TH‐C‐BRB‐05: Monte Carlo Simulations for Quality Assurance of Varian TrueBeam 6MV FFF RapidArc SBRT Treatments
Bibliographic record
Abstract
Purpose: To establish feasibility of performing quality assurance for Flattening Filter Free (FFF) RapidArc stereotactic body radiotherapy treatments (SBRT) on a TrueBeam LINAC using Monte Carlo simulations. Methods: Phase‐space files for TrueBeam FFF photon beams were made available by Varian in IAEA‐compliant format. Monte Carlo simulations were performed using BEAMnrc and DOSXYZnrc to validate the 6MV FFF phase space files for use in this study. The phase space data provided by Varian is in cylindrical geometry and required conversion into a format that was compatible with BEAMnrc prior to use. To establish validity of the phase space data, dose calculations in a water phantom for fields ranging from 3×3 cm̂2 to 40 × 40 cm̂2 were performed using DOSXYZnrc. Percent depth doses (PDDs), transverse profiles and output factors were calculated and compared with measurements. Monte Carlo simulations of 6MV FFF SBRT RapidArc plans were performed using a 2mm̂3 voxel size and compared with both ion chamber measurement and Eclipse Treatment Planning System (TPS) dose calculations. 3D gamma analysis (3%,3mm) comparing Monte Carlo and TPS results was performed. Results: Monte Carlo simulations and measured values agreed within 1% and 1.5% for PDDs and profiles respectively for all fields. The agreement between measured and calculated output factors was within 1 % including for highly asymmetric fields. These results indicate that the 6MV FFF phase space data is sufficiently accurate for use in quality assurance in radiation therapy. For the 6 MV FFF RapidArc plans the agreement between MC and both measured and TPS dose calculations was within 2%. Over 95% of the points passed the 3D Gamma test Conclusions: We have demonstrated the feasibility of performing patient specific quality assurance for 6 MV FFF SBRT RapidArc treatments using Monte Carlo simulations for a TrueBeam linac. This project is funded by Varian Medical Systems
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".