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主页 旧刊 有关人员 未来大会 American Society of Thermal and Fluids Engineering
Second Thermal and Fluids Engineering  Conference

ISSN: 2379-1748

APPROXIMATION ERROR MODEL FOR STATE ESTIMATION IN THE RADIOFREQUENCY HYPERTHERMIA TREATMENT OF CANCER

Leonardo A. Bermeo 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
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/TFEC2017.mnp.018460
pages 885-896

摘要

The hyperthermia treatment of cancer induced by electromagnetic waves in the radiofrequency range, with nanoparticles loaded in the tumor region, has been recently gaining the interest of different research groups. As for other kinds of cancer therapy, a successful hyperthermia treatment requires appropriate and individualized planning for each patient, based not only on medical images but also on complex mathematical formulations of multiphysics problems. Such planning is required to avoid thermal damage to the healthy cells and is performed under conditions of large uncertainties caused, for example, by the large variability exhibited by thermophysical properties of human tissues, which can vary from individual to individual and even for the same individual under different physiological conditions. Moreover, temperature measurements might be available during the hyperthermia treatment, thus allowing that real-time numerical simulations under the effects of uncertainties be treated as a state estimation problems. In this paper, a reduced model is implemented for the solution of the state estimation problem, by statistically taking into account the errors between the complete and the reduced mathematical models with the application of the Approximation Error Model (AEM).

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