Beltran, Chris

PurposeProvide an adaptive multicriteria optimization (MCO) method for intensity‐modulated proton therapy (IMPT) utilizing GPU technology. Previously described limitations of MCO such as Pareto approximation and limitation on the number of objectives were addressed.MethodsThe treatment planning process for IMPT must account for multiple objectives, which requires extensive treatment planning resources. Often a large number of objectives (>10) are required. Hence the need for an MCO algorithm that can handle large number of objectives. The novelty of the MCO method presented here lies on the introduction of the adaptive weighting scheme that can generate a well‐distributed and dense representation of the Pareto surface for a large number of objectives in an efficient manner. In our approach the generated Pareto surface is constructed for a set of DVH objectives. The MCO algorithm is based on the augmented weighted Chebychev metric (AWCM) method with an adaptive weighting scheme. This scheme uses the differential evolution (DE) method to generate a set of well‐distributed Pareto points. The quality of the Pareto points’ distribution in the objective space was assessed quantitatively using the Pareto sampling metric. The MCO algorithm was developed to perform multiple parallel searches to achieve a rapid mapping of the Pareto surface, produce clinically deliverable plans, and was implemented on a GPU cluster. The MCO algorithm was tested on two clinical cases with 10 and 18 objectives. For each case one of the MCO‐generated plans was selected for comparison with the clinically generated plan. The MCO plan was randomly selected out of the set of MCO plans that had target coverage similar to the clinically generated plan and the same or better sparing of the organs at risk (OAR). Additionally, a validation study of the AWCM method vs the weighted sum method was performed.ResultsThe adaptive MCO algorithm generated Pareto points on the Pareto hypersurface in a fast (2–3 hr) and efficient manner for 2 cases with 10 and 18 objectives. The MCO algorithm generated a dense and well‐distributed set of Pareto points on the objective space, and was able to achieve minimization of the Pareto sampling metric. The selected MCO plan showed an improvement of the DVH objectives in comparison to the clinically optimized plan in both cases. For case one, the MCO plan showed a 48% reduction of the 50% dose to OARs and a 16% reduction of the 1% dose to OARs. For case 2, the MCO plan showed a 72% reduction of the 50% dose to OARs and a 42% reduction of the 1% dose to OARs. The comparison of AWCM to WS showed that the AWCM method has a dosimetric advantage over WS for both patient cases.ConclusionWe introduced an adaptive MCO algorithm for IMPT accelerated using GPUs. The algorithm is based on an adaptive method for generating Pareto plans in the objective space. We have shown that the algorithm can provide rapid and efficient mapping of the multicriteria Pareto surface with clinically deliverable plans.

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.

Abstract Background and study aims Little consideration has been given to selection of endoscopic ultrasound-guided fiducials for proton radiotherapy and the resulting perturbations in the therapy dose and pattern. Our aim was to assess the impact of perturbations caused by six fiducials of different composition and dimensions in a phantom gel model. Materials and methods The phantom was submerged in a water bath and irradiated with a uniform 10 cm × 10 cm field of 119.7 MeV monoenergetic spot scanning protons delivered through a 45 mm range shifter. The proton “Bragg Peak” was evaluated. Results Dose perturbations manifesting as dose reductions up to 30 % were observed. A carbon composite (1 × 5 mm) and gold (0.4 × 10 mm) fiducial with backload potential rather than dedicated EUS pre-loaded gold fiducial needles had the best performance in terms of minimizing the dose perturbation. Conclusions Our data demonstrate that a carbon composite fiducial has a less untoward effect on proton therapy dose distribution than dedicated EUS pre-loaded gold fiducial needles. Such information is important to consider when selecting fiducials specifically for proton therapy.

With robust optimization for spot scanning proton therapy now commercially available, the ability exists to account for setup, range, and interfield uncertainties during optimization. Robust optimization is particularly beneficial for craniospinal irradiation (CSI) where the large target volume lends itself to larger setup uncertainties and the need for robust match lines can all be handled with the uncertainty parameters found inside the optimizer. Suggested robust optimization settings, parameters, and image guidance for CSI patients using proton therapy spot scanning are provided. Useful structures are defined and described. Suggestions are given for perturbations to be entered into the optimizer in order to achieve a plan that provides robust target volume coverage and critical structure sparing as well as a robust match line. Interfield offset effects, a concern when using multifield optimization, can also be addressed within the robust optimizer. A robust optimizer can successfully be employed to produce robust match lines, target volume coverage, and critical structure sparing under specified uncertainties. The robust optimizer can also be used to reduce effects arising from interfield uncertainties. Using robust optimization, a plan robust against setup, range, and interfield uncertainties for craniospinal treatments can be created. Utilizing robust optimization allows one to ensure critical structures are spared and target volumes are covered under the desired uncertainty parameters.

