Fast range-corrected proton dose approximation method using prior dose distribution.

For robust plan optimization and evaluation purposes, one needs a computationally efficient way to calculate dose distributions and dose-volume histograms (DVHs) under various changes in the variables associated with beam delivery and images. In this study, we report an approximate method for rapid calculation of dose when setup errors and anatomical changes occur during proton therapy. This fast dose approximation method calculates new dose distributions under various circumstances based on the prior knowledge of dose distribution from a reference setting. In order to validate the method, we calculated and compared the dose distributions from our approximation method to the dose distributions calculated from a clinically commissioned treatment planning system which was used as the ground truth. The overall accuracy of the proposed method was tested against varying degrees of setup error and anatomical deformation for selected patient cases. The setup error was simulated by rigid shifts of the patient; while the anatomical deformation was introduced using weekly acquired repeat CT data sets. We evaluated the agreement between the dose approximation method and full dose recalculation using a 3D gamma index and the root-mean-square (RMS) and maximum deviation of the cumulative dose volume histograms (cDVHs). The average passing rate of 3D gamma analysis under 3% dose and 3 mm distance-to-agreement criteria were 96% and 89% for setup errors and severe anatomy changes, respectively. The average of RMS and maximum deviation of the cDVHs under the setup error was 0.5% and 1.5%, respectively for all structures considered. Similarly, the average of RMS and maximum deviations under the weekly anatomical change were 0.6% and 2.7%, respectively. Our results show that the fast dose approximation method was able to account for the density variation of the patient due to the setup and anatomical changes with acceptable accuracy while significantly improving the computation time.