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Original paper| Volume 64, P195-203, August 2019

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Experimental characterisation of a proton kernel model for pencil beam scanning techniques

      Highlights

      • Monte-Carlo simulation of a proton pencil beam scanning machine.
      • Dose distributions in water were measured and compared with simulations using a Geant4 Monte Carlo platform (TOPAS).
      • The low-dose envelope of scanned proton beams was characterised.

      Abstract

      The aim of this work is to perform Monte Carlo simulations of a proton pencil beam scanning machine, characterise the low-dose envelope of scanned proton beams and assess the differences between various approximations for nozzle geometry. Measurements and Monte Carlo simulations were carried out in order to describe the dose distribution of a proton pencil beam in water for energies between 100 and 220 MeV. Dose distributions were simulated by using a Geant4 Monte Carlo platform (TOPAS), and were measured in water using a two-dimensional ion chamber array detector. The beam source in air was adjusted for each configuration. Double Gaussian parameterisation was proposed for definition of the beam source model in order to improve simulations starting at the nozzle exit. Absolute dose distributions and field size factors were measured and compared with simulations. The influence of the high-density components present in the treatment nozzle was also investigated by analysis of proton phase spaces at the nozzle exit. An excellent agreement was observed between experimental dose distributions and simulations for energies higher than 160 MeV. However, minor differences were observed between 100 and 160 MeV, suggesting poorer modelling of the beam when the full treatment head was not taken into account. We found that the first ionisation chamber was the main cause of the tail component observed for low proton beam energies. In this work, various parameterisations of proton sources were proposed, thereby allowing reproduction of the low-dose envelope of proton beams and excellent agreement with measured data.

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