Simulation of the precision limits of plastic scintillation detectors using optimal component selection
Bibliographic record
Abstract
PURPOSE: The purpose of this work was threefold: First, to determine which type of charge-coupled device (CCD) would provide the best dosimetric precision for plastic scintillation detectors (PSDs); second, to design a high-photon-efficiency PSD system by optimizing its signal-to-noise ratio (SNR) using off-the-shelf technology; and third, to establish the spatial, temporal, and dose precision limits of such a PSD system. The authors have attempted to design a dosimetric tool suitable for radiotherapy treatment modalities employing small fields or fast temporal modulation of the radiation fields, and to explore the current precision limits of PSD systems. METHODS: The authors used an SNR simulation model to design and calculate the dosimetric precision of a PSD employing a fiber taper to couple the optical fiber to the photodetector. The authors also used the SNR simulation model to evaluate the impact of the photodetector performance characteristics on the SNR and to establish the spatial, temporal, and dose precision limits. RESULTS: The authors found that a high-photon-efficiency PSD can provide a precision of 1% in 45 micros of integration time for a dose rate of 400 cGy/min when a single image is taken, detect a dose of 1 cGy with a detector volume of 0.0007 mm3, and image over 15,000 detectors with a precision of 1% on a 30.7 x 30.7 mm2 CCD imaging area. CONCLUSIONS: These characteristics establish that PSDs theoretically constitute a suitable dosimetric tool for radiotherapy treatment modalities employing small fields or fast temporal modulation of the radiation fields.
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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 | 0.001 |
| 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) | 0.001 | 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".