ESTRO 2022 - Abstract Book

S1336

Abstract book

ESTRO 2022

samples. Together with the performance of extremely high spatial resolution LET d measurements in the Bragg peak region, we present the first step of our objective towards a biologically informed radiotherapy treatment approach.

PO-1553 A review of Monte Carlo calculated fQ factors for ionization chambers in clinical proton beams

K. Baumann 1 , C. Gomà 2 , J. Wulff 3 , J. Kretschmer 4 , K. Zink 5

1 University Medical Center Giessen-Marburg, Department of Radiotherapy and Radiooncology, Marburg, Germany; 2 Hospital Clínic de Barcelona, Department of Radiation Oncology, Barcelona, Spain; 3 West German Proton Therapy Centre Essen (WPE), Department of Particle Therapy, Essen, Germany; 4 Carl-von-Ossietzky University Oldenburg, University Clinic for Medical Radiation Physics, Medical Campus Pius Hospital, Oldenburg, Germany; 5 Marburg Ion-Beam Therapy Center, Department of Particle Therapy, Marburg, Germany 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 ( k Q ) 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 k Q 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 f Q factors in clinical proton beams which enter into Monte Carlo calculated k Q factors. We provide average f Q factors and discuss type-B uncertainties. Materials and Methods From f Q factors as published in the literature, weight-averaged f Q factors for different ionization chamber models were derived. The weight of each f Q 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 f Q factor from one Monte Carlo code was reduced if more than one value was available in the literature. Subsequently, the weight-averaged f Q factors as a function of residual range ( R res ) were fitted with a polynomial function of order 2. In order to estimate the type-B uncertainty of weight-averaged f Q factors, a rectangular distribution was assumed. The width of this distribution was defined as the difference between the largest and smallest f Q factor enlarged by their corresponding relative type-A uncertainties. Results In Figure 1 the fits of weight-averaged f Q factors are shown in solid lines as a function of R res for an exemplary cylindrical and plane-parallel ionization chamber. The uncertainty is indicated as dashed lines. Additionally, the original f Q factors from the literature are depicted. For the cylindrical chamber f Q decreases by 0.5% with increasing R res . 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 R res while the codes tend to diverge for larger values of R res . Correspondingly, the uncertainty of weight-averaged f Q factors increases with R res and reach up to 2% (k=1), following the estimation described above.

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

PO-1554 Catching errors by synthetic CT in the clinical workflow of an MR-Linac

J. Li 1 , B. Tang 1 , M. Liu 1 , S. Guo 2 , X. Yao 1 , X. Liao 1 , X. Feng 1 , L. Clara Orlandini 1