An Efficiency Studying of an Ion Chamber Simulation Using Vriance Reduction Techniques with EGSnrc

Radiotherapy is an important technique of cancer treatment using ionizing radiation. The determination of total dose in reference conditions is an important contribution to uncertainty that could achieve 2%. The source of this uncertainty comes from cavity theory that relates the in-air cavity dose...

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Veröffentlicht in:Journal of biomedical physics and engineering 2019-06, Vol.9 (3), p.259-266
Hauptverfasser: L T, Campos, L A, Magalhães, C E V, de Almeida
Format: Artikel
Sprache:eng
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Zusammenfassung:Radiotherapy is an important technique of cancer treatment using ionizing radiation. The determination of total dose in reference conditions is an important contribution to uncertainty that could achieve 2%. The source of this uncertainty comes from cavity theory that relates the in-air cavity dose and the dose to water. These correction factors are determined from Monte Carlo calculations of ionization chambers. The main problem of this type of calculation is the extremely long computation time to achieve reasonable statistics. The main purpose of this work is to present a combination with variance reduction techniques for the case of an ionization chamber in water. The egs_chamber code allows for very efficient computation of ionization chamber doses and dose ratios by using various variance reduction techniques, and also permits realistic simulations of the experimental setup due to the use of EGSnrc C++ library. Russian roulette and Photon Cross Section Enhancement were used with egs_chamber code. Tests were performed to obtain the parameters of variance reduction techniques resulting in a maximum efficiency. It can be seen that the parameters which result in improved Monte Carlo calculation of the efficiency values are XCSE 64 and Russian Roulette (RR) 128. This study determines the parameters of variance reduction techniques that result in an optimal computational efficiency.
ISSN:2251-7200
2251-7200
DOI:10.31661/jbpe.v9i3jun.682