Herman, Michael

AbstractPurposeTo quantify the effects of combining layer‐based repainting and respiratory gating as a strategy to mitigate the dosimetric degradation caused by the interplay effect between a moving target and dynamic spot‐scanning proton delivery.MethodsAn analytic routine modeled three‐dimensional dose distributions of pencil‐beam proton plans delivered to a moving target. Spot positions and weights were established for a single field to deliver 100 cGy to a static, 15‐cm deep, 3‐cm radius spherical clinical target volume with a 1‐cm isotropic internal target volume expansion. The interplay effect was studied by modeling proton delivery from a clinical synchrotron‐based spot scanning system and respiratory target motion, patterned from surrogate patient breathing traces. Motion both parallel and orthogonal to the beam scanning direction was investigated. Repainting was modeled using a layer‐based technique. For each of 13 patient breathing traces, the dose from 20 distinct delivery schemes (combinations of four gate window amplitudes and five repainting techniques) was computed. Delivery strategies were inter‐compared based on target coverage, dose homogeneity, high dose spillage, and delivery time.ResultsNotable degradation and variability in plan quality were observed for ungated delivery. Decreasing the gate window reduced this variability and improved plan quality at the expense of longer delivery times. Dose deviations were substantially greater for motion orthogonal to the scan direction when compared with parallel motion. Repainting coupled with gating was effective at partially restoring dosimetric coverage at only a fraction of the delivery time increase associated with very small gate windows alone. Trends for orthogonal motion were similar, but more complicated, due to the increased severity of the interplay.ConclusionsLayer‐based repainting helps suppress the interplay effect from intra‐gate motion, with only a modest penalty in delivery time. The magnitude of the improvement in target coverage is strongly influenced by individual patient breathing patterns and the tumor motion trajectory.

Abstract Soft tissue sarcomas are a rare and heterogeneous group of malignancies that present significant diagnostic and therapeutic challenges. Patient stratification based on tumor aggressiveness and early therapeutic response based on quantitative imaging may improve prediction of treatment response and the evaluation of new treatment strategies in clinical trials. The purpose of this pilot study was to determine the technical feasibility of magnetic resonance elastography (MRE) and dynamic contrast-enhanced (DCE) MRI for the evaluation of sarcoma stiffness and perfusion in 9 patients with histologically confirmed sarcoma. Additionally, we assessed the feasibility of utilizing MRE and DCE-MRI for the early evaluation of response to radiation therapy in 4 patients to determine the utility of further evaluation in a larger cohort study. Tumor size, stiffness, and perfusion parameters all decreased from baseline at the time of the pre-surgery or follow-up MRI, and results were compared to pathology or conventional imaging. MRE and DCE-MRI may be useful for the quantitative evaluation of tumor stiffness and perfusion, and therapy response assessment in soft tissue sarcomas.

AbstractPurposeThe aim of this work is to describe the clinical implementation of respiratory‐gated spot‐scanning proton therapy (SSPT) for the treatment of thoracic and abdominal moving targets. The experience of our institution is summarized, from initial acceptance and commissioning tests to the development of standard clinical operating procedures for simulation, motion assessment, motion mitigation, treatment planning, and gated SSPT treatment delivery.Materials and methodsA custom respiratory gating interface incorporating the Real‐Time Position Management System (RPM, Varian Medical Systems, Inc., Palo Alto, CA, USA) was developed in‐house for our synchrotron‐based delivery system. To assess gating performance, a motion phantom and radiochromic films were used to compare gated vs nongated delivery. Site‐specific treatment planning protocols and conservative motion cutoffs were developed, allowing for free‐breathing (FB), breath‐holding (BH), or phase‐gating (Ph‐G). Room usage efficiency of BH and Ph‐G treatments was retrospectively evaluated using beam delivery data retrieved from our record and verify system and DICOM files from patient‐specific quality assurance (QA) procedures.ResultsMore than 70 patients were treated using active motion management between the launch of our motion mitigation program in October 2015 and the end date of data collection of this study in January 2018. During acceptance procedures, we found that overall system latency is clinically‐suitable for Ph‐G. Regarding room usage efficiency, the average number of energy layers delivered per minute was <10 for Ph‐G, 10‐15 for BH and ≥15 for FB, making Ph‐G the slowest treatment modality. When comparing to continuous delivery measured during pretreatment QA procedures, the median values of BH treatment time were extended from 6.6 to 9.3 min (+48%). Ph‐G treatments were extended from 7.3 to 13.0 min (+82%).ConclusionsActive motion management has been crucial to the overall success of our SSPT program. Nevertheless, our conservative approach has come with an efficiency cost that is more noticeable in Ph‐G treatments and should be considered in decision‐making.

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.

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.

PurposeTo accurately model the beam delivery time (BDT) for a synchrotron‐based proton spot scanning system using experimentally determined beam parameters.MethodsA model to simulate the proton spot delivery sequences was constructed, and BDT was calculated by summing times for layer switch, spot switch, and spot delivery. Test plans were designed to isolate and quantify the relevant beam parameters in the operation cycle of the proton beam therapy delivery system. These parameters included the layer switch time, magnet preparation and verification time, average beam scanning speeds in x‐ and y‐directions, proton spill rate, and maximum charge and maximum extraction time for each spill. The experimentally determined parameters, as well as the nominal values initially provided by the vendor, served as inputs to the model to predict BDTs for 602 clinical proton beam deliveries. The calculated BDTs (TBDT) were compared with the BDTs recorded in the treatment delivery log files (TLog): ∆t = TLog−TBDT.ResultsThe experimentally determined average layer switch time for all 97 energies was 1.91 s (ranging from 1.9 to 2.0 s for beam energies from 71.3 to 228.8 MeV), average magnet preparation and verification time was 1.93 ms, the average scanning speeds were 5.9 m/s in x‐direction and 19.3 m/s in y‐direction, the proton spill rate was 8.7 MU/s, and the maximum proton charge available for one acceleration is 2.0 ± 0.4 nC. Some of the measured parameters differed from the nominal values provided by the vendor. The calculated BDTs using experimentally determined parameters matched the recorded BDTs of 602 beam deliveries (∆t = −0.49 ± 1.44 s), which were significantly more accurate than BDTs calculated using nominal timing parameters (∆t = −7.48 ± 6.97 s).ConclusionsAn accurate model for BDT prediction was achieved by using the experimentally determined proton beam therapy delivery parameters, which may be useful in modeling the interplay effect and patient throughput. The model may provide guidance on how to effectively reduce BDT and may be used to identifying deteriorating machine performance.