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Optimizing cone beam CT scatter estimation in egs_cbct for a clinical and virtual chest phantom
Author(s) -
Thing Rune Slot,
MainegraHing Ernesto
Publication year - 2014
Publication title -
medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 180
eISSN - 2473-4209
pISSN - 0094-2405
DOI - 10.1118/1.4881142
Subject(s) - imaging phantom , variance reduction , monte carlo method , cone beam computed tomography , projection (relational algebra) , image quality , computer science , photon , physics , algorithm , optics , computer vision , mathematics , computed tomography , statistics , image (mathematics) , medicine , radiology
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 in egs_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 in egs_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.