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March 16, 2026Open Access

Automated CTA-Derived Collateral Grading and Morphologic Metrics for Enhanced Prediction of Post-Stroke Outcomes

Puntos clave

The aim is to evaluate the effectiveness of automated CTA-derived collateral grading and morphology in predicting post-stroke outcomes.
Assess collateral circulation using automated CTA-derived qCI and cerebrovascular morphology.
Compare performance of these metrics against perfusion measurements in predictive tasks.
Evaluate agreement between automated and expert grading.
Automated collateral assessment shows high agreement with expert evaluations.
qCI and morphology metrics outperform traditional perfusion metrics in predicting outcomes.
Combining qCI with CTP significantly enhances prognostic accuracy.

Resumen

Automated CTA-derived qCI and cerebrovascular morphology provide rapid, objective, and reproducible collateral assessment with high agreement to expert grading. These features outperform perfusion metrics in several predictive tasks and further enhance prognostic accuracy when combined with CTP. Because CTA is widely available, qCI offers a scalable, clinically practical tool for improving stroke outcome prediction, particularly in settings where CTP is unavailable.

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