Using Monte Carlo simulations to commission photon beam output factors—a feasibility study
Bibliographic record
Abstract
This study investigates the feasibility of using Monte Carlo methods to assist the commissioning of photon beam output factors from a medical accelerator. The Monte Carlo code, BEAMnrc, was used to model 6 MV and 18 MV photon beams from a Varian linear accelerator. When excellent agreements were obtained between the Monte Carlo simulated and measured dose distributions in a water phantom, the entire geometry including the accelerator head and the water phantom was simulated to calculate the relative output factors. Simulated output factors were compared with measured data, which consist of a typical commission dataset for the output factors. The measurements were done using an ionization chamber in a water phantom at a depth of 10 cm with a source-detector distance of 100 cm. Square fields and rectangular fields with widths and lengths ranging from 4 cm to 40 cm were studied. The result shows a very good agreement (< 1.5%) between the Monte Carlo calculated and the measured relative output factors for a typical commissioning dataset. The Monte Carlo calculated backscatter factors to the beam monitor chamber agree well with measured data in the literature. Monte Carlo simulations have also been shown to be able to accurately predict the collimator exchange effect and its component for rectangular fields. The information obtained is also useful to develop an algorithm for accurate beam modelling. This investigation indicates that Monte Carlo methods can be used to assist commissioning of output factors for photon beams.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".