Poster — Thur Eve — 43: Monte Carlo Modeling of Flattening Filter Free Beams and Studies of Relative Output Factors
Bibliographic record
Abstract
Flattening filter free (FFF) beams have been adopted by many clinics and used for patient treatment. However, compared to the traditional flattened beams, we have limited knowledge of FFF beams. In this study, we successfully modeled the 6 MV FFF beam for Varian TrueBeam accelerator with the Monte Carlo (MC) method. Both the percentage depth dose and profiles match well to the Golden Beam Data (GBD) from Varian. MC simulations were then performed to predict the relative output factors. The in‐water output ratio, Scp, was simulated in water phantom and data obtained agrees well with GBD. The in‐air output ratio, Sc, was obtained by analyzing the phase space placed at isocenter, in air, and computing the ratio of water Kerma rates for different field sizes. The phantom scattering factor, Sp, can then be obtained from the traditional way of taking the ratio of Scp and Sc. We also simulated Sp using a recently proposed method based on only the primary beam dose delivery in water phantom. Because there is no concern of lateral electronic disequilibrium, this method is more suitable for small fields. The results from both methods agree well with each other. The flattened 6 MV beam was simulated and compared to 6 MV FFF. The comparison confirms that 6 MV FFF has less scattering from the Linac head and less phantom scattering contribution to the central axis dose, which will be helpful for improving accuracy in beam modeling and dose calculation in treatment planning 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 | 0.001 |
| 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) | 0.004 | 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".