Monte Carlo investigation into feasibility and dosimetry of flat flattening filter free beams
Bibliographic record
Abstract
Flattening filter free (FFF) beams due to their non-uniformity, are sub-optimal for larger field sizes. The purpose of this study was to investigate the incident electron beam distributions that would produce flat FFF (F4) beams without the use of a flattening filter (FF). Monte Carlo (MC) simulations with BEAMnrc and DOSXYZnrc codes have been performed to evaluate feasibility of this approach. The dose distributions in water for open 6 MV beams were simulated using the Varian 21EX linac head model, which will be called the FF model. The FF was then removed from the FF model, and MC simulations were performed using (1) 6 MeV electrons incident on the target and (2) a 6 MeV electron beam with electron angular distributions optimized to provide as flat dose profiles as possible. Configuration (1) represents FFF beam while configuration (2) allowed producing a F4 beam. Optimizations have also been performed to produce flattest profiles for a set of dose rates (DRs) in the range from 1.25 to 2.4 of the DR of FF beam. Profiles and percentage depth doses (PDDs) from 6 MV F4 beams have been calculated and compared to those from the FF beam. Calculated profiles demonstrated improved flatness of the FFF beams. In fact, up to field sizes within the circle of 35 cm diameter the flatness of F4 beam at dmax was better or comparable to that of FF beam. At 20 cm off-axis the dose increased from 52% for FFF to 92% for F4 beam. Also, profiles of F4 beams did not change considerably with depth. PDDs from F4 beams were similar to those of the FFF beam. The DR for the largest modeled (44 cm diameter) F4 beam was higher than the DR from FF beam by a factor of 1.25. It was shown that the DR can be increased while maintaining beam flatness, but at the cost of reduced field size.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".