Denoising of Monte Carlo dose distributions

The Monte Carlo (MC) method has long been viewed as the ultimate dose distribution computational technique, due to its capability of realistically accounting for underlying physical processes and anatomical information. The inherent stochastic fluctuations (i.e., noise) in the resulting dose distributions, however, have several important effects: noise will affect estimations of all the relevant dosimetric and radiobiological indices; noise degrades the resulting visualizations, e.g. isodose plots, which are used to guide treatment planning; and the noise level dictates the computation time. We ask the question whether it is feasible to introduce a post-processing step which reduces statistical fluctuations in the MC dose distribution. We call this process denoising. It may also be referred to as filtering or smoothing. We present a few limited results for a homogeneous and a step-heterogeneity phantom. Our initial investigations support the hypothesis that denoising of MC dose distributions is, to some degree, feasible