Monte Carlo simulation of 6‐MV dynamic wave VMAT deliveries by Vero4DRT linear accelerator using EGSnrc moving sources
Bibliographic record
Abstract
Abstract 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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".