Radiofrequency ablation planning beyond simulation.

It is a challenging task to plan a radiofrequency (RF) ablation therapy to achieve the best outcome of the treatment and avoid recurrences at the same time. A patient specific simulation in advance that takes the cooling effect of blood vessels into account is a helpful tool for radiologists, but this needs a very high accuracy and thus high computational costs. In this work, we present various methods, which improve and extend the planning of an RF ablation procedure. First, we discuss two extensions of the simulation model to obtain a higher accuracy, including the vaporization of the water in the tissue and identifying the model parameters and to analyze their uncertainty. Furthermore, we discuss an extension of the planning procedure namely the optimization of the probe placement, which optimizes the overlap of the tumor area with the estimated coagulation in order to avoid recurrences. Since the optimization is constrained by the model, we have to take into account the uncertainties in the model parameters for the optimization as well. Finally, applications of our methods to a real RF ablation case are presented.

Optimal applicator placement in hepatic radiofrequency ablation on the basis of rare data.

In this paper, a numerical procedure to determine an optimal applicator placement for hepatic radiofrequency ablation incorporating uncertain material parameters is presented. The main focus is set on the treatment of subjective and rare data-based information. For this purpose, we employ the theory of fuzzy sets and model uncertain parameters as fuzzy quantities. While fuzzy modelling has been established in structural engineering in the recent past, it is novel in biomedical engineering. Incorporating fuzzy quantities within an optimisation task is basically innovative. In our context, fuzzy modelling allows us to determine an optimal applicator placement that maximises the therapy success under the given uncertainty conditions. The applicability of our method is demonstrated by means of an example case.

Multiscale optimization of the probe placement for radiofrequency ablation.

RATIONALE AND OBJECTIVES: We present a model for the optimal placement of mono- and bipolar probes in radiofrequency (RF) ablation. The model is based on a system of partial differential equations that describe the electric potential of the probe and the steady state of the induced heat distribution. MATERIALS AND METHODS: To optimize the probe placement we minimize a temperature-based objective function under the constraining system of partial differential equations. Further, the extension of the resulting optimality system for the use of multiple coupled RF probes is discussed. We choose a multiscale gradient descent approach to solve the optimality system. RESULTS: This article describes the discretization and implementation of the approach with finite elements on three-dimensional hexahedral grids. CONCLUSION: Applications of the optimization to artificial test scenarios as well as a comparison to a real RF ablation show the usefulness of the approach.

Numerical simulation of radio frequency ablation with state dependent material parameters in three space dimensions.

We present a model for the numerical simulation of radio frequency (RF) ablation of tumors with mono- or bipolar probes. This model includes the electrostatic equation and a variant of the well-known bio-heat transfer equation for the distribution of the electric potential and the induced heat. The equations are nonlinearly coupled by material parameters that change with temperature, dehydration and damage of the tissue. A fixed point iteration scheme for the nonlinear model and the spatial discretization with finite elements are presented. Moreover, we incorporate the effect of evaporation of water from the cells at high temperatures using a predictor-corrector like approach. The comparison of the approach to a real ablation concludes the paper.

Towards optimization of probe placement for radio-frequency ablation.

We present a model for the optimal placement of mono- and bipolar probes in radio-frequency (RF) ablation. The model is based on a numerical computation of the probe's electric potential and of the steady state of the heat distribution during RF ablation. The optimization is performed by minimizing a temperature based objective functional under these constraining equations. The paper discusses the discretization and implementation of the approach. Finally, applications of the optimization to artificial data and a comparison to a real RF ablation are presented.