Extended in-field and out-of-field validation of a compact Monte Carlo model of an IBA PROTEUS®ONE proton beam in TOPAS/GEANT4

Abstract

Objective: This study evaluates a compact Monte Carlo (MC) model of a pencil beam scanning clinical proton beam using TOPAS to estimate the dose out-of-field (OOF). Compact modelling means that the model starts from a pristine proton beam at the nozzle exit, customised based on acceptance and commissioning data, instead of modelling the full treatment head and room. Approach: First, in-field validation tests were performed. Then, the OOF dose was validated in an RW3 phantom with bubble detectors for personal neutron dosimetry (measuring the neutron dose equivalent) and thermoluminiescent detectors (measuring the absorbed dose by protons and gammas). Measurements were performed at 15 and 35 cm from the distal edge of the field for five different irradiation plans, covering different beam orientations, proton energies and a 40 mm range shifter. TOPAS simulations were performed with QGSP Binary Cascade HP (BIC) and QGSP Bertini HP (Bertini) hadron physics lists. Main results: In-field validation shows that MC simulations agree with point dose measurements within −2.5 % and +1.5 % at locations on- and off-axis and before, in and after the Bragg peak or plateau. The gamma passing rate 2%/3mm of four simulated treatment plans compared to the dose distribution calculated by the TPS exceeds 97 % agreement score. OOF dose simulations showed an average overestimation of 27 % of the neutron dose equivalent for the BIC hadron physics list and an average underestimation of 20 % for the Bertini hadron physics list. The simulated absorbed dose of protons and gammas showed a systematic underestimation which was on average 21 % and 51 % for BIC and Bertini respectively. Significance: Our study demonstrates that a compact MC model can reliably produce in-field data, while out-of-field dose data are within the uncertainties of the detector systems and MC simulations nuclear models, and do so with shorter modelling and faster calculation time.