Comparison of different 4D CT-Perfusion algorithms to visualize lesions after microwave ablation in an in vivo porcine model

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. Parametermaps 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.

Short-term PET-derived kinetic estimation for the diagnosis of hepatocellular carcinoma: a combination of the maximum-slope method and dual-input three-compartment model

“…The maximum-slope method presented by Mullani et al [ 20 , 21 ] was used to calculate the required blood flow parameters [ 22 ]. The model assumes that when the tracer first passes through the tissue, the venous exit of the tracer is delayed for a period of time, which is a function of the volume of distribution of the tracer in the target tissue and the blood vessel density [ 23 ]. During this time delay, for highly extracted tracers, most of the tracers remain in the tissue because the vein outlet is very small [ 24 ].…”

Imaging in interventional oncology, the better you see, the better you treat

“…21,[23][24][25][26][27] Retrospective data show that perfusion CT could improve the pretreatment prediction of response to radioembolization in HCC 28 and is the best predictor of response in colorectal liver metastasis. 29,30 Moreover, it has shown to be very useful for the early detection of viable tumour after ablation, in which dualinput-deconvolution-mode appears to be the most feasible model. 19 While single energy cannot differentiate between different body materials that have an overlapping linear attenuation coefficient, dual energy can improve material differentiation using two different X-ray energy spectra, from these data, virtual unenhanced images can be extracted and quantification of contrast medium uptake can be done.…”

“…Evaluation of response to therapy (systemic, intra‐arterial and ablative) in liver lesions, pulmonary cancer, pancreatic cancer, GIST, kidney tumours and lymphoma appears to be possible 21,23–27 . Retrospective data show that perfusion CT could improve the pretreatment prediction of response to radioembolization in HCC 28 and is the best predictor of response in colorectal liver metastasis 29,30 . Moreover, it has shown to be very useful for the early detection of viable tumour after ablation, in which dual‐input‐deconvolution‐mode appears to be the most feasible model 19 .…”

CT perfusion imaging can detect residual lung tumor early after radiofrequency ablation: a preliminary animal study on both tumoral and peri-tumoral region assessment

“…Third, the according TDC was generated automatically and adjusted to show the left atrium enhancement peak point in the intersection of the TDC of the pulmonary artery truck and descending aorta (cutpoint of the pulmonary and systemic circulation). Fourth, the dual-input CTP pseudo-color mapping (displaying calculated parameters in rainbow colors) was calculated with the maximum slope method (31,32) at CT values ranging from −80 to 150 HU. Finally, ROIs of the VX2 tumor before and after RFA (referring to the images before RFA) were delineated on the maximal axial images while avoiding the lobar bronchi and vessels to generate the perfusion parameters of bronchial flow (BF, unit: mL/min/100 mL), pulmonary flow (PF, unit: mL/min/100 mL) and lung perfusion index (PI, unit: %).…”