Compensator quality control with an amorphous silicon EPID
Bibliographic record
Abstract
The calibration and quality control of compensators is conventionally performed with an ion chamber in a water-equivalent phantom. In our center, the compensator factor and four off-axis fluence ratios are measured to verify the central axis beam modulation and orientation of the compensator. Here we report the investigation of an alternative technique for compensator quality control using an amorphous silicon electronic portal imaging device (a-Si EPID). Preliminary experiments were performed to identify appropriate EPID operating parameters for this relative dosimetric study and also to quantify EPID operation. The pixel value versus energy fluence response of the EPID for both open and compensated fields was then determined, and expressed via calibration curves. For open fields the response was seen to be linear, whereas for compensated fields it exhibited a small quadratic component. To account for field size effects, we measured EPID scatter factors. These exhibited small but non-negligible dependencies on compensator thickness and source-detector distance. Finally, a number of test and clinical compensators were evaluated to assess the suitability of the EPID for compensator quality control. Our results indicate that the a-Si EPID can measure clinical compensator factors and off-axis energy fluence ratios to within 2% of values measured by a Farmer chamber on average, and so is a suitable ion chamber replacement.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".