Heat transition during magnetic heating treatment: Study with tissue models and simulation
Source abstract
Abstract
Abstract The magnetic heating treatment (MHT) is well known as a promising therapy for cancer diseases. Depending on concentration and specific heating power of the magnetic material as well as on parameters of the magnetic field, temperatures between 43 and 55 °C can be reached. This paper deals with the evaluation of heat distribution around such a heat source in a tissue model, thereby focusing on the heat transfer from tissue enriched with magnetic nanoparticles to regions of no or little enrichment of magnetic nanoparticles. We examined the temperature distribution with several tissue phantoms made of polyurethane (PUR) with similar thermal conductivity coefficient as biological tissue. These phantoms are composed of a cylinder with one sphere embedded, enriched with magnetic fluid. Thereby the spheres have different diameters in order to study the influence of the surface-to-volume ratio. The phantoms were exposed to an alternating magnetic field. The magnetically induced heat increase within the phantoms was measured with thermocouples. Those were placed at defined positions inside the phantoms. Based on the measured results a 3-dimensional simulation of each phantom was built. We achieved an agreement between the measured and simulated temperatures for all phantoms produced in this experimental study. The established experiment theoretically allows a prediction of temperature profiles in tumors and the surrounding tissue for the potential cancer treatment and therefore an optimization of e.g. the respective magnetic nanoparticles concentrations for the desirable rise of temperature.
Source: openaire · Quality: candidate unverified · Source record ↗
Research context
OpenAlex topics: Characterization and Applications of Magnetic Nanoparticles · Advanced Thermodynamics and Statistical Mechanics · Cardiovascular Health and Disease Prevention
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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