A method to reduce the statistical uncertainty caused by high-energy cutoffs in Monte Carlo treatment planning

Abstract

The effects of high electron energy cutoff (ECUT) have been investigated and a method to reduce the dose statistical uncertainty caused by high ECUT was implemented in this work. In EGS4 Monte Carlo simulations, an electron is discarded and its energy is deposited locally when its total energy is lower than ECUT. The deposited energy can be significantly higher than the energy loss calculated using the CSDA model with the corresponding stopping powers in a low-density medium. This will create higher statistical uncertainties in the dose distributions, especially in air and lung tissues. In this work, a new method was implemented to continuously transport a discarded electron without considering multiple scattering or secondary particle generation. The energy loss is calculated based on the mass collision stopping powers in the local medium with an additional energy loss (70%) to account for the effect of approximations made in transporting the electron in a straight line rather than a curved path. The new method can significantly reduce the dose statistical uncertainty and thus improve the simulation efficiency even though the new method requires about 2% more CPU time. Our results showed that the statistical uncertainty of the dose in air cavities of a head-and-neck patient was reduced from 39% to about 2%. The dose distribution for the head and neck patient was significantly improved without losing dose accuracy and simulation efficiency.