Abstract. BACKGROUND: Induction heating devices using the induction coil and magnetic nanoparticles (MNPs) are the way that the magnetic hyperthermia is heading. OBJECTIVE: To facilitate the induction heating of in vivo magnetic nanoparticles in hyperthermia experiments on large animals. METHODS: An induction heating device using a planar coil was designed with a magnetic field frequency of 328 kHz. The coil's magnetic field distribution and the device's induction heating performance on different concentrations of magnetic nanoparticles were measured. RESULTS: The alternating magnetic field produced in the axis position 165 mm away from the coil center is 40 Gs in amplitude; magnetic nanoparticles with a concentration higher than 80 mg. mL −1 can be heated up rapidly. CONCLUSION: Our results demonstrate that the device can be applied not only to in vitro and in small animal experiments of magnetic hyperthermia using MNPs, but also in large animal experiments.
Purpose. Monte Carlo (MC) track structure codes are commonly used for predicting energy deposition and radiation-induced DNA damage at the nanometer scale. Various simulation parameters such as physics model, DNA model, and direct damage threshold have been developed. The differences in adopted parameters lead to disparity in calculation results, which requires quantitative evaluation. Methods. Three simulation configurations were implemented in TOPAS-nBio MC toolkit to investigate the impact of physics models, DNA model, and direct damage threshold on the prediction of energy deposition and DNA damage. Dose point kernels (DPKs) of electrons and nanometer-sized volumes irradiated with electrons, protons, and alpha particles were utilized to evaluate the impact of physics models on energy deposition. Proton irradiation of plasmid DNA was used to investigate the disparity in single-strand break and double-strand break (DSB) yields caused by differences in physics models, DNA models, and direct damage thresholds. Results. Electron DPKs obtained with different physics models show similar trends but different diffusiveness and maximums. Energy deposition distributions in nanometer-sized volumes irradiated with electrons, protons, and alpha particles calculated using different physics models have the same trend although discrepancies can be observed at the lowest and highest energy deposits. Strand breaks from incident protons in DNA plasmids vary with adopted parameters. For the configurations in this study, changing physics model, DNA model, and direct damage threshold can cause differences of up to 57%, 69%, and 15% in DSB yields, respectively. All these simulation results are essentially in agreement with previously published simulation or experimental studies. Conclusion. All the physics models, DNA models, and direct damage thresholds investigated in this study are applicable to predict energy deposition and DNA damage. Although the choice of parameters can lead to disparity in simulation results, which serves as a reference for future studies.
Abstract. In order to ensure the safety and effectiveness of magnetic induction hyperthermia in clinical applications, numerical simulations on the temperature distributions and extent of thermal damage to the targeted regions must be conducted in the preoperative treatment planning system. In this paper, three models, including a thermoseed thermogenesis model, tissue heat transfer model, and tissue thermal damage model, were established based on the four-dimensional energy field, temperature field, and thermal damage field distributions exhibited during hyperthermia. In addition, a numerical simulation study was conducted using the Finite Volume Method (FVM), and the accuracy and reliability of the magnetic induction hyperthermia model and its numerical calculations were verified using computer simulations and experimental results. Thus, this study promoted the application of computing methods to magnetic induction therapy and conformal hyperthermia, and improved the accuracy of the temperature field and tissue thermal damage distribution predictions.
This study aims to develop a method for verifying site-specific and/or beam path specific proton beam range, which could reduce range uncertainty margins and the associated treatment complications. It investigates the range uncertainties from both CT HU to relative stopping power conversion and patient positioning errors for prostate treatment using pelvic-like biological phantoms. Three 25 × 14 × 12 cm3 phantoms, made of fresh animal tissues mimicking the pelvic anatomies of prostate patients, were scanned with a general electric CT simulator. A 22 cm circular passive scattering beam with 29 cm range and 8 cm modulation width was used to measure the water equivalent path lengths (WEPL) through the phantoms at multiple points using the dose extinction method with a MatriXXPT detector. The measured WEPLs were compared to those predicted by TOPAS simulations and ray-tracing WEPL calculations. For the three phantoms, the WEPL differences between measured and theoretical prediction (WDMT) are below 1.8% for TOPAS, and 2.5% for ray-tracing. WDMT varies with phantom anatomies by about 0.5% for both TOPAS and ray-tracing. WDMT also correlates with the tissue types of a specific treated region. For the regions where the proton beam path is parallel to sharp bone edges, the WDMTs of TOPAS and ray-tracing respectively reach up to 1.8% and 2.5%. For the region where proton beams pass through just soft tissues, the WDMT is mostly less than 1% for both TOPAS and ray-tracing. For prostate treatments, range uncertainty depends on the tissue types within a specific treated region, patient anatomies and the range calculation methods in the planning algorithms. Our study indicates range uncertainty is less than 2.5% for the whole treated region with both ray-tracing and TOPAS, which suggests the potential to reduce the current 3.5% range uncertainty margin used in the clinics by at least 1% even for single-energy CT data.
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