A novel multilayer MV imager computational model for component optimization
A novel Megavoltage (MV) multilayer imager (MLI) design featuring higher detective quantum efficiency and lower noise than current conventional MV imagers in clinical use has been recently reported. Optimization of the MLI design for multiple applications including tumor tracking, MV-CBCT and portal dosimetry requires a computational model that will provide insight into the physics processes that affect the overall and individual components' performance. The purpose of the current work was to develop and validate a comprehensive computational model that can be used for MLI optimization.Methods:
The MLI model was built using the Geant4 Application for Tomographic Emission (GATE) application. The model includes x-ray and charged-particle interactions as well as the optical transfer within the phosphor. A first prototype MLI device featuring a stack of four detection layers was used for model validation. Each layer of the prototype contains a copper buildup plate, a phosphor screen and photodiode array. The model was validated against measured data of Modulation Transfer Function (MTF), Noise-Power Spectrum (NPS), and Detective Quantum Efficiency (DQE). MTF was computed using a slanted slit with 2.3° angle and 0.1 mm width. NPS was obtained using the autocorrelation function technique. DQE was calculated from MTF and NPS data. The comparison metrics between simulated and measured data were the Pearson's correlation coefficient (r) and the normalized root-mean-square error (NRMSE).Results:
Good agreement between measured and simulated MTF and NPS values was observed. Pearson's correlation coefficient for the combined signal from all layers of the MLI was equal to 0.9991 for MTF and 0.9992 for NPS; NRMSE was 0.0121 for MTF and 0.0194 for NPS. Similarly, the DQE correlation coefficient for the combined signal was 0.9888 and the NRMSE was 0.0686.Conclusions:
A comprehensive model of the novel MLI design was developed using the GATE toolkit and validated against measured MTF, NPS, and DQE data acquired with a prototype device featuring four layers. This model will be used for further optimization of the imager components and configuration for clinical radiotherapy applications.