Technical Note: validation of a material assignment method for a retrospective study of carbon-ion radiotherapy using Monte Carlo simulation

We propose a two-step method to converse human tissue materials from patient computed tomography (CT) images, which is required in dose reconstructions for a retrospective study of carbon-ion radiotherapy (CIRT) using Monte Carlo (MC) simulation. The first step was to assign the standard tissues of the International Commission on Radiological Protection reference phantoms according to the CT-number. The second step was to determine the mass density of each material based on the relationship between CT-number and stopping power ratio (Hounsfield unit [HU]-SPR) registered in treatment planning system (TPS). Direct implementation of the well-calibrated HU-SPR curve allows the reproduction of previous clinical treatments recorded in TPS without uncertainty due to a mismatch of the CT scanner or scanning conditions, whereas MC simulation with realistic human tissue materials can fulfill the out-of-field dose, which was missing in the record. To validate our proposed method, depth-dose distributions in the homogenous and heterogeneous phantoms irradiated by a 400 MeV/u carbon beam with an 8 cm spread-out Bragg peak (SOBP) were computed by the MC simulation in combination with the proposed methods and compared with those of TPS. Good agreement of the depth-dose distributions between the TPS and MC simulation (within a 1% discrepancy in range) was obtained for different materials. In contrast, fluence distributions of secondary particles revealed the necessity of MC simulation using realistic human tissue. The proposed material assignment method will be used for a retrospective study using previous clinical data of CIRT at the National Institute of Radiological Sciences (NIRS).     

How air influences radiation dose deposition in multiwell culture plates: a Monte Carlo simulation of radiation geometry

Radiation of experimental culture cells on plates with various wells can cause a risk of underdosage as a result of the existence of multiple air–water interfaces. The objective of our study was to quantify this error in culture plates with multiple wells. Radiation conditions were simulated with the GAMOS code, based on the GEANT4 code, and this was compared with a simulation performed with PENELOPE and measured data. We observed a slight underdosage of ∼4% on the most superficial half of the culture medium. We believe that this underdosage does not have a significant effect on the dose received by culture cells deposited in a monolayer and adhered to the base of the wells.     

Monte Carlo modeling in CT-based geometries: dosimetry for biological modeling experiments with particle beam radiation

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.     

Modelling personal exposure to particulate air pollution: An assessment of time-integrated activity modelling,Monte Carlo simulation & artificial neural network approaches

An experimental assessment of personal exposure to PM₁₀ in 59 office workers was carried out in Dublin, Ireland. 255 samples of 24-h personal exposure were collected in real time over a 28 month period. A series of modelling techniques were subsequently assessed for their ability to predict 24-h personal exposure to PM₁₀. Artificial neural network modelling, Monte Carlo simulation and time–activity based models were developed and compared. The results of the investigation showed that using the Monte Carlo technique to randomly select concentrations from statistical distributions of exposure concentrations in typical microenvironments encountered by office workers produced the most accurate results, based on 3 statistical measures of model performance. The Monte Carlo simulation technique was also shown to have the greatest potential utility over the other techniques, in terms of predicting personal exposure without the need for further monitoring data. Over the 28 month period only a very weak correlation was found between background air quality and personal exposure measurements, highlighting the need for accurate models of personal exposure in epidemiological studies.     

Application of Mathematical Models in Combination with Monte Carlo Simulation for Prediction of Isoflurane Concentration in an Operation Room Theater

Applicability of two mathematical models in inhalation exposure prediction (well mixed room and near field-far field model) were validated against standard sampling method in one operation room for isoflurane. Ninety six air samples were collected from near and far field of the room and quantified by gas chromatography-flame ionization detector. Isoflurane concentration was also predicted by the models. Monte Carlo simulation was used to incorporate the role of parameters variability. The models relatively gave more conservative results than the measurements. There was no significant difference between the models and direct measurements results. There was no difference between the concentration prediction of well mixed room model and near field far field model. It suggests that the dispersion regime in room was close to well mixed situation. Direct sampling showed that the exposure in the same room for same type of operation could be up to 17 times variable which can be incorporated by Monte Carlo simulation. Mathematical models are valuable option for prediction of exposure in operation rooms. Our results also suggest that incorporating the role of parameters variability by conducting Monte Carlo simulation can enhance the strength of prediction in occupational hygiene decision making.     

The choice of prior distribution for a covariance matrix in multivariate meta‐analysis: a simulation study

Bayesian meta‐analysis is an increasingly important component of clinical research, with multivariate meta‐analysis a promising tool for studies with multiple endpoints. Model assumptions, including the choice of priors, are crucial aspects of multivariate Bayesian meta‐analysis (MBMA) models. In a given model, two different prior distributions can lead to different inferences about a particular parameter. A simulation study was performed in which the impact of families of prior distributions for the covariance matrix of a multivariate normal random effects MBMA model was analyzed. Inferences about effect sizes were not particularly sensitive to prior choice, but the related covariance estimates were. A few families of prior distributions with small relative biases, tight mean squared errors, and close to nominal coverage for the effect size estimates were identified. Our results demonstrate the need for sensitivity analysis and suggest some guidelines for choosing prior distributions in this class of problems. The MBMA models proposed here are illustrated in a small meta‐analysis example from the periodontal field and a medium meta‐analysis from the study of stroke. Copyright © 2015 John Wiley & Sons, Ltd.