Validation of a method to differentiate arterial and venous vessels in CT perfusion data using linear combinations of quantitative time-density curve characteristics.

Objectives: We aimed to develop and evaluate a new method that reliably differentiates between cerebral arteries and veins using voxel-wise CT-perfusion-derived parameters. Materials and Methods: Fourteen consecutive patients with suspected stroke but without pathological findings were examined on a multi-detector CT system: 32 dynamic phases (∆t = 1.5 s) during application of 35 mL iomeprol-350 were acquired at 80 kV/200mAs. Three hemodynamic parameters were calculated for 18 arterial and venous vessel segments: A (maximum of the time-density-curve), T (time-to-peak), and W (full-width-at-half-maximum). Using receiver operator characteristic (ROC) curve analysis and Fisher's linear discriminant analysis (FLDA), the performance of every classifier ( A, T, W) and of all linear combinations for the differentiation of arterial and venous vessels was determined. Results: A maximum area under the ROC-curve (AUC) of 0.945 (accuracy = 86.8 %) was obtained using the FLDA combination of A&T or the triplet FLDA of A&T&W for the classification of venous and arterial vessels. The best single parameter was T with an AUC of 0.871 (accuracy = 79.0 %), which performed significantly worse than the combination A&T ( p < 0.001). Conclusions: Arteries and veins can be accurately differentiated based on dynamic CT perfusion data using the maximum of the time-density curve, its time-to-peak, its width, and FLDA combinations of these parameters, which yield accuracies up to 87 %. Key points: • For classification of cerebral vasculature, time-to-peak has the best single-parameter accuracy. • Fisher's linear discriminant analysis improves the performance of the individual classifiers. • Combining signal maximum and time-to-peak parameters significantly increased the classifying potential. • Pre-processing of time-density-curves by Gaussian filtering or fitting can improve diagnostic accuracy. [ABSTRACT FROM AUTHOR]