Contemporary treatment planning systems for proton radiotherapy typically use analytical pencil-beam algorithms - which require a comprehensive set of configuration data - to predict the absorbed dose distributions in the patient. In order to reduce the time required to prepare a new proton treatment planning system for clinical use, it was desirable to configure the planning system before measured beam data were available. However, it was not known if the Monte Carlo simulation method was a practical alternative to measuring beam profiles. The purpose of this study was to develop a model of a passively scattered proton therapy unit, to simulate the properties of the proton fields using the Monte Carlo technique and to configure an analytical treatment planning system using the simulated beam data. Additional simulations and treatment plans were calculated in order to validate the pencil-beam predictions against the Monte Carlo simulations using realistic treatment beams. Comparison of dose distributions in a water phantom revealed small dose difference and distances to agreement under the validation conditions. The total simulation time for generating the 768 beam configuration profiles was approximately 6 weeks using 30 nodes in a parallel processing cluster. The results of this study show that it is possible to configure and test a proton treatment planning system prior to the availability of measured proton beam data. The model presented here provided a means to reduce by several months the time required to prepare an analytical treatment planning system for patient treatments.
Many clinical studies have demonstrated that implanted radiopaque fiducial markers improve targeting accuracy in external-beam radiotherapy, but little is known about the dose perturbations these markers may cause in patients receiving proton radiotherapy. The objective of this study was to determine what types of implantable markers are visible in setup radiographs and, at the same time, perturb the therapeutic proton dose to the prostate by less than 10%. The radiographic visibility of the markers was assessed by visual inspection of lateral setup radiographs of a pelvic phantom using a kilovoltage x-ray imaging system. The fiducial-induced perturbations in the proton dose were estimated with Monte Carlo simulations. The influence of marker material, size, placement depth and orientation within the pelvis was examined. The radiographic tests confirmed that gold and stainless steel markers were clearly visible and that titanium markers were not. The Monte Carlo simulations revealed that titanium and stainless steel markers minimally perturbed the proton beam, but gold markers cast unacceptably large dose shadows. A 0.9 mm diameter, 3.1 mm long cylindrical stainless steel marker provides good radiographic visibility yet perturbs the proton dose distribution in the prostate by less than 8% when using a parallel opposed lateral beam arrangement.
By the end of 2002, 33 398 patients worldwide had been treated with proton radiotherapy, 10 829 for eye diseases. The dose prediction algorithms used today for ocular proton therapy treatment planning rely on parameterizations of measured proton dose distributions, i.e., broad-beam and pencil-beam techniques, whose predictive capabilities are inherently limited by severe approximations and simplifications in modelling the radiation transport physics. In contrast, the Monte Carlo radiation transport technique can, in principle, provide accurate predictions of the proton treatment beams by taking into account all the physical processes involved, including coulombic energy loss, energy straggling, multiple Coulomb scattering, elastic and nonelastic nuclear interactions, and the transport of secondary particles. It has not been shown, however, whether it is possible to commission a proton treatment planning system by using data exclusively from Monte Carlo simulations of the treatment apparatus and a phantom. In this work, we made benchmark comparisons between Monte Carlo predictions and measurements of an ocular proton treatment beamline. The maximum differences between absorbed dose profiles from simulations and measurements were 6% and 0.6 mm, while typical differences were less than 2% and 0.2 mm. The computation time for the entire virtual commissioning process is less than one day. The study revealed that, after a significant development effort, a Monte Carlo model of a proton therapy apparatus is sufficiently accurate and fast for commissioning a treatment planning system.
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