SU‐E‐T‐697: Simple and Efficient Modeling of the Varian TrueBeam Linac Using the GATE Monte Carlo Platform
Bibliographic record
Abstract
Purpose: To investigate the use of the relatively new Geant4 Application for Tomographic Emission (GATE) Monte Carlo (MC) platform for radiotherapy applications, by developing a model of the Varian TrueBeam™ linac. Methods: Multiple 6 MV phase space files were provided by Varian, serving as a simulation particle source. Modeling of the jaws and multileaf collimator (MLC) in GATE's macro structured environment was automated through the use of an in‐house developed Python program. Profiles and percentage depth doses (PDD) were compared between MC calculations, diode, ion chamber and radiochromic film measurements for six square fields ranging in size from 1 × 1 cm2 to 40 × 40 cm2 at five different depths. The MLC model was evaluated through comparison to radiochromic film for an “E” shaped field defined by the MLCs at two different depths. Results: Profiles and PDDs of MC calculated values for each field size and depth were evaluated against measurements using a tight 3%/1 mm gamma criteria. It was found that 28 out of 30 square field profiles have a gamma pass rate of ≥90%, 20 of which achieved at least a 95% pass rate. All PDDs were found to have at least a 97% pass rate. When compared to film measurements, 97% and 99% of points met the gamma criteria for the MLC shaped field along the inline and crossline axes respectively, while 100% of points achieved the criteria along both axes at a depth of 50 mm. Conclusion: GATE provides a valid, efficient and simple to use MC platform to model medical linear accelerators for the purpose of radiotherapy dose calculation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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 source (direct Gemma or distilled Codex), 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".