Abstract
Dose-volume histograms (DVHs) of the dose distributions calculated by theMonte Carlo method contain statistical uncertainties. The Monte Carlo DVH canbe considered as blurred from the noiseless DVH by the statisticaluncertainty. The focus of the present work is on the removal of thestatistical uncertainty effect on the Monte Carlo DVHs and the reconstructionof the noiseless DVHs. We first study the effect of statistical uncertainty.It is found that the steeper the DVH, the more significant the effect. Fortypical critical structure DVHs the effect is usually negligible. For thetarget DVHs the effect could be clinically significant, depending on thevalue of uncertainty and the slope of the DVH. We then propose an iterativereconstruction algorithm. Using the DVHs and statistical uncertainties fromthe Monte Carlo simulations, we are able to reconstruct the noiseless DVHs. Ahypothetical example and a number of clinical cases have been used to test theproposed algorithm. For each clinical case, two Monte Carlo simulations(denoted A and B) were performed. Simulation A has very large statisticaluncertainties (about 10% of dose in the target volume) while simulation B hasvery small uncertainties (about 1%). DVHs from simulation B were used toapproximate the noiseless DVHs. Using the proposed algorithm, the effect ofstatistical uncertainty can be removed from the DVHs of simulation A. Thereconstructed DVHs were in good agreement with the DVHs from simulation B. Theproposed approach is expected to be useful in removing the blurring effect ona quickly calculated Monte Carlo DVH when performing the iterative forwardtreatment planning.
Published Version
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