Measurement accuracy and Cerenkov removal for high performance, high spatial resolution scintillation dosimetry
Bibliographic record
Abstract
With highly conformal radiation therapy techniques such as intensity-modulated radiation therapy, radiosurgery, and tomotherapy becoming more common in clinical practice, the use of these narrow beams requires a higher level of precision in quality assurance and dosimetry. Plastic scintillators with their water equivalence, energy independence, and dose rate linearity have been shown to possess excellent qualities that suit the most complex and demanding radiation therapy treatment plans. The primary disadvantage of plastic scintillators is the presence of Cerenkov radiation generated in the light guide, which results in an undesired stem effect. Several techniques have been proposed to minimize this effect. In this study, we compared three such techniques-background subtraction, simple filtering, and chromatic removal-in terms of reproducibility and dose accuracy as gauges of their ability to remove the Cerenkov stem effect from the dose signal. The dosimeter used in this study comprised a 6-mm(3) plastic scintillating fiber probe, an optical fiber, and a color charge-coupled device camera. The whole system was shown to be linear and the total light collected by the camera was reproducible to within 0.31% for 5-s integration time. Background subtraction and chromatic removal were both found to be suitable for precise dose evaluation, with average absolute dose discrepancies of 0.52% and 0.67%, respectively, from ion chamber values. Background subtraction required two optical fibers, but chromatic removal used only one, thereby preventing possible measurement artifacts when a strong dose gradient was perpendicular to the optical fiber. Our findings showed that a plastic scintillation dosimeter could be made free of the effect of Cerenkov radiation.
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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 | 0.007 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".