Measured and Monte Carlo simulated electron backscatter to the monitor chamber for the Varian TrueBeam Linac
Bibliographic record
Abstract
Abstract To accurately simulate therapeutic electron beams using Monte Carlo methods, backscatter from jaws into the monitor chamber must be accounted for via the backscatter factor, S b . Measured and simulated values of S b for the TrueBeam are investigated. Two approaches for measuring S b are presented. Both require service mode operation with the dose and pulse forming networking servos turned off in order to assess changes in dose rate with field size. The first approach samples an instantaneous dose rate, while the second approach times the delivery of a fixed number of monitor units to assess dose rate. Dose rates were measured for 6, 12 and 20 MeV electrons for jaw- or MLC-shaped apertures between 1 × 1 and 40 × 40 cm 2 . The measurement techniques resulted in values of S b that agreed within 0.21% for square and asymmetric fields collimated by the jaws. Measured values of S b were used to calculate the forward dose component in a virtual monitor chamber using BEAMnrc. Based on this forward component, simulated values of S b were calculated and compared to measurement and Varian’s VirtuaLinac simulations. BEAMnrc results for jaw-shaped fields agreed with measurements and with VirtuaLinac simulations within 0.2%. For MLC-shaped fields, the respective measurement techniques differed by as much as 0.41% and BEAMnrc results differed with measurement by as much as 0.4%, however, all measured and simulated values agreed within experimental uncertainty. Measurement sensitivity was not sufficient to capture the small backscatter effect due to the MLC, and Monte Carlo predicted backscatter from the MLC to be no more than 0.3%. Backscatter from the jaws changed the electron dose rate by up to 2.6%. This reinforces the importance of including a backscatter factor in simulations of electron fields shaped with secondary collimating jaws, but presents the option of ignoring it when jaws are retracted and collimation is done with the MLC.
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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 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".