Citations

PurposeThe presence of respiratory motion during radiation treatment leads to degradation of the expected dose distribution, both for target coverage and healthy tissue sparing, particularly for techniques like pencil beam scanning proton therapy which have dynamic delivery systems. While tools exist to estimate this degraded four‐dimensional (4D) dose, they typically have one or more deficiencies such as not including the particular effects from a dynamic delivery, using analytical dose calculations, and/or using nonphysical dose‐accumulation methods. This work presents a clinically useful 4D‐dose calculator that addresses each of these shortcomings.MethodsTo quickly compute the 4D dose, the three main tasks of the calculator were run on graphics processing units (GPUs). These tasks were (a) simulating the delivery of the plan using measured delivery parameters to distribute the plan amongst 4DCT phases characterizing the patient breathing, (b) using an in‐house Monte Carlo simulation (MC) dose calculator to determine the dose delivered to each breathing phase, and (c) accumulating the doses from the various breathing phases onto a single phase for evaluation. The accumulation was performed by individually transferring the energy and mass of dose‐grid subvoxels, a technique that models the transfer of dose in a more physically realistic manner. The calculator was run on three test cases, with lung, esophagus, and liver targets, respectively, to assess the various uncertainties in the beam delivery simulation as well as to characterize the dose‐accumulation technique.ResultsFour‐dimensional doses were successfully computed for the three test cases with computation times ranging from 4–6 min on a server with eight NVIDIA Titan X graphics cards; the most time‐consuming component was the MC dose engine. The subvoxel‐based dose‐accumulation technique produced stable 4D‐dose distributions at subvoxel scales of 0.5–1.0 mm without impairing the total computation time. The uncertainties in the beam delivery simulation led to moderate variations of the dose–volume histograms for these cases; the variations were reduced by implementing repainting or phase‐gating motion mitigation techniques in the calculator.ConclusionsA MC‐based and GPU‐accelerated 4D‐dose calculator was developed to estimate the effects of respiratory motion on pencil beam scanning proton therapy treatments. After future validation, the calculator could be used to assess treatment plans and its quick runtime would make it easily usable in a future 4D‐robust optimization system.

PurposeAccuracy of dose calculation models and robustness under various uncertainties are key factors influencing the quality of intensity modulated proton therapy (IMPT) plans. To mitigate the effects of uncertainties and to improve the dose calculation accuracy, an all‐scenario robust IMPT optimization based on accurate Monte Carlo (MC) dose calculation was developed.MethodsIn the all‐scenario robust IMPT optimization, dose volume histograms (DVHs) were computed for the nominal case and for each uncertainty scenario. All scenarios were weighted by DVH values dynamically throughout optimization iterations. In contrast, probabilistic approach weighted scenarios with fixed scenario weights and worst case optimizations picked one single scenario — the worst scenario for each iteration. Uncertainties in patient setup and proton range were considered in all clinical cases studied. Graphics processing unit (GPU) computation was employed to reduce the computational time in both the MC dose calculation and optimization stages. A previously published adaptive quasi‐Newton method for proton optimization was extended to include robustness. To validate the all‐scenario algorithm extension, it was compared with the single scenario optimization target volume (OTV) based approach in clinical cases of three different disease sites. Additional comparisons with worst case optimization methods were conducted to evaluate the performance of the all‐scenario robust optimization against other robust optimizations.ResultsThe all‐scenario robust IMPT optimization spared organs at risk (OARs) better than the OTV‐based method while maintaining target coverage and improving the robustness of targets and OARs. Compared with composite and voxel‐wise worst case optimization, the all‐scenario robust optimization converged faster, and arrived at solutions of tighter DVH robustness spread, better target coverage and lower OAR dose.ConclusionAn all‐scenario robust IMPT treatment planning system was developed using an adaptive quasi‐Newton optimization method. The optimization system was GPU‐accelerated and based on MC dose calculation. Improved performance was observed in clinical cases when compared with worst case optimization methods.