Evaluation of the uncertainty associated with the ion recombination correction in high dose-per-pulse electron beam dosimetry: an MC approach
- 25 February 2020
- journal article
- research article
- Published by IOP Publishing in Physics in Medicine & Biology
- Vol. 65 (9), 09NT01
- https://doi.org/10.1088/1361-6560/ab79c2
Abstract
The high dose and dose-per-pulse rates (up to 130 mGy/pulse) produced by some Intraoperative Radiation Therapy (IORT) accelerators pose specific dosimetric problems due to the high density of electric charge per pulse produced in the ionization chamber cavity. In particular, the correction factor for ion recombination, ks calculated with the two-voltage method is significantly overestimated and three alternative models have been proposed in the literature allowing for the presence of a free-electron component. However, at present there is no general consensus on the best model to assess the ion recombination correction and controversy remains on the uncertainty associated with ks. In the present work we adopted a Monte Carlo approach to assess the combined standard uncertainty associated with the ion recombination correction in plane-parallel chambers used in high dose-per-pulse electron beam dosimetry. The uncertainty associated with ks was calculated for the following plane-parallel ionization chambers: Scanditronix/Wellhofer Parallel Plate Chamber PPC05 and PPC40, PTW Advanced Markus Model N34045 and PTW Roos Model 34001. Input variables for MC calculations were derived from experimental data at 28 and 73 mGy/pulse. Taken together, the results of this study indicate that the three ion recombination models do not overlap within their standard uncertainties, indicating that an additional type-B uncertainty component would be necessary to take into account possible differences between the models. Our results indicate that the combined relative standard uncertainty in ks should be calculated as the sum in quadrature of a (type-A) MC based uncertainty component and a (type-B) uncertainty contribution evaluated assuming a uniform distribution between ks values obtained from the two extreme models.This publication has 14 references indexed in Scilit:
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