The effect of common dental fixtures on treatment planning and delivery for head and neck intensity modulated proton therapy.

PURPOSE: Proton treatment plan perturbation by common dental fixtures such as amalgams (Am) and porcelain-fused-to-metal (PFM) crowns has, to date, been uncharacterized. Previous studies have been conducted to determine the physical effect of these materials within the beam path for single spots, but their effects on complex treatment plans and clinical anatomy have not yet been quantified. The present manuscript aims to study the effect of Am and PFM fixtures on proton treatment planning in a clinical setting. METHODS: An anthropomorphic phantom with removable tongue, maxilla, and mandible modules was simulated on a clinical computed tomography (CT) scanner. Spare maxilla modules were modified to include either a 1.5 mm depth central groove occlusal amalgam (Am) or a porcelain-fused-to-metal (PFM) crown, implanted on the first right molar. Modified tongue modules were 3D printed to accommodate several axial or sagittal oriented pieces of EBT-3 film. Clinically representative spot-scanning proton plans were generated in Eclipse v.15.6 using the proton convolution superposition (PCS) algorithm v.15.6.06 using a multi-field optimization (MFO) technique with the goal of delivering a uniform 54 Gy dose to a clinical target volume (CTV) typical of a base-of-tongue (BoT) treatment. A typical geometric beam arrangement of two anterior oblique (AO) beams and a posterior beam was employed. Plans optimized without any material overrides were delivered to the phantom A) without implants; B) with Am fixture; or C) with PFM crown. Plans were also reoptimized and delivered with inclusion of material overrides to equate relative stopping power of the fixture with that of a previously measured result. RESULTS: Plans exhibit slightly greater dose weight towards AO beams. The optimizer accounted for inclusion of fixture overrides by increasing beam weights to the beam closest to the implant. Film measurements exhibited cold spots directly within the beam path through the fixture in plans with and without overridden materials. Cold spots were somewhat mitigated in plans including overridden materials in the structure set but were not entirely eliminated. Cold spots associated with Am and PFM fixtures were quantified at 17% and 14% for plans without overrides, respectively, and 11% and 9% with using Monte Carlo simulation. Compared with film measurements and Monte Carlo simulation, the treatment planning system underestimates the dose shadowing effect in plans including material overrides. CONCLUSIONS: Dental fixtures create a dose shadowing effect directly in line with the beam path through the material. This cold spot is partially mitigated by overriding the material to measured relative stopping powers. Due to uncertainties in modeling perturbation through the fixture, the magnitude of the cold spot is underestimated using the institutional TPS when compared to measurement and MC simulation.

A GPU-accelerated and Monte Carlo-based intensity modulated proton therapy optimization system.

PURPOSE: Conventional spot scanning intensity modulated proton therapy (IMPT) treatment planning systems (TPSs) optimize proton spot weights based on analytical dose calculations. These analytical dose calculations have been shown to have severe limitations in heterogeneous materials. Monte Carlo (MC) methods do not have these limitations; however, MC-based systems have been of limited clinical use due to the large number of beam spots in IMPT and the extremely long calculation time of traditional MC techniques. In this work, the authors present a clinically applicable IMPT TPS that utilizes a very fast MC calculation. METHODS: An in-house graphics processing unit (GPU)-based MC dose calculation engine was employed to generate the dose influence map for each proton spot. With the MC generated influence map, a modified least-squares optimization method was used to achieve the desired dose volume histograms (DVHs). The intrinsic CT image resolution was adopted for voxelization in simulation and optimization to preserve spatial resolution. The optimizations were computed on a multi-GPU framework to mitigate the memory limitation issues for the large dose influence maps that resulted from maintaining the intrinsic CT resolution. The effects of tail cutoff and starting condition were studied and minimized in this work. RESULTS: For relatively large and complex three-field head and neck cases, i.e., >100,000 spots with a target volume of ∼ 1000 cm(3) and multiple surrounding critical structures, the optimization together with the initial MC dose influence map calculation was done in a clinically viable time frame (less than 30 min) on a GPU cluster consisting of 24 Nvidia GeForce GTX Titan cards. The in-house MC TPS plans were comparable to a commercial TPS plans based on DVH comparisons. CONCLUSIONS: A MC-based treatment planning system was developed. The treatment planning can be performed in a clinically viable time frame on a hardware system costing around 45,000 dollars. The fast calculation and optimization make the system easily expandable to robust and multicriteria optimization.