Model-based optimal planning of hepatic radiofrequency ablation

This article presents a model-based pre-treatment optimal planning framework for hepatic tumour radiofrequency (RF) ablation. Conventional hepatic radiofrequency (RF) ablation methods rely on pre-specified input voltage and treatment length based on the tumour size. Using these experimentally obtained pre-specified treatment parameters in RF ablation is not optimal to achieve the expected level of cell death and usually results in more healthy tissue damage than desired. In this study we present a pre-treatment planning framework that provides tools to control the levels of both the healthy tissue preservation and tumour cell death. Over the geometry of tumour and surrounding tissue, we formulate the RF ablation planning as a constrained optimization problem. With specific constraints over the temperature profile (TP) in pre-determined areas of the target geometry, we consider two different cost functions based on the history of the TP and Arrhenius index (AI) of the target location, respectively. We optimally compute the input voltage variation to minimize the damage to the healthy tissue while ensuring a complete cell death in the tumour and immediate area covering the tumour. As an example, we use a simulation of a 1D symmetric target geometry mimicking the application of single electrode RF probe. Results demonstrate that compared to the conventional methods both cost functions improve the healthy tissue preservation.