ABSTRACT
Objective:In Shanghai Advanced Proton Therapy Facility (SAPT) of Ruijin Hospital Proton Therapy Center, the calculation accuracy of the commercial proton treatment planning system RayStation (V10), especially the accuracy of the proton range calculation, was measured and verified, aiming to provide reference for the clinical application of the treatment planning system.Methods:A head phantom was used to verify the calculation accuracy of RayStation. The phantom CT was imported into treatment planning system (TPS). The phantom was followed closely by a water tank with a one-liter cubic target. A single field verification plan with the prescribed dose of 200 cGy (relative biological effectiveness) was designed and implemented. Then, the measured distribution results were compared with the calculation results.Results:When the verification plan of the phantom was designed with the default settings of RayStation, the measured longitudinal dose distribution was approximately 4 mm deeper than that of TPS, indicating that RayStation overestimated the water equivalent thickness (WET) of the tissue substitute materials in the phantom. To study the range error, the actual beam was used to measure the WET of the soft tissue substitute material. The default setting of RayStation was fine-tuned according to the measured results. It was found that the error between the measured SOBP and TPS calculations was reduced to only 2 mm.Conclusions:Using the default setting of RayStation to calculate the stopping power of the phantom may cause a large range error. A method that combines tissue segmentation with the measured WET of the tissue substitute material is proposed to improve the range calculation accuracy of the TPS. The results show that the proposed method can improve the dose and range accuracy of the commercial TPS including RayStation for tissue substitute materials.
ABSTRACT
Objective:To introduce the method and result of the modeling and preliminary dose verification of the treatment planning system used in the first domestic proton therapy device of China (Raystation 10B, a system of scientific research version with no available registration certificate) and to verify the modeling accuracy using dose verification result.Methods:The modeling method for a treatment planning system (TPS) mainly included the data acquisition and modeling of integrated depth dose (IDD) curves, the data acquisition and modeling of beam spot profiles in air, and the calibration and modeling of absolute dose by scanning a 10 cm ×10 cm square field with a spot spacing of 2.5 mm. By measuring the dose distributions in three cases with different complexity levels and comparing them with the dose distributions calculated using the TPS, this study verified and analyzed the modeling accuracy and proposed the requirements for beam parameters and the commissioning suggestions of the proton device.Results:The peak values of the IDD curves of low-energy regions fitted using the TPS model were less than the measured values, while those of medium- and high-energy regions fitted using the TPS model approximated the measured values. The range in all energy regions fitted accurately. For the three cases with different complexity levels, the deviation between the average dose calculated by the TPS and that measured was within ±5% (national standard for type tests of medical devices). Moreover, the DTA of high-dose-gradient areas was less than 3 mm.Conclusions:The modeling accuracy of the TPS generally meets the verification requirements. However, due to the low resolution of IDDs obtained by Monte Carlo simulation in the TPS model and the sharp Bragg peaks of low-energy regions, the IDD modeling accuracy of low-energy regions is insufficient.
ABSTRACT
Objective:To propose a new robust optimization method, known as modified worst case method, was proposed, which can enable users to control the trade-off between nominal plan quality and plan robustness.Methods:In each iteration of the plan optimization process, the dose value of each voxel in nine scenarios, which corresponded to a nominal scenario and eight perturbed scenarios with range or set-up uncertainties, were calculated and the maximum of deviations of each scenario voxel dose from that of the nominal scenario was included as an additive robust optimization term in the objective function. A weighting factor p robust was used to this robust optimization term to balance the nominal plan quality and plan robustness. Results:The robust optimization methods were implemented and compared in an in-house developed robust optimization module. When p robust=0.8, compared with conventional optimization, the ΔD 95% of CTV was reduced from 9.8 Gy to 7.6 Gy. When p robust was reduced from 1 to 0, ΔD 95% was increased from 7.0 Gy to 9.8 Gy, whereas the D 95% and D max of CTV, and the D 5% and D max of organs at risk (OAR) in the nominal scenario were reduced. Conclusions:The proposed modified worst case method can effectively improve the robustness of the plan to the range and set-up uncertainties. Besides, the weighting factor p robust in this method can be adopted to control the trade-off between nominal plan quality and plan robustness.