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Início Arquivos Representantes Futuras reuniões American Society of Thermal and Fluids Engineering
First Thermal and Fluids Engineering Summer Conference

ISSN: 2379-1748

A COMPARISON OF PARTICLE FILTER ALGORITHMS APPLIED TO THE TEMPERATURE FIELD ESTIMATION IN HYPERTHERMIA PHANTOMS




Bernard Lamien
Department of Mechanical Engineering, Federal University of Rio de Janeiro − POLI/COPPE, Centro de Tecnologia, Caixa Postal: 68503, Cidade Universitária, Rio de Janeiro, 21941-972, Brazil; Univ. Bretagne Sud, FRE CNRS 3744, IRDL, F-56100 Lorient, France

Helcio R. B. Orlande
Department of Mechanical Engineering, Federal University of Rio de Janeiro – POLI/COPPE, Centro de Tecnologia, Caixa Postal: 68503, Cidade Universitária, Rio de Janeiro, 21941-972, Brazil

Guillermo Elicabe
Institute of Materials Science and Technology (INTEMA), University of Mar del Plata and National Research Council (CONICET)

DOI: 10.1615/TFESC1.bio.013764
pages 2117-2130


Palavras-chave: Inverse Problem, Particle Filters, Hyperthermia, Uncertainties, Phantom

Resumo

In this paper, the hyperthermia treatment of cancer induced by near-infrared laser light is formulated as a state estimation problem and solved with Particle Filters. The physical problem considered in this paper involves the irradiation with a laser in the near infrared range, of a two-layer cylindrical medium simulating a soft-tissue phantom with a tumor. The layer representing the tumor is assumed to be loaded with plasmonic nanoparticles in order to enhance the hyperthermia effects and to limit such effects to the tumor region. The laser tissue interaction is modelled as a coupled radiation-heat conduction problem. For the solution of the inverse problem, local temperature measurements are assumed available. The inverse problem involves the estimation of the transient temperature field and of the fluence rate distribution in the region. Two different Particle Filter algorithms are used in the inverse analysis, namely: the Sampling Importance Resampling (SIR) and the Auxiliary Sampling Importance Resampling (ASIR) algorithms; their performances are compared in terms of accuracy of the inverse problem solution and computational time.

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