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Analysis of electromagnetic heating in magnetic fluid deep hyperthermia

Authors: Eugeniusz Kurgan; Piotr Gas;

Analysis of electromagnetic heating in magnetic fluid deep hyperthermia

Abstract

In this paper, we describe the numerical modeling of magnetically induced heating of tissues in magnetic fluid deep hyperthermia. When placed in sinusoidal varying electromagnetic field, the Brownian particle rotation and Neel relaxation mechanisms induce in ferrofluid heat generation. In order to destroy tumors cells while preventing healthy tissues, such parameters as susceptibility of nanoparticles, exciting current parameters and intensity of magnetic fields should be calculated precisely. In this publication, coupled field problem resulting from Maxwell’s equation and Penne’s bioheat equation is discussed. Both sets of equations are solved using the finite element analysis method. We use a simplify model of human thorax with blood perfusion mechanism and tu-mor placed in it. Heat generation source in tumor is obtained by ferromagnetic nanoparticles. Magnetic and temperature field distribution is discussed in context of optimal tumor treatment.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
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