Sci‐PM Sat ‐ 04: Characterization of a tissue equivalent plastic scintillator dosimetry system
Bibliographic record
Abstract
High precision techniques in radiation therapy such as IMRT offer the potential for improved target coverage and increased normal tissue sparing. The complex fluence maps used in many of these techniques, however, lead to more challenging quality assurance with dose verification being labor‐intensive and time consuming. We have developed a dose verification system using tissue equivalent plastic scintillator that provides easy to acquire, rapid electronic and directly digital dose measurements in a plane perpendicular to the beam. The prototype system consists of a water‐filled Lucite phantom with a scintillator screen built into the top surface. The phantom contains a plastic mirror to reflect scintillation light towards a viewing window where it is captured using a CCD camera and a personal computer. Optical photon spread is removed using a micro‐louvre optical collimator and by deconvolving a glare kernel from the raw images. System characterization tests indicate excellent dose and spatial linearity. The system was found to have good signal uniformity and to be independent of dose rate. Spatial resolution was determined to be 0.53 mm/pixel. The system's ability to verify a dynamic treatment field was evaluated using a 60° enhanced dynamic wedge and comparing the results to 2‐D film dosimetry. Results indicate agreement within 5% of 2‐D film dosimetry. With further development this system promises to provide a fast, directly digital, and tissue equivalent alternative to current dose verification systems.
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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 | 0.001 | 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) | 0.003 | 0.001 |
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".