Parameter estimation for personalization of liver tumor radiofrequency ablation

Abstract

Mathematical modeling has the potential to assist radiofrequency ablation (RFA) of tumors as it enables prediction of the extent of ablation. However, the accuracy of the simulation is challenged by the material properties since they are patient-specific, temperature and space dependent. In this paper, we present a framework for patientspecific radiofrequency ablation modeling of multiple lesions in the case of metastatic diseases. The proposed forward model is based upon a computational model of heat diffusion, cellular necrosis and blood flow through vessels and liver which relies on patient images. We estimate the most sensitive material parameters, those need to be personalized from the available clinical imaging and data. The selected parameters are then estimated using inverse modeling such that the point-to-mesh distance between the computed necrotic area and observed lesions is minimized. Based on the personalized parameters, the ablation of the remaining lesions are predicted. The framework is applied to a dataset of seven lesions from three patients including pre- and post-operative CT images. In each case, the parameters were estimated on one tumor and RFA is simulated on the other tumor(s) using these personalized parameters, assuming the parameters to be spatially invariant within the same patient. Results showed significantly good correlation between predicted and actual ablation extent (average point-to-mesh errors of 4.03 mm).