Monte Carlo modeling in CT-based geometries: dosimetry for biological modeling experiments with particle beam radiation
Author(s) -
Eric S. Diffenderfer,
D Dolney,
M. Schaettler,
Jenine K. Sanzari,
John E. McDonough,
Keith A. Cengel
Publication year - 2013
Publication title -
journal of radiation research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.643
H-Index - 60
eISSN - 1349-9157
pISSN - 0449-3060
DOI - 10.1093/jrr/rrt118
Subject(s) - monte carlo method , dosimetry , ionizing radiation , radiation , computational physics , physics , absorbed dose , medical physics , computer science , statistical physics , biological system , nuclear medicine , nuclear engineering , nuclear physics , irradiation , medicine , mathematics , statistics , engineering , biology
The space radiation environment imposes increased dangers of exposure to ionizing radiation, particularly during a solar particle event (SPE). These events consist primarily of low energy protons that produce a highly inhomogeneous dose distribution. Due to this inherent dose heterogeneity, experiments designed to investigate the radiobiological effects of SPE radiation present difficulties in evaluating and interpreting dose to sensitive organs. To address this challenge, we used the Geant4 Monte Carlo simulation framework to develop dosimetry software that uses computed tomography (CT) images and provides radiation transport simulations incorporating all relevant physical interaction processes. We found that this simulation accurately predicts measured data in phantoms and can be applied to model dose in radiobiological experiments with animal models exposed to charged particle (electron and proton) beams. This study clearly demonstrates the value of Monte Carlo radiation transport methods for two critically interrelated uses: (i) determining the overall dose distribution and dose levels to specific organ systems for animal experiments with SPE-like radiation, and (ii) interpreting the effect of random and systematic variations in experimental variables (e.g. animal movement during long exposures) on the dose distributions and consequent biological effects from SPE-like radiation exposure. The software developed and validated in this study represents a critically important new tool that allows integration of computational and biological modeling for evaluating the biological outcomes of exposures to inhomogeneous SPE-like radiation dose distributions, and has potential applications for other environmental and therapeutic exposure simulations.
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