Physiological evidence suggests that neighboring brain regions have similar perfusion characteristics (vascular supply, collateral blood flow). It is largely unknown whether integrating perfusion CT (pCT) information from the area surrounding a given voxel (i.e. the receptive field (RF)) improves the prediction of infarction of this voxel. Based on general linear regression models (GLMs) and using acute pCT-derived maps, we compared the added value of cuboid RF to predict the final infarct. To this aim, we included 144 stroke patients with acute pCT and follow-up MRI, used to delineate the final infarct. Overall, the performance of GLMs to predict the final infarct improved when using RF for all pCT maps (cerebral blood flow, cerebral blood volume, mean transit time and time-to-maximum of the tissue residual function (Tmax)). The highest performance was obtained with Tmax (glm(Tmax); AUC = 0.89 ± 0.03 with RF vs. 0.78 ± 0.02 without RF; p < 0.001) and with a model combining all perfusion parameters (glm(multi); AUC 0.89 ± 0.02 with RF vs. 0.79 ± 0.02 without RF; p < 0.001). These results suggest that prediction of infarction improves by integrating perfusion information from adjacent tissue. This approach may be applied in future studies to better identify ischemic core and penumbra thresholds and improve patient selection for acute stroke treatment.
Keywords: Machine learning; perfusion imaging; prediction; receptive field; stroke.