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

Abstract

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.

Sectional global sensitivity measures

“…In this case, if the design parameters are selected from the middle of the input ranges the design is more robust than other ones. By the way, this design will also be an optimal one, see [26].…”

“…A plain optimization is not straightforward since the input parameters are uncertain. Details to design a reliable intervention are given in [26]. Here, the functional input-output interrelations are determined for a preliminary investigation.…”

Computational modelling of microwave tumour ablations

“…Optimization algorithms have been used to rank the possible insertion points and find the best antenna placement location, maximizing chances of the best clinical outcome and minimizing risks for complications (115–117). To cut down on computing costs, some groups have utilized an objective cost function that optimizes the temperature distribution by avoiding larger vasculatures, creating an optimal trajectory of an RFA applicator (104). Decreasing the computational time of calculating ablation zone behavior near major vasculatures has also been demonstrated using patient-independent lookup tables.…”

“…These tables utilized previously-calculated cooling rates of blood vessels based on vessel size and distance from an RF applicator. Using these tables to predict ablation zone characteristics was demonstrated to be computationally faster than solving for the electromagnetic and heat transfer solutions independently for a patient-specific anatomy (118). Other efforts to improve computational speed have involved a GPU-driven real-time calculation of the projected ablation zone, while incorporating the cooling effects of nearby liver vasculature (119).…”

Assessment of structural sensitivity on the basis of artificial neural networks

“…This example is elucidated in detail in [2]. Generally, the aim is to assess the influence of the eight input parameters in view of the result parameters.…”