title: On the de-noising of Monte Carlo calculated dose distributions.
abstract: This paper presents an algorithm for de-noising Monte Carlo calculated dose distributions for use in radiation treatment planning. The algorithm is a three-dimensional generalization of a Savitzky-Golay digital filter and uses an adaptive smoothing window size to reduce the probability for systematic bias. The paper also introduces five accuracy criteria that are relevant for the expected clinical use of Monte Carlo techniques, which can be used to evaluate the performance of smoothing algorithms. Using these accuracy criteria it is demonstrated that the smoothing algorithm presented here decreases the uncertainty of Monte Carlo calculated dose distributions. The corresponding decrease in necessary particle tracks ranges from a factor of 2 to a factor of 20, depending on the accuracy criterion used. It is shown that very short Monte Carlo simulations combined with smoothing deliver satisfactory dose distributions and may therefore be extremely valuable for the initial trial and error phase of the radiation treatment planning process.
mesh_terms: D000388:Air; D000465:Algorithms; D000535:Aluminum; D016477:Artifacts; D008954:Models, Biological; D015233:Models, Statistical; D009010:Monte Carlo Method; D011786:Quality Control; D011874:Radiometry / Q000379:methods*; D011879:Radiotherapy Dosage; D011880:Radiotherapy Planning, Computer-Assisted / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted; D013269:Stochastic Processes; D014867:Water
chemical_list: D014867:Water; D000535:Aluminum