Rune Slot Thing1, Ernesto Mainegra-Hing2. 1. Institute of Clinical Research, University of Southern Denmark, Odense 5000, Denmark and Laboratory of Radiation Physics, Odense University Hospital, Odense 5000, Denmark. 2. Ionizing Radiation Standards, National Research Council of Canada, Ottawa K1A 0R6, Canada.
Abstract
PURPOSE: Cone beam computed tomography (CBCT) image quality suffers from contamination from scattered photons in the projection images. Monte Carlo simulations are a powerful tool to investigate the properties of scattered photons.egs_cbct, a recent EGSnrc user code, provides the ability of performing fast scatter calculations in CBCT projection images. This paper investigates how optimization of user inputs can provide the most efficient scatter calculations. METHODS: Two simulation geometries with two different x-ray sources were simulated, while the user input parameters for the efficiency improving techniques (EITs) implemented inegs_cbct were varied. Simulation efficiencies were compared to analog simulations performed without using any EITs. Resulting scatter distributions were confirmed unbiased against the analog simulations. RESULTS: The optimal EIT parameter selection depends on the simulation geometry and x-ray source. Forced detection improved the scatter calculation efficiency by 80%. Delta transport improved calculation efficiency by a further 34%, while particle splitting combined with Russian roulette improved the efficiency by a factor of 45 or more. Combining these variance reduction techniques with a built-in denoising algorithm, efficiency improvements of 4 orders of magnitude were achieved. CONCLUSIONS: Using the built-in EITs inegs_cbct can improve scatter calculation efficiencies by more than 4 orders of magnitude. To achieve this, the user must optimize the input parameters to the specific simulation geometry. Realizing the full potential of the denoising algorithm requires keeping the statistical uncertainty below a threshold value above which the efficiency drops exponentially.
PURPOSE: Cone beam computed tomography (CBCT) image quality suffers from contamination from scattered photons in the projection images. Monte Carlo simulations are a powerful tool to investigate the properties of scattered photons.egs_cbct, a recent EGSnrc user code, provides the ability of performing fast scatter calculations in CBCT projection images. This paper investigates how optimization of user inputs can provide the most efficient scatter calculations. METHODS: Two simulation geometries with two different x-ray sources were simulated, while the user input parameters for the efficiency improving techniques (EITs) implemented inegs_cbct were varied. Simulation efficiencies were compared to analog simulations performed without using any EITs. Resulting scatter distributions were confirmed unbiased against the analog simulations. RESULTS: The optimal EIT parameter selection depends on the simulation geometry and x-ray source. Forced detection improved the scatter calculation efficiency by 80%. Delta transport improved calculation efficiency by a further 34%, while particle splitting combined with Russian roulette improved the efficiency by a factor of 45 or more. Combining these variance reduction techniques with a built-in denoising algorithm, efficiency improvements of 4 orders of magnitude were achieved. CONCLUSIONS: Using the built-in EITs inegs_cbct can improve scatter calculation efficiencies by more than 4 orders of magnitude. To achieve this, the user must optimize the input parameters to the specific simulation geometry. Realizing the full potential of the denoising algorithm requires keeping the statistical uncertainty below a threshold value above which the efficiency drops exponentially.