PurposeMicrodosimetry is a vital tool for assessing the microscopic patterns of energy deposition by radiation, which ultimately govern biological effect. Solid‐state, silicon‐on‐insulator microdosimeters offer an approach for making microdosimetric measurements with high spatial resolution (on the order of tens of micrometers). These high‐resolution, solid‐state microdosimeters may therefore play a useful role in characterizing proton radiotherapy fields, particularly for making highly resolved measurements within the Bragg peak region. In this work, we obtain microdosimetric measurements with a solid‐state microdosimeter (MicroPlus probe) in a clinical, spot‐scanning proton beam of small spot size.MethodsThe MicroPlus probe had a 3D single sensitive volume on top of silicon oxide. The sensitive volume had an active cross‐sectional area of 250 μm × 10 μm and thickness of 10 μm. The proton facility was a synchrotron‐based, spot‐scanning system with small spot size (σ ≈ 2 mm). We performed measurements with the clinical beam current (≈1 nA) and had no detected pulse pile‐up. Measurements were made in a water‐equivalent phantom in water‐equivalent depth (WED) increments of 0.25 mm or 1.0 mm along pristine Bragg peaks of energies 71.3 MeV and 159.9 MeV, respectively. For each depth, we measured lineal energy distributions and then calculated the dose‐weighted mean lineal energy, . The measurements were repeated for two field sizes: 4 × 4 cm2 and 20 × 20 cm2.ResultsFor both 71.3 MeV and 159.9 MeV and for both field sizes, increased with depth toward the distal edge of the Bragg peak, a result consistent with Monte Carlo calculations and measurements performed elsewhere. For the 71.3 MeV, 4 × 4 cm2 beam (range at 80% distal falloff, R80 = 3.99 cm), we measured keV/μm at WED = 2 cm, and keV/μm at WED = 3.95 cm. For the 71.3 MeV, 20 × 20 cm2 beam, we measured keV/μm at WED = 2.6 cm, and keV/μm at WED = 3 cm. For the 159.9 MeV, 4 × 4 cm2 beam (R80 = 17.7 cm), keV/μm at WED = 5 cm, and keV/μm at WED = 17.6 cm. For the 159.9 MeV, 20 × 20 cm2 beam, keV/μm at WED = 5 cm, and keV/μm at WED = 17.6 cm.ConclusionsWe performed microdosimetric measurements with a novel solid‐state, silicon‐on‐insulator microdosimeter in a clinical spot‐scanning proton beam of small spot size and unmodified beam current. For all of the proton field sizes and energies considered, the measurements of were in agreement with expected trends. Furthermore, we obtained measurements with a spatial resolution of 10 μm in the beam direction. This spatial resolution greatly exceeded that possible with a conventional gaseous tissue‐equivalent proportional counter and allowed us to perform a high‐resolution investigation within the Bragg peak region. The MicroPlus probe is therefore suitable for applications in proton radiotherapy.

PurposeThis work aims to characterize a proton pencil beam scanning (PBS) and passive double scattering (DS) systems as well as to measure parameters relevant to the relative biological effectiveness (RBE) of the beam using a silicon on insulator (SOI) microdosimeter with well‐defined 3D sensitive volumes (SV). The dose equivalent downstream and laterally outside of a clinical PBS treatment field was assessed and compared to that of a DS beam.MethodsA novel silicon microdosimeter with well‐defined 3D SVs was used in this study. It was connected to low noise electronics, allowing for detection of lineal energies as low as 0.15 keV/μm. The microdosimeter was placed at various depths in a water phantom along the central axis of the proton beam, and at the distal part of the spread‐out Bragg peak (SOBP) in 0.5 mm increments. The RBE values of the pristine Bragg peak (BP) and SOBP were derived using the measured microdosimetric lineal energy spectra as inputs to the modified microdosimetric kinetic model (MKM). Geant4 simulations were performed in order to verify the calculated depth‐dose distribution from the treatment planning system (TPS) and to compare the simulated dose‐mean lineal energy to the experimental results.ResultsFor a 131 MeV PBS spot (124.6 mm R90 range in water), the measured dose‐mean lineal energy increased from 2 keV/μm at the entrance to 8 keV/μm in the BP, with a maximum value of 10 keV/μm at the distal edge. The derived RBE distribution for the PBS beam slowly increased from 0.97 ± 0.14 at the entrance to 1.04 ± 0.09 proximal to the BP, then to 1.1 ± 0.08 in the BP, and steeply rose to 1.57 ± 0.19 at the distal part of the BP. The RBE distribution for the DS SOBP beam was approximately 0.96 ± 0.16 to 1.01 ± 0.16 at shallow depths, and 1.01 ± 0.16 to 1.28 ± 0.17 within the SOBP. The RBE significantly increased from 1.29 ± 0.17 to 1.43 ± 0.18 at the distal edge of the SOBP.ConclusionsThe SOI microdosimeter with its well‐defined 3D SV has applicability in characterizing proton radiation fields and can measure relevant physical parameters to model the RBE with submillimeter spatial resolution. It has been shown that for a physical dose of 1.82 Gy at the BP, the derived RBE based on the MKM model increased from 1.14 to 1.6 in the BP and its distal part. Good agreement was observed between the experimental and simulation results, confirming the potential application of SOI microdosimeter with 3D SV for quality assurance in proton therapy.

ABSTRACT Various types of radiation are utilized in the treatment of cancer. Equal physical doses of different radiation types do not always result in the same amount of biological damage. In order to account for these differences, a scaling factor known as the relative biological effectiveness (RBE) can be used. 137Cesium (137Cs) has been used as a source of radiation in a significant body of radiation therapy research. However, high-energy X-rays, such as 6 MV X-rays, are currently used clinically to treat patients. To date, there is a gap in the literature regarding the RBE comparison of these two types of radiation. Therefore, the purpose of this study was to investigate the RBE of 137Cs relative to that of 6 MV X-rays. To determine the RBE, five cell lines were irradiated [Chinese hamster ovary (CHO); human lung adenocarcinoma (A549); human glioma (U251); human glioma (T98); and human osteosarcoma (U2OS)] by both types of radiation and assessed for cell survival using a clonogenic assay. Three of the five cell lines resulted in RBE values of ~1.00 to within 11% for all survival fractions, showing the physical and biological dose for these two types of radiation were equivalent. The other two cell lines gave RBE values differing from 1.00 by up to 36%. In conclusion, the results show the range in biological effect seen between cell lines, and therefore cell type must be considered when characterizing RBE.