Evaluation of the uncertainty associated with the ion recombination correction in high dose-per-pulse electron beam dosimetry: an MC approach. (28th May 2020)

• Record Type: Journal Article
• Authors: D'Arienzo, Marco; Andreoli, Stefano; Pimpinella, Maria
• Is Part Of: Physics in medicine & biology. Volume 65:Number 9(2020:May)
• Publication Date: 2020-05-28
• Subjects: recombination correction -- high dose-per-pulse beams -- parallel plate ionization chambers -- IORT -- Monte Carlo; Biophysics -- Periodicals; Medical physics -- Periodicals; 610.153
• DOI: 10.1088/1361-6560/ab79c2
• Languages: English
• ISSNs: 0031-9155

Abstract

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 traditional 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 k s . In the present work we adopted a Monte Carlo (MC) approach to assess the uncertainty associated with the ion recombination correction in plane-parallel chambers used in high dose-per-pulse electron beam dosimetry. The uncertainty associated with k s was calculated for the following plane-parallel ionization chambers: Scanditronix/Wellhofer Parallel Plate Chamber PPC05 and PPC40, PTW Advanced Markus Model 34 045 and PTW Roos Model 34 001. 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 ks values calculated according to the three ion recombination models do not overlap within their standard uncertainties, suggesting 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 k s 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 k s values obtained from the two extreme models.