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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-timecourse 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. [ABSTRACT FROM AUTHOR]

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]

Objectives: To validate the anatomical accuracy, homogeneity and sensitivity of two-compartment modeled inversion recovery (TCIR) magnetic resonance imaging (MRI) in a multimodal animal experiment as a non-invasive alternative to standard functional imaging techniques.Methods: Seven pigs were studied on a 1.5 T whole-body MR scanner and SPECT/CT. The specimens were intubated and maintained in general anesthesia throughout the experiment. TCIR maps of the fractional pulmonary blood volume were compared to dynamic contrast enhanced MRI and SPECT/CT via a region of interest (ROI) based reader study. A comprehensive statistical analysis was performed on the coefficient of variation to evaluate homogeneity properties. Sensitivity was assessed by detecting gravitation dependent perfusion variation and delineation of pathological areas.Results: The fPBV-maps of all examined specimens indicate a superior homogeneity in the computed values (p<1.3×10(-4)). The sensitivity of the TCIR maps to a gravitation effect on the blood distribution was verified and a similar anteroposterior signal and count dependency was observed in DCE MRI and SPECT. Bland-Altman analysis showed no significant intra- or inter-observer difference within the ROI reader study (p>0.06).Conclusion: Superior information content, significantly higher homogeneity and similar sensitivity of TCIR when compared to DCE and SPECT/CT demonstrated the feasibility of TCIR MRI as an alternative contrast agent-free, non-invasive functional lung imaging approach. [ABSTRACT FROM AUTHOR]