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Deep learning proton beam range estimation model for quality assurance based on two-dimensional scintillated light
Eunho Lee1, Byungchul Cho2,3, Jungwon Kwak2
1Department of Radiation Oncology, Yonsei Cancer Center, Seoul, Republic of Korea.
Background:
Many studies have utilized optical camera systems with volumetric scintillators for quality assurances (QA) to estimate the proton beam range. However, previous analytically driven range estimation methods have the difficulty to derive the dose distributions from the scintillation images with quenching and optical effects.
Purpose:
In this study, a deep learning method utilized to QA was used to predict the beam range and spread-out Bragg peak (SOBP) for two-dimensional (2D) map conversion from the scintillation light distribution (LD) into the dose distribution in a water phantom.
Methods:
The 2D residual U-net modeling for deep learning was used to predict the 2D water dose map from a 2D scintillation LD map. Monte Carlo simulations for dataset preparation were performed with varying monoenergetic proton beam energies, field sizes, and beam axis shifts. The LD was reconstructed using photons backpropagated from the aperture as a virtual lens. The SOBP samples were constructed based on monoenergetic dose distributions. The training set, including the validation set, consisted of 8659 image pairs of LD and water dose maps. After training, dose map prediction was performed using a 300 image pair test set generated under random conditions. The pairs of simulated and predicted dose maps were analyzed by Bragg peak fitting and gamma index maps to evaluate the model prediction.
Result:
The estimated beam range and SOBP width resolutions were 0.02 and 0.19 mm respectively for varying beam conditions, and the beam range and SOBP width deviations from the reference simulation result were less than 0.1 and 0.8 mm respectively. The simulated and predicted distributions showed good agreement in the gamma analysis, except for rare cases with failed gamma indices in the proximal and field-marginal regions.
Conclusion:
The deep learning conversion method using scintillation LDs in an optical camera system with a scintillator is feasible for estimating proton beam range and SOBP width with high accuracy.
