The commissioning and benchmark of a Monte Carlo (MC) model of the 6-MV Brainlab-Mitsubishi Vero4DRT linear accelerator for the purpose of quality assurance of clinical dynamic wave arc (DWA) treatment plans is reported. Open-source MC applications based on EGSnrc particle transport codes are used to simulate the medical linear accelerator head components. Complex radiotherapy irradiations can be simulated in a single MC run using a shared library format combined with BEAMnrc "source20." Electron energy tuning is achieved by comparing measured vs simulated percentage depth doses (PDDs) for MLC-defined field sizes in a water phantom. Electron spot size tuning is achieved by comparing measured and simulated inplane and crossplane beam profiles. DWA treatment plans generated from RayStation (RaySearch) treatment planning system (TPS) are simulated on voxelized (2.5 mm 3) patient CT datasets. Planning target volume (PTV) and organs at risk (OAR) dose-volume histograms (DVHs) are compared to TPS-calculated doses for clinically deliverable dynamic volumetric modulated arc therapy (VMAT) trajectories. MC simulations with an electron beam energy of 5.9 MeV and spot size FWHM of 1.9 mm had the closest agreement with measurement. DWA beam deliveries simulated on patient CT datasets results in DVH agreement with TPS-calculated doses. PTV coverage agreed within 0.1% and OAR max doses (to 0.035 cc volume) agreed within 1 Gy. This MC model can be used as an independent dose calculation from the TPS and as a quality assurance tool for complex, dynamic radiotherapy treatment deliveries. Full patient CT treatment simulations are performed in a single Monte Carlo run in 23 min. Simulations are run in parallel using the Condor High-Throughput Computing software 1 on a cluster of eight servers. Each server has two physical processors (Intel Xeon CPU E5-2650 0 @2.00 GHz), with 8 cores per CPU and two threads per core for 256 calculation nodes.
Purpose and Aim: The Vero4DRT (Brainlab AG) linear accelerator is capable of dynamic tumor tracking (DTT) by panning/tilting the radiation beam to follow respiratory-induced tumor motion in real time. In this study, the panning/tilting motion is modeled in Monte Carlo (MC) for quality assurance (QA) of four-dimensional (4D) dose distributions created within the treatment planning system (TPS). Materials and Methods: Step-and-shoot intensity-modulated radiation therapy plans were optimized for 10 previously treated liver patients. These plans were recalculated on multiple phases of a 4D computed tomography (4DCT) scan using MC while modeling panning/tilting. The dose distributions on each phase were accumulated to create a respiratory-weighted 4D dose distribution. Differences between the TPS and MC modeled doses were examined. Results: On average, 4D dose calculations in MC showed the maximum dose of an organ at risk (OAR) to be 10% greater than the TPS’ three-dimensional dose calculation (collapsed cone [CC] convolution algorithm) predicted. MC’s 4D dose calculations showed that 6 out of 24 OARs could exceed their specified dose limits, and calculated their maximum dose to be 4% higher on average (up to 13%) than the TPS’ 4D dose calculations. Dose differences between MC and the TPS were greatest in the beam penumbra region. Conclusion: Modeling panning/tilting for DTT has been successfully modeled with MC and is a useful tool to QA respiratory-correlated 4D dose distributions. The dose differences between the TPS and MC calculations highlight the importance of using 4D MC to confirm the safety of OAR doses before DTT treatments.
To assess dynamic tumor tracking (DTT) target localization uncertainty for in-vivo marker-based stereotactic ablative radiotherapy (SABR) treatments of the liver using electronic-portal-imaging-device (EPID) images. The Planning Target Volume (PTV) margin contribution for DTT is estimated. Methods: Phantom and patient EPID images were acquired during noncoplanar 3DCRT-DTT delivered on a Vero4DRT linac. A chain-code algorithm was applied to detect Multileaf Collimator (MLC)-defined radiation field edges. Gold-seed markers were detected using a connected neighbor algorithm. For each EPID image, the absolute differences between the measured center-ofmass (COM) of the markers relative to the aperture-center (Tracking Error, (E T )) was reported in pan, tilt, and 2D-vector directions at the isocenter-plane. Phantom study: An acrylic cube phantom implanted with gold-seed markers was irradiated with non-coplanar 3DCRT-DTT beams and EPID images collected. Patient Study: Eight liver SABR patients were treated with non-coplanar 3DCRT-DTT beams. All patients had three to four implanted gold-markers. In-vivo EPID images were analyzed. Results: Phantom Study: On the 125 EPID images collected, 100% of the markers were identified. The average ± SD of E T were 0.24 ± 0.21, 0.47 ± 0.38, and 0.58 ± 0.37 mm in pan, tilt and 2D directions, respectively. Patient Study: Of the 1430 EPID patient images acquired, 78% had detectable markers. Over all patients, the average ± SD of E T was 0.33 ± 0.41 mm in pan, 0.63 ± 0.75 mm in tilt and 0.77 ± 0.80 mm in 2D directions The random 2D-error, σ, for all patients was 0.79 mm and the systematic 2D-error, Σ, was 0.20 mm. Using the Van Herk margin formula 1.1 mm planning target margin can represent the marker based DTT uncertainty. Conclusions: Marker-based DTT uncertainty can be evaluated in-vivo on a field-by-field basis using EPID images. This information can contribute to PTV margin calculations for DTT.
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