A Monte Carlo investigation of contaminant electrons due to a novel<i>in vivo</i>transmission detector
Bibliographic record
Abstract
A novel transmission detector (IBA Dosimetry, Germany) developed as an IMRT quality assurance tool, intended for in vivo patient dose measurements, is studied here. The goal of this investigation is to use Monte Carlo techniques to characterize treatment beam parameters in the presence of the detector and to compare to those of a plastic block tray (a frequently used clinical device). Particular attention is paid to the impact of the detector on electron contamination model parameters of two commercial dose calculation algorithms. The linac head together with the COMPASS transmission detector (TRD) was modeled using BEAMnrc code. To understand the effect of the TRD on treatment beams, the contaminant electron fluence, energy spectra, and angular distributions at different SSDs were analyzed for open and non-open (i.e. TRD and block tray) fields. Contaminant electrons in the BEAMnrc simulations were separated according to where they were created. Calculation of surface dose and the evaluation of contributions from contaminant electrons were performed using the DOSXYZnrc user code. The effect of the TRD on contaminant electrons model parameters in Eclipse AAA and Pinnacle(3) dose calculation algorithms was investigated. Comparisons of the fluence of contaminant electrons produced in the non-open fields versus open field show that electrons created in the non-open fields increase at shorter SSD, but most of the electrons at shorter SSD are of low energy with large angular spread. These electrons are out-scattered or absorbed in air and contribute less to surface dose at larger SSD. Calculated surface doses with the block tray are higher than those with the TRD. Contribution of contaminant electrons to dose in the buildup region increases with increasing field size. The additional contribution of electrons to surface dose increases with field size for TRD and block tray. The introduction of the TRD results in a 12% and 15% increase in the Gaussian widths used in the contaminant electron source model of the Eclipse AAA dose algorithm. The off-axis coefficient in the Pinnacle(3) dose calculation algorithm decreases in the presence of TRD compared to without the device. The electron model parameters were modified to reflect the increase in electron contamination with the TRD, a necessary step for accurate beam modeling when using the device.
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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 | 0.000 |
| 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) | 0.001 | 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".