State Estimation Problem in a Complex Domain: RF Hyperthermia Treatment using Nanoparticles
Leonardo Antonio Bermeo Varon1; Helcio Rangel Barreto Orlande1; Guillermo Enrique Eliçabe2
1 Universidade Federal do Rio de Janeiro; 2 Universidad del Mar del Plata
doi:10.20906/CPS/USM-2016-0058
Resumo
The particle filter methods have been commonly used to solve inverse problems of sequential Bayesian inference in dynamic models. The particle filter methods are an approximation of sequences of probability distributions of interest, using a large set of random samples, which take into account uncertainties in the model and in the measurements. In this paper the main focus is the solution the state estimation in radiofrequency (RF) hyperthermia with nanoparticles in a complex domain. This domain contains different tissues like muscle, pancreas, lungs, duodenum and a tumor which is loaded with magnetic nanoparticles. The radiofrequency induction causes a temperature increase in the tissues, majorly in the tumor where the nanoparticles are concentrated. The results indicated an excellent agreement between estimated and exact values of temperature in the region.
Palavras-chave: Bayesian Inference; Inverse Problem; Hyperthermia; Radiofrequency; Nanoparticles