Sean Frigo, PhD

BACKGROUND: Quality assurance measurement of IMRT/VMAT treatment plans is resource intensive, and other more efficient methods to achieve the same confidence are desirable.

PURPOSE: We aimed to analyze treatment plans in the context of the treatment planning systems that created them, in order to predict which ones will fail a standard quality assurance measurement. To do so, we sought to create a tool external to the treatment planning system that could analyze a set of MLC positions and provide information that could be used to calculate various evaluation metrics.

METHODS: The tool was created in Python to read in DICOM plan files and determine the beam fluence fraction incident on each of seven different zones, each classified based on the RayStation MLC model. The fractions, termed grid point fractions, were validated by analyzing simple test plans. The average grid point fractions, over all control points for 46 plans were then computed. These values were then compared with gamma analysis pass percentages and median dose differences to determine if any significant correlations existed.

RESULTS: Significant correlation was found between the grid point fraction metrics and median dose differences, but not with gamma analysis pass percentages. Correlations were positive or negative, suggesting differing model parameter value sensitivities, as well as potential insight into the treatment planning system dose model.

CONCLUSIONS: By decomposing MLC control points into different transmission zones, it is possible to create a metric that predicts whether the analyzed plan will pass a quality assurance measurement from a dose calculation accuracy standpoint. The tool and metrics developed in this work have potential applications in comparing clinical beam models or identifying their weak points. Implementing the tool within a treatment planning system would also provide more potential plan optimization parameters.

PMID:37031363 | PMC:PMC10402675 | DOI:10.1002/acm2.13990

PURPOSE: We investigated the hypothesis that the transmission function of rounded end linearly traveling multileaf collimators (MLCs) is constant with position. This assumption is made by some MLC models used in clinical treatment planning systems (TPSs) and in the Varian MLC calibration convention. If not constant, this would have implications for treatment plan QA results.

METHODS: A two-dimensional ray-tracing tool to generate transmission curves as a function of leaf position was created and validated. The curves for clinically available leaf tip positions (-20 to 20 cm) were analyzed to determine the location of the beam edge (half-attenuation X-ray [XR]) location, the beam edge broadening (BEB, 80%-20% width), as well as the leaf tip zone width. More generalized scenarios were then simulated to elucidate trends as a function of leaf tip radius.

RESULTS: In the analysis of the Varian high-definition MLC, two regions were identified: a quasi-static inner region centered about central axis (CAX), and an outer one, in which large deviations were observed. A phenomenon was identified where the half-attenuation ray position, relative to that of the tip or tangential ray, increases dramatically at definitive points from CAX. Similar behavior is seen for BEB. An analysis shows that as the leaf radius parameter value is made smaller, the size of the quasi-static region is greater (and vice versa).

CONCLUSION: The MLC transmission curve properties determined by this study have implications both for MLC position calibrations and modeling within TPSs. Two-dimensional ray tracing can be utilized to identify where simple behaviors hold, and where they deviate. These results can help clinical physicists engage with vendors to improve MLC models, subsequent fluence calculations, and hence dose calculation accuracy.

PMID:35596533 | PMC:PMC9359033 | DOI:10.1002/acm2.13646

A 6 MV flattened beam model for a Varian TrueBeamSTx c-arm treatment delivery system in RayStation, developed and validated at one institution, was implemented and validated at another institution. The only parameter value adjustments were to accommodate machine output at the second institution. Validation followed MPPG 5.a. recommendations, with particular attention paid to IMRT and VMAT deliveries. With this minimal adjustment, the model passed validation across a broad spectrum of treatment plans, measurement devices, and staff who created the test plans and executed the measurements. This work demonstrates the possibility of using a single template model in the same treatment planning system with matched machines in a mixed vendor environment.

PMID:34643323 | PMC:PMC8664150 | DOI:10.1002/acm2.13445

PURPOSE: To explore candidate RayStation beam models to serve as a class-specific template for a TrueBeam treatment delivery system.

METHODS: Established validation techniques were used to evaluate three photon beam models: a clinically optimized model from the authors' institution, the built-in RayStation template, and a hybrid consisting of the RayStation template except substituting average MLC parameter values from a recent IROC survey. Comparisons were made for output factors, dose profiles from open fields, as well as representative VMAT test plans.

