The effects of mapping CT images to Monte Carlo materials on GEANT4 proton simulation accuracy

Monte Carlo simulations of radiation therapy require conversion from Hounsfield units (HU) in CT images to an exact tissue composition and density. The number of discrete densities (or density bins) used in this mapping affects the simulation accuracy, execution time, and memory usage in GEANT4 and other Monte Carlo code. The relationship between the number of density bins and CT noise was examined in general for all simulations that use HU conversion to density. Additionally, the effect of this on simulation accuracy was examined for proton radiation.

Relative uncertainty from CT noise was compared with uncertainty from density binning to determine an upper limit on the number of density bins required in the presence of CT noise. Error propagation analysis was also performed on continuously slowing down approximation range calculations to determine the proton range uncertainty caused by density binning. These results were verified with Monte Carlo simulations.

In the presence of even modest CT noise (5 HU or 0.5%) 450 density bins were found to only cause a 5% increase in the density uncertainty (i.e., 95% of density uncertainty from CT noise, 5% from binning). Larger numbers of density bins are not required as CT noise will prevent increased density accuracy; this applies across all types of Monte Carlo simulations. Examining uncertainty in proton range, only 127 density bins are required for a proton range error of <0.1 mm in most tissue and <0.5 mm in low density tissue (e.g., lung).

By considering CT noise and actual range uncertainty, the number of required density bins can be restricted to a very modest 127 depending on the application. Reducing the number of density bins provides large memory and execution time savings in GEANT4 and other Monte Carlo packages.