Development and validation of an automatic commissioning tool for the Monte Carlo dose engine in myQA iON. (March 2022) - Cohilis, M.; Hong, L.; Janssens, G.; Rossomme, S.; Sterpin, E.; Lee, J.A.; Souris, K.
Highlights: Building beam models for the Monte Carlo dose engine of myQA iON (IBA Dosimetry). Commissioning automation will ease the integration of independent dose engines. Fast modes allow the tool to run even on low performance computers. Very good accuracy obtained in the validation of a beam model for clinical practice. Abstract: Independent dose verification with Monte Carlo (MC) simulations is an important feature of proton therapy quality assurance (QA). However, clinical integration of such tools often generates an additional and complex workload for medical physicists. The preparation of the necessary clinical inputs, such as the machine beam model, should therefore be automated. In this work, a methodology for automatic MC commissioning has been devised, validated, and developed into a MATLAB tool for the users of myQA iON, the recent QA platform of IBA Dosimetry. With this workflow, all necessary parameters can easily be tuned using dedicated optimization methods. For the geometrical beam parameters (phase space), the assumption of a single or double Gaussian is made. To model the energy spectrum, a Gaussian function is assumed and parameters are optimized using either MC simulations or a library of pre-computed Bragg peaks. For the absolute dose calibration, commissioning fields can be reproduced with the dose engine to retrieve the necessary parameters. We discuss in a first time the tool efficiency and show that one can optimize all parameters in less than 4 min per energy with excellent accuracy. We then validate a beam model obtained with the tool by simulating homogeneous spread-out Bragg peaks (SOBPs) and patient QA plans previously measured in water. An average range agreement of 0.29 ± 0.34 mm is achieved for the SOBPs while 3%/3 mm local gamma passing rates reach 99.3% on average over all 62 measured patient QA planes, which is well within clinical tolerances.