Voxel2MCNP is a program that sets up radiation protection scenarios with voxel models and generates corresponding input files for the Monte Carlo code MCNPX. Its technology is based on object-oriented programming, and the development is platform-independent. It has a user-friendly graphical interface including a two- and three-dimensional viewer. A row of equipment models is implemented in the program. Various voxel model file formats are supported. Applications include calculation of counting efficiency of in vivo measurement scenarios and calculation of dose coefficients for internal and external radiation scenarios. Moreover, anthropometric parameters of voxel models, for instance chest wall thickness, can be determined. Voxel2MCNP offers several methods for voxel model manipulations including image registration techniques. The authors demonstrate the validity of the program results and provide references for previous successful implementations. The authors illustrate the reliability of calculated dose conversion factors and specific absorbed fractions. Voxel2MCNP is used on a regular basis to generate virtual radiation protection scenarios at Karlsruhe Institute of Technology while further improvements and developments are ongoing.

The basic idea of Voxel2MCNP is to provide a framework supporting users in modeling radiation transport scenarios using voxel phantoms and other geometric models, generating corresponding input for the Monte Carlo code MCNPX, and evaluating simulation output. Applications at Karlsruhe Institute of Technology are primarily whole and partial body counter calibration and calculation of dose conversion coefficients. A new generic data model describing data related to radiation transport, including phantom and detector geometries and their properties, sources, tallies and materials, has been developed. It is modular and generally independent of the targeted Monte Carlo code. The data model has been implemented as an XML-based file format to facilitate data exchange, and integrated with Voxel2MCNP to provide a common interface for modeling, visualization, and evaluation of data. Also, extensions to allow compatibility with several file formats, such as ENSDF for nuclear structure properties and radioactive decay data, SimpleGeo for solid geometry modeling, ImageJ for voxel lattices, and MCNPX's MCTAL for simulation results have been added. The framework is presented and discussed in this paper and example workflows for body counter calibration and calculation of dose conversion coefficients is given to illustrate its application.

Zusammenfassung AbstractBody counting is a method for in vivo activity assessment applied to the monitoring of people with high risk of radionuclide incorporation. Energy-sensitive radiation detectors are arranged relative to the body to quantify radionuclide deposits in anatomical structures, such as lungs, liver and skeleton. This method depends on the specific detection system and is sensitive to the individual anatomy of the person. Accurate activity estimates, which are the basis for dose calculation, require extensive calibration procedures typically involving experimental measurements of anthropomorphic phantoms conforming to a reference person. Current calibration methods offer personalisation for lung and liver counting only with respect to body mass and height and do not specify uncertainties.This work revises and extends the currently applied personalisation methods using radiation transport simulation in combination with computational phantoms derived from medical imaging data. A framework was developed that allows computation of samples of calibration factors for various anatomies in standard measurement setups and anthropometric parameters quantifying anatomic properties. Those samples are applied to create statistical models to derive personalised calibration factors given specific values of anthropometric parameters measured on the person. This gives better estimates in activity assessment and, thereby, dose calculation while quantifying and reducing uncertainties.The framework was implemented in form of an abstract, modular data model, a software tool for modelling, simulation and evaluation of general body counting scenarios, and a statistical analysis method for correlating anthropometric parameters and calibration factors. This allows efficient and reproducible modelling for virtual measurement reconstruction as well as sensitivity analyses. The framework was applied to the calibration of the In Vivo Measurement Laboratory (IVM) at Karlsruhe Institute of Technology (KIT) comprising four freely arrangeable high-purity germanium detectors in lung, liver, knee and head measurement setups. Because of the interindividual anatomical variations in the applied phantoms and iv additional sensitivity analyses, it was possible to give estimates of the expected uncertainties and to reduce them through an algorithmically reproducible approach on calibration.

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