RESULTS: For jaw-defined output factors, each beam model was within 1.6% of expected published values. Similarly, the majority (57-66%) of jaw-defined dose curves from each model had a gamma pass rate >95% (2% / 3 mm, 20% threshold) when compared to TrueBeam representative beam data. For dose curves from MPPG 5.a MLC-defined fields, average gamma pass rates (1% / 1 mm, 20% threshold) were 92.9%, 85.1%, and 86.0% for the clinical, template, and hybrid models, respectively. For VMAT test plans measured with a diode array detector, median dose differences were 0.6%, 1.3%, and 1.1% for the clinical, template, and hybrid models, respectively. For in-phantom ionization chamber measurements with the same VMAT test plans, the average percent difference was -0.3%, -1.4%, and -1.0% for the clinical, template, and hybrid models, respectively.

CONCLUSION: Beam model templates taken from the vendor and aggregate results within the community were both reasonable starting points, but neither approach was as optimal as a clinically tuned model, the latter producing better agreement with all validation measurements. Given these results, the clinically optimized model represents a better candidate as a consensus template. This can benefit the community by reducing commissioning time and improving dose calculation accuracy for matched TrueBeam treatment delivery systems.

PMID:34036726 | PMC:PMC8200503 | DOI:10.1002/acm2.13278

PURPOSE: To assess the implementation, accuracy, and validity of the dosimetric leaf gap correction (DLGC) in Mobius3D VMAT plan calculations.

METHODS: The optimal Mobius3D DLGC was determined for both a TrueBeam with a Millennium multi-leaf collimator and a TrueBeamSTx with a high-definition multi-leaf collimator. By analyzing a broad series of seven VMAT plans and comparing the calculated to the measured dose delivered to a cylindrical phantom, optimal DLGC values were determined by minimizing the dose difference for both the collection of all plans, as well as for each plan individually. The effects of plan removal from the optimization of the collective DLGC value, as well as plan-specific DLGC values, were explored to determine the impact of plan suite design on the final DLGC determination.

RESULTS: Optimal collective DLGC values across all energies were between -0.71 and 0.89 mm for the TrueBeam, and between 0.35 and 1.85 mm for the TrueBeamSTx. The dose differences ranged between -6.1% and 2.6% across all plans when the optimal collective DLGC values were used. On a per-plan basis, the plan-specific optimal DLGC values ranged from -4.36 to 2.35 mm for the TrueBeam, and between -1.83 and 2.62 mm for the TrueBeamSTx. Comparing the plan-specific optimal DLGC to the average absolute leaf position from the central axis for each plan, a negative correlation was observed.

CONCLUSIONS: The optimal DLGC determination depends on the plans investigated, making it essential for users to utilize a suite of test plans that encompasses the full range of expected clinical plans when determining the optimal DLGC value. Validation of the secondary dose calculation should always be based on measurements, and not a comparison with the primary TPS. Varying disagreement with measurements across plans for a single DLGC value indicates potential limitations in the Mobius3D MLC model.

PMID:33666339 | PMC:PMC7984465 | DOI:10.1002/acm2.13206

The AAPM Medical Physics Practice Guideline (MPPG) 5.a provides concise guidance on the commissioning and QA of beam modeling and dose calculation in radiotherapy treatment planning systems. This work discusses the implementation of the validation testing recommended in MPPG 5.a at two institutions. The two institutions worked collaboratively to create a common set of treatment fields and analysis tools to deliver and analyze the validation tests. This included the development of a novel, open-source software tool to compare scanning water tank measurements to 3D DICOM-RT Dose distributions. Dose calculation algorithms in both Pinnacle and Eclipse were tested with MPPG 5.a to validate the modeling of Varian TrueBeam linear accelerators. The validation process resulted in more than 200 water tank scans and more than 50 point measurements per institution, each of which was compared to a dose calculation from the institution's treatment planning system (TPS). Overall, the validation testing recommended in MPPG 5.a took approximately 79 person-hours for a machine with four photon and five electron energies for a single TPS. Of the 79 person-hours, 26 person-hours required time on the machine, and the remainder involved preparation and analysis. The basic photon, electron, and heterogeneity correction tests were evaluated with the tolerances in MPPG 5.a, and the tolerances were met for all tests. The MPPG 5.a evaluation criteria were used to assess the small field and IMRT/VMAT validation tests. Both institutions found the use of MPPG 5.a to be a valuable resource during the commissioning process. The validation testing in MPPG 5.a showed the strengths and limitations of the TPS models. In addition, the data collected during the validation testing is useful for routine QA of the TPS, validation of software upgrades, and commissioning of new algorithms.

PMID:28291929 | PMC:PMC5689890 | DOI:10.1002/acm2.12015