Copenhagen, Denmark
Onsite/Online

ESTRO 2022

Session Item

Dosimetry
6034
Poster (digital)
Physics
A review of Monte Carlo calculated fQ factors for ionization chambers in clinical proton beams
Kilian-Simon Baumann, Germany
PO-1553

Abstract

A review of Monte Carlo calculated fQ factors for ionization chambers in clinical proton beams
Authors:

Kilian-Simon Baumann1, Carles Gomà2, Jörg Wulff3, Jana Kretschmer4, Klemens Zink5

1University Medical Center Giessen-Marburg, Department of Radiotherapy and Radiooncology, Marburg, Germany; 2Hospital Clínic de Barcelona, Department of Radiation Oncology, Barcelona, Spain; 3West German Proton Therapy Centre Essen (WPE), Department of Particle Therapy, Essen, Germany; 4Carl-von-Ossietzky University Oldenburg, University Clinic for Medical Radiation Physics, Medical Campus Pius Hospital, Oldenburg, Germany; 5Marburg Ion-Beam Therapy Center, Department of Particle Therapy, Marburg, Germany

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Purpose or Objective

Currently, the IAEA TRS-398 Code of Practice is being updated. The updated version will provide values of beam quality correction factors (kQ) for air-filled ionization chambers in clinical proton beams based on Monte Carlo simulations and experimental data. In recent years, the Monte Carlo codes PENH, GEANT4 and FLUKA have been investigated in terms of their feasibility for dosimetric calculations. It was shown that all three codes can be used to calculate kQ factors to be in agreement with experimentally determined ones on the 1%-level.

In this study, we review the available data on Monte Carlo calculated fQ factors in clinical proton beams which enter into Monte Carlo calculated kQ factors. We provide average fQ factors and discuss type-B uncertainties.

Material and Methods

From fQ factors as published in the literature, weight-averaged fQ factors for different ionization chamber models were derived. The weight of each fQ factor was indirectly proportional to its corresponding relative type-A uncertainty. In general, the amount of available data is not the same for all Monte Carlo codes. To avoid a code-specific bias, the weight of each fQ factor from one Monte Carlo code was reduced if more than one value was available in the literature. Subsequently, the weight-averaged fQ factors as a function of residual range (Rres) were fitted with a polynomial function of order 2.

In order to estimate the type-B uncertainty of weight-averaged fQ factors, a rectangular distribution was assumed. The width of this distribution was defined as the difference between the largest and smallest fQ factor enlarged by their corresponding relative type-A uncertainties.

Results

In Figure 1 the fits of weight-averaged fQ factors are shown in solid lines as a function of Rres for an exemplary cylindrical and plane-parallel ionization chamber. The uncertainty is indicated as dashed lines. Additionally, the original fQ factors from the literature are depicted. For the cylindrical chamber fQ decreases by 0.5% with increasing Rres. For  the plane-parallel chamber it is almost constant with variations of 0.2%.

Additionally, it can be seen that the agreement between the individual Monte Carlo codes is better for small values of Rres while the codes tend to diverge for larger values of Rres. Correspondingly, the uncertainty of weight-averaged fQ factors increases with Rres and reach up to 2% (k=1), following the estimation described above.



Conclusion

Weight-averaged fQ factors were derived from currently published Monte Carlo calculated fQ factors. The Monte Carlo codes show a better agreement for small values of Rres while the codes diverge for larger values of Rres. This might be due to differences in the modelling of nuclear interactions whereas the role of nuclear interactions increases with energy and hence Rres. As a result, overall uncertainties of Monte Carlo calculated fQ factors can be expected to be larger for higher energies.