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First Thermal and Fluids Engineering Summer Conference

ISSN: 2379-1748
ISBN: 978-1-56700-430-4

STATE ESTIMATION WITH THE AUXILIARY SAMPLING IMPORTANCE RESAMPLING PARTICLE FILTER FOR THE RADIOFREQUENCY HYPERTHERMIA THERAPY OF CANCER

DOI: 10.1615/TFESC1.bio.013638
pages 2135-2145

Leonardo A. B. Varon
Federal University of Rio de Janeiro PEM/COPPE-UFRJ, Rio de Janeiro, RJ, 21941-972, Brazil University of Santiago de Cali, School of Engineering, street 5 N 62-00, Cali, Colombia

Helcio R. B. Orlande
North Carolina State University; Department of Mechanical Engineering, POLI/COPPE, Federal University of Rio de Janeiro, UFRJ Cid. Universitaria, Cx. Postal: 68503 Rio de Janeiro, RJ, 21941-972 Brazil

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


KEY WORDS: Bayesian framework, Particle filters, Cancer, Hyperthermia, Nanoparticles

Abstract

Radiofrequency (RF) hyperthermia therapy combined with nanoparticles can be used for cancer treatment. In this technique, nanoparticles are introduced in cancerous tissues and with the induction of electromagnetic waves the temperature is locally increased, ideally causing damages only to cancer cells without affecting normal cells. In the study of RF hyperthermia with tumors loaded with nanoparticles, Maxwell's equations with frequency dependence properties are coupled to the bioheat transfer equation, which requires the knowledge of several physical properties that exhibit large variability from individual to individual. In addition, the state variables are difficult to observe or they are unobservable; then, they would need to be estimated indirectly via measurements of other variables in the model, possibly with large uncertainty. In this work, the RF hyperthermia with nanoparticles is treated as an inverse problem of state estimation, based on stochastic evolution and observation models. This problem is solved with the Particle Filter by using the Auxiliary Sampling Importance Resampling (ASIR) algorithm. The results show an excellent agreement between estimated and exact values that can be used to establish protocols and temperature control in cancer treatment by RF hyperthermia with tumors loaded with nanoparticles.

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