Particle Filter and Approximation Error Model for State Estimation in Hyperthermia

2016 ◽  
Vol 139 (1) ◽  
Author(s):  
Bernard Lamien ◽  
Helcio Rangel Barreto Orlande ◽  
Guillermo Enrique Eliçabe

This work deals with numerical simulation of a hyperthermia treatment of skin cancer as a state estimation problem, where uncertainties in the evolution and measurement models, as well as in the measured data, are accounted for. A reduced model is adopted, based on a coarse mesh for the solution of the partial differential equations that describe the physical problem, in order to expedite the solution of the state estimation problem with a particle filter algorithm within the Bayesian framework of statistics. The so-called approximation error model (AEM) is used in order to statistically compensate for model reduction effects. The Liu and West algorithm of the particle filter, together with the AEM, is shown to provide accurate estimates for the temperature and model parameters in a multilayered region containing a tumor loaded with nanoparticles. Simulated transient temperature measurements from one sensor are used in the analysis.

Author(s):  
Bernard Lamien ◽  
Leonardo A.B. Varon ◽  
Helcio R.B. Orlande ◽  
Guillermo E. Elicabe

Purpose The purpose of this paper is to focus on applications related to the hyperthermia treatment of cancer, with heating imposed either by a laser in the near-infrared range or by radiofrequency waves. The particle filter algorithms are compared in terms of computational time and solution accuracy. Design/methodology/approach The authors extend the analyses performed in their previous works to compare three different algorithms of the particle filter, as applied to the hyperthermia treatment of cancer. The particle filters examined here are the sampling importance resampling (SIR) algorithm, the auxiliary sampling importance resampling (ASIR) algorithm and Liu & West’s algorithm. Findings Liu & West’s algorithm resulted in the largest computational times. On the other hand, this filter was shown to be capable of dealing with very large uncertainties. In fact, besides the uncertainties in the model parameters, Gaussian noises, similar to those used for the SIR and ASIR filters, were added to the evolution models for the application of Liu & West’s filter. For the three filters, the estimated temperatures were in excellent agreement with the exact ones. Practical implications This work may help medical doctors in the future to prescribe treatment protocols and also opens the possibility of devising control strategies for the hyperthermia treatment of cancer. Originality/value The natural solution to couple the uncertain results from numerical simulations with the measurements that contain uncertainties, aiming at the better prediction of the temperature field of the tissues inside the body, is to formulate the problem in terms of state estimation, as performed in this work.


Author(s):  
Helcio R.B. Orlande ◽  
Marcelo Colaco ◽  
George S. Dulikravich ◽  
Luiz F.S. Ferreira

Evolution model is based on that used by Hernandez et al., which considers the following groups: Susceptible, Incubating, Asymptomatic, Symptomatic, Hospitalized, Recovered and Accumulated deaths. Evolution model considers the possibility of infections from asymptomatic, symptomatic and hospitalized individuals. Evolution model considers the possibility that individuals who have recovered from the disease become symptomatic again. Observation model accounts for underreport of cases and deaths. Observation model accounts for delays in reporting cases and deaths. Model parameters were initially estimated with the Markov Chain Monte Carlo (MCMC) method, by using the data of the city of Rio de Janeiro from February 28, 2020 to April 29, 2020. These estimations were used as initial input values for the solution of the state estimation problem for the city of Rio de Janeiro. Algorithm of Liu & West for the Particle Filter was used for the solution of the state estimation problem because it allows the simultaneous estimation of state variables and model parameters. State estimation problem was solved with the data of the city of Rio de Janeiro, from February 28, 2020 to May 05, 2020. Monte Carlo simulations were run for 20 future days, considering uncertainties in the model parameters and state variables. Initial conditions were given by the state variables and corresponding distributions estimated with the particle filter on May 05, 2020. Distributions of the model parameters were also given by the estimations obtained for this date. Data of the city of Rio de Janeiro, from May 06, 2020 to May 15, 2020, were used for the validation of the solution of the state estimation problem. The present model, with the parameters obtained with the Particle Filter, accurately fits the number of reported cases and the number of reported deaths, for 10 days ahead of the period used for the solution of the state estimation problem. The Ratio of Infected Individuals per Reported Cases was around 15 on May 05, 2020. The Indexes of Under-Reported Cases and Deaths were around 12 and 2, respectively, on May 05, 2020. The Effective Reproduction Number was around 1.6 on February 28, 2020 and dropped to around 0.9 on May 05, 2020. However, uncertainties related to this parameter are large and the effective reproduction number is between 0.3 and 1.5, at the 95% credibility level. The particle filter must be used to periodically update the estimation of state variables and model parameters, so that future predictions can be made. Day 0 is February 28, 2020.


2015 ◽  
Author(s):  
Leonardo Antonio Bermeo Varon ◽  
Helcio Rangel Barreto Orlande ◽  
Guillermo Enrique Eliçabe

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