Poster — Thur Eve — 03: A Monte Carlo Investigation of the Effects of a Novel <i>In Vivo</i> Transmission Detector on a 6 MV Photon Beam
Bibliographic record
Abstract
The effect of a transmission detector (TRD), a novel IMRT quality assurance tool to be used for in vivo dose measurements, was investigated. As a scattering material, the device will be a source of contaminant electrons and could potentially affect prescribed dose to patients during treatment. The goals of this investigation are to characterize the effect of the TRD on the clinical photon beam including IMRT fields, using Monte Carlo simulation. The linear accelerator head and the TRD were modeled using BEAMnrc. Particles scored at 70 cm and 100cm SSD for different field sizes (i.e. 5×5 cm2, 10×10 cm2, and 20 × 20 cm2) with and without the TRD were separated according to where they were created in the linac head. In addition, two IMRT fields with and without the TRD were simulated and their respective absolute dose distributions were compared. Without the TRD, air is the major source of contaminant electrons. When TRD is used, it absorbs nearly all the incident contaminant electrons, while becoming the major source of contaminant electrons. For both IMRT fields, the percentage dose differences of the calculated absolute doses with and without TRD at the isocenter were found to be within uncertainty of the measured transmission factor for the TRD. Although a major source of contaminant electrons, the TRD does not introduce excessive electron contamination into a clinical 6 MV photon beam and is therefore suitable for in vivo dose measurements if fully commissioned as an IMRT quality assurance tool. Research partly sponsored by IBA Dosimetry.
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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) | 0.002 | 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".