Time-resolved CT assessment of collaterals as imaging biomarkers to predict clinical outcomes in acute ischemic stroke

Purpose Collateral circulation plays a pivotal role in the pathophysiology of acute ischemic stroke and is increasingly recognized as a promising biomarker for predicting the clinical outcome. However, there is no single established grading system. We designed a novel machine-learning software that allows non-invasive, objective, and quantitative assessment of collaterals according to their vascular territories. Our goal is to investigate the prognostic and predictive value of this collateral score for the prediction of acute stroke outcome.