Improved stratification for thrombectomy after acute ischemic stroke using personalized brain thermal modeling

Motivation: Prior research has demonstrated the benefits of thrombectomy after acute ischemic stroke (AIS). Despite improvements in surgical techniques, failed reperfusion after thrombectomy is problematic. Goal(s): Our goal was to evaluate brain temperature-based identification of infarcted and salvageable tissue for improved stratification after AIS. Approach: A patient-specific computational model using imaging data was used to predict local brain temperatures after AIS to identify infarcted and salvageable tissue and compared to existing clinical methods (RAPID). Results: Temperature-based stratification identified infarct regions not observed with RAPID and predicted lower mismatch ratios more consistent with final clinical outcomes. Impact: We demonstrate the potential for model-predicted brain temperatures to quantify infarcted and salvageable tissue after acute ischemic stroke for patient selection for thrombectomy. Local brain temperature may complement existing metrics, particularly for patients without sufficient salvageable tissue.