Spatial distribution of perfusion abnormality in acute MCA occlusion is associated with likelihood of later recanalization.

The aim of this study is to investigate whether different spatial perfusion-deficit patterns, which indicate differing compensatory mechanisms, can be recognized and used to predict recanalization success of intravenous fibrinolytic therapy in acute stroke patients. Twenty-seven acute stroke data sets acquired within 6 hours from symptom onset including diffusion- (DWI) and perfusion-weighted magnetic resonance (MR) imaging (PWI) were analyzed and dichotomized regarding recanalization outcome using time-of-flight follow-up data sets. The DWI data sets were used for calculation of apparent diffusion coefficient (ADC) maps and subsequent infarct core segmentation. A patient-individual three-dimensional (3D) shell model was generated based on the segmentation and used for spatial analysis of the ADC as well as cerebral blood volume (CBV), cerebral blood flow, time to peak (TTP), and mean transit time (MTT) parameters derived from PWI. Skewness, kurtosis, area under the curve, and slope were calculated for each parameter curve and used for classification (recanalized/nonrecanalized) using a LogitBoost Alternating Decision Tree (LAD Tree). The LAD tree optimization revealed that only ADC skewness, CBV kurtosis, and MTT kurtosis are required for best possible prediction of recanalization success with a precision of 85%. Our results suggest that the propensity for macrovascular recanalization after intravenous fibrinolytic therapy depends not only on clot properties but also on distal microvascular bed perfusion. The 3D approach for characterization of perfusion parameters seems promising for further research.