Purpose: Modern radiotherapy delivers radiation doses to targets within a few minutes using intricate multiple-beam segments determined with multi-leaf collimators (MLC). Therefore, we propose a scintillator-based dosimetry system capable of assessing the dosimetric and mechanical performance of intensity-modulated radiotherapy (IMRT) and volumetric-modulated arc therapy (VMAT) in real time.
Methods: The dosimeter was equipped with a scintillator plate and two digital cameras. The dose distribution was generated by applying deep learning-based signal processing to correct the intrinsic characteristics of the camera sensor and a tomographic image reconstruction technique to rectify the geometric distortion of the recorded video. Dosimetric evaluations were performed using a gamma analysis against a two-dimensional array and radiochromic film measurements for 20 clinical cases. The average difference in the MLC position measurements and machine log files was tested for the applicability of the mechanical quality assurance (QA) of MLCs.
Results: The agreement of the dose distribution in the IMRT and VMAT plans was clinically acceptable between the proposed system and conventional dosimeters. The average differences in the MLC positions for the IMRT/VMAT plans were 1.7010/2.8107 mm and 1.4722/2.7713 mm in banks A and B, respectively.
Conclusions: In this study, we developed an instantaneously interpretable real-time dosimeter for QA in a medical linear accelerator using a scintillator plate and digital cameras. The feasibility of the proposed system was investigated using dosimetric and mechanical evaluations in the IMRT and VMAT plans. The developed system has clinically acceptable accuracy in both the dosimetric and mechanical QAs of the IMRT and VMAT plans.

KeywordsDosimeter, Real-time, Deep learning, Dose rate, Multi-leaf collimators