Inova Fairfax Hospital (US)
- Capital Meeting Planning (US)
- University of Virginia Medical Center (US)
Publication Details
- Journal
- medRxiv
- Published
- 2026-02-17
- DOI
- https://doi.org/10.64898/2026.02.12.26346217
- Primary Topic
- Acute Ischemic Stroke Management
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Institutes of Health
- National Institute of Neurological Disorders and Stroke