Commissioning dose computation model for proton source in pencil beam scanning therapy by convolution neural networks

Abstract

Objective. Proton source model commissioning (PSMC) is critical for ensuring accurate dose calculation in pencil beam scanning (PBS) proton therapy using Monte Carlo (MC) simulations. PSMC aims to match the calculated dose to the delivered dose. However, commissioning the ‘nominal energy’ and ‘energy spread’ parameters in PSMC can be challenging, as these parameters cannot be directly obtained from solving equations. To efficiently and accurately commission the nominal energy and energy spread in a proton source model, we developed a convolution neural network (CNN) named ‘PSMC-Net.’ Methods. The PSMC-Net was trained separately for 33 energies (E, 70–225 MeV with a step of 5 MeV plus 226.09 MeV). For each E, a dataset was generated consisting of 150 source model parameters (15 nominal energies ∈ [E, E + 1.5 MeV], ten spreads ∈ [0, 1]) and the corresponding 150 MC integrated depth doses (IDDs). Of these 150 data pairs, 130 were used for training the network, 10 for validation, and 10 for testing. Results. The source model, built by 33 measured IDDs and 33 PSMC-Nets (cost 0.01 s), was used to compute the MC IDDs. The gamma passing rate (GPRs, 1 mm/1%) between MC and measured IDDs was 99.91 ± 0.12%. However, when no commissioning was made, the corresponding GPR was reduced to 54.11 ± 22.36%, highlighting the tremendous significance of our CNN commissioning method. Furthermore, the MC doses of a spread-out Bragg peak and 20 patient PBS plans were also calculated, and average 3D GPRs (2 mm/2% with a 10% threshold) were 99.89% and 99.96 ± 0.06%, respectively. Significance. We proposed a nova commissioning method of the proton source model using CNNs, which made the PSMC process easy, efficient, and accurate.