Acute Stroke: Automatic Perfusion Lesion Outlining Using Level Sets

Abstract

To develop a user-independent algorithm for the delineation of hypoperfused tissue on perfusion-weighted images and evaluate its performance relative to a standard threshold method in simulated data, as well as in acute stroke patients. Materials andMethods:The study was approved by the local ethics committee, and patients gave written informed consent prior to their inclusion in the study. The algorithm identifies hypoperfused tissue in mean transit time maps by simultaneously minimizing the mean square error between individual and mean perfusion values inside and outside a smooth boundary. In 14 acute stroke patients, volumetric agreement between automated outlines and manual outlines determined in consensus among four neuroradiologists was assessed with Bland-Altman analysis, while spatial agreement was quantified by using lesion overlap relative to mean lesion volume (Dice coefficient). Performance improvement relative to a standard threshold approach was tested with the Wilcoxon signed rank test. Results:The mean difference in lesion volume between automated outlines and manual outlines was 29.0 mL 6 44.5 (standard deviation). The lowest mean volume difference for the threshold approach was 225.8 mL 6 88.2. A significantly higher Dice coefficient was observed with the algorithm (0.71; interquartile range [IQR], 0.42-0.75) compared with the threshold approach (0.50; IQR, 0.27-0.57; P , .001). The corresponding agreement among experts was 0.79 (IQR, 0.69-0.83). Conclusion:The perfusion lesions outlined by the automated algorithm agreed well with those defined manually in consensus by four experts and were superior to those obtained by using the standard threshold approach. This user-independent algorithm may improve the assessment of perfusion images as part of acute stroke treatment.