Optimizing cone beam CT scatter estimation in egs-cbct for a clinical and virtual chest phantom

  1. Get@NRC: Optimizing cone beam CT scatter estimation in egs-cbct for a clinical and virtual chest phantom (Opens in a new window)
DOIResolve DOI: http://doi.org/10.1118/1.4881142
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Journal titleMedical Physics
Article number71902
Subjectalgorithm; analytical parameters; computed tomography scanner; computer simulation; cone beam computed tomography; controlled study; geometry; imaging phantom; intermethod comparison; light scattering; noise reduction; process optimization; thorax; X ray
AbstractPurpose: 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.
Publication date
PublisherAIP Publishing
AffiliationNational Research Council Canada; Measurement Science and Standards
Peer reviewedYes
NPARC number21272754
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Record identifier5511ac5f-2788-4b42-8a68-13ae7e17eebc
Record created2014-12-03
Record modified2016-05-09
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