Classification of arterial and venous cerebral vasculature based on wavelet postprocessing of CT perfusion data - Havla, Lukas; Schneider, Moritz J.; Thierfelder, Kolja M.; Beyer, Sebastian E.; Ertl‐Wagner, Birgit; Reiser, Maximilian F.; Sommer, Wieland H.; Dietrich, Olaf (Medical physics (Lancaster), 2016)
Purpose: The purpose of this study was to propose and evaluate a new wavelet‐based technique for classification of arterial and venous vessels using time‐resolved cerebral CT perfusion data sets. Methods: Fourteen consecutive patients (mean age 73 yr, range 17–97) with suspected stroke but no pathology in follow‐up MRI were included. A CT perfusion scan with 32 dynamic phases was performed during intravenous bolus contrast‐agent application. After rigid‐body motion correction, a Paul wavelet (order 1) was used to calculate voxelwise the wavelet power spectrum (WPS) of each attenuation‐time course. The angiographic intensity A was defined as the maximum of the WPS, located at the coordinates T (time axis) and W (scale/width axis) within the WPS. Using these three parameters (A, T, W) separately as well as combined by (1) Fisher's linear discriminant analysis (FLDA), (2) logistic regression (LogR) analysis, or (3) support vector machine (SVM) analysis, their potential to classify 18 different arterial and venous vessel segments per subject was evaluated. Results: The best vessel classification was obtained using all three parameters A and T and W [area under the curve (AUC): 0.953 with FLDA and 0.957 with LogR or SVM]. In direct comparison, the wavelet‐derived parameters provided performance at least equal to conventional attenuation‐time‐course parameters. The maximum AUC obtained from the proposed wavelet parameters was slightly (although not statistically significantly) higher than the maximum AUC (0.945) obtained from the conventional parameters. Conclusions: A new method to classify arterial and venous cerebral vessels with high statistical accuracy was introduced based on the time‐domain wavelet transform of dynamic CT perfusion data in combination with linear or nonlinear multidimensional classification techniques.