Comparison of different 4D CT-Perfusion algorithms to visualize lesions after microwave ablation in an in vivo porcine model. (1st January 2019) - Bressem, Keno K.; Vahldiek, Janis L.; Erxleben, Christoph; Geyer, Beatrice; Poch, Franz; Shnayien, Seyd; Lehmann, Kai S.; Hamm, B.; Niehues, Stefan M.
Abstract: Background: Accurate lesion visualization after microwave ablation (MWA) remains a challenge. Computed tomography perfusion (CTP) has been proposed to improve visualization, but it was shown that different perfusion-models delivered different results on the same data set. Purpose: Comparison of different perfusion algorithms and identification of the algorithm enables for the best imaging of lesion after hepatic MWA. Materials and methods: 10 MWA with consecutive CTP were performed in healthy pigs. Parameter-maps were generated using a single-input-dual-compartment-model with Patlak's algorithm (PM), a dual-input-maximum-slope-model (DIMS), a dual-input-one-compartment-model (DIOC), a single-(SIDC) and dual-input-deconvolution-model (DIDC). Parameter-maps for hepatic arterial (AF) and portal venous blood flow (PF), mean transit time, hepatic blood volume (HBV) and capillary permeability were compared regarding the values of the normal liver tissue (NLT), lesion, contrast- and signal-to-noise ratios (SNR, CNR) and inter- and intrarater-reliability using the intraclass correlation coefficient, Bland-Altman plots and linear regression. Results: Perfusion values differed between algorithms with especially large fluctuations for the DIOC. A reliable differentiation of lesion margin appears feasible with parameter-maps of PF and HBV for most algorithms, except for the DIOC due to large fluctuations in PF. All algorithms allowed for a demarcation of the central necrotic zone based on hepatic AF and HBV. The DIDC showed the highest CNR and the best inter- and intrarater reliability. Conclusion: The DIDC appears to be the most feasible model to visualize margins and necrosis zones after microwave ablation, but due to high computational demand, a single input deconvolution algorithm might be preferable in clinical practice.