Abstract

In this work, we investigate heat transfer phenomena in muscle and prostate tissues during magnetic hyperthermia cancer therapy with intravenously-injected maghemite nanoparticles. The resulting suspension of nanoparticles in blood is considered to be a non-Newtonian nanofluid, and a two-phase model that considers Brownian and thermophoretic effects is considered. The geometrical domain is comprised of a blood vessel region, cancerous and non-cancerous (prostate or muscle) tissue regions, and a region of fat tissue. The mixed finite element technique is utilized for solving the governing equations and their associated initial and boundary conditions; triangular quadratic elements are chosen for the temperature, velocity and nanoparticle volume fraction approximations, whereas triangular linear elements are selected for the pressure approximation. Based on this numerical solution, we examine the impact of the nanoparticle volume fraction at the inlet of the blood and magnetic field oscillation frequency on heat transfer and nanoparticle transport. The findings of this study indicate that in the prostate and muscle tissue cases, the nanoparticle distribution non-uniformity and the tissue and blood vessel temperatures are increased, and the average pressure is reduced, by increasing the inlet nanoparticle concentration and magnetic field oscillation frequency. In the muscle tissue case, the mean flow velocity can be increased by increasing the inlet nanoparticle concentration and decreasing the magnetic field oscillation frequency. Based on these findings, recommendations are made for improving the effectiveness of magnetic nanoparticle hyperthermia in cancerous muscle and prostate tissues. The research conducted in this work is motivated by the need for a better understanding of the factors impacting nanoparticle transport in tissues and heat generation during magnetic hyperthermia. Using the mathematical model developed herein, tissue temperatures attained under intravenous magnetic hyperthermia treatment can be accurately predicted.

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