Monte Carlo calculation of the photon beam quality correction factor kQ,Q0 for ionization chambers of very small volume: Use of variance reduction techniques driven with an ant colony algorithm

Abstract

Purpose

: To evaluate the effectiveness of an ant colony algorithm in implementing variance reduction techniques in the Monte Carlo computation of the photon beam quality correction factor

for ionization chambers characterized with very small active volumes. Methods:

The Monte Carlo code PENELOPE has been utilized to compute the photon beam quality correction factor for the Semiflex 3D 31021 ionization chamber, which has an active volume of . Various clinical photon beams generated with nominal potentials from 4 to MV have been considered, with a 60Co beam serving as the reference. The calculation involved determining the absorbed dose to both water and the sensitive volume of the ionization chamber. This information was used to derive the factors for the photon beams and the factor for the 60Co beam, whose ratio provides the

factors. Results:

The algorithm has been initially validated by comparing with analog simulations where no variance reduction techniques are applied. The results have demonstrated an efficiency improvement ranging from a factor of 7 to 44. By incorporating the ant colony algorithm along with the variance reduction techniques, the determination of TPR values for various studied photon beams has been achieved. The calculated

factors agree with previously published values. Two distinct protocols outlined in the TRS-398 have been taken into account and the results obtained for these protocols were compared to explore any differences between them. Conclusions:

The ant colony algorithm facilitates the automatic application of variance reduction techniques, such as splitting and Russian roulette, without the need to delve into the geometric intricacies of the simulation. This automated approach results in increased efficiency, enabling simulations to be conducted within reasonable times while maintaining uncertainties at levels that ensure reliability.