Compensator thickness verification using an<i>a</i>-Si EPID
Bibliographic record
Abstract
Electronic portal imaging devices (EPIDs) are being increasingly employed to make therapy verification and dose measurements in the clinic. In this work, we investigate the use of an amorphous silicon (a-Si) EPID to verify the accuracy of compensator fabrication and mounting. Compensator thickness estimates on a two-dimensional grid were calculated from the primary component of transmission obtained by subtracting a modeled scatter component from the total transmission measured with the EPID. The primary component was related to the thickness via an exponential relation that includes beam hardening. Implementation of the method involved determination of: (i) a calibration curve relating EPID pixel values to energy fluence for open and attenuated fields, which was found to be linear for open fields but to have a small quadratic component for attenuated beams; (ii) EPID scatter factors to account for field size effects, which exhibited a small dependence on compensator thickness and field size; (iii) the attenuation coefficient of the steel shot compensator material, which varied slightly with off-axis distance and field size, and (iv) an analytical model to predict scatter from the compensator, which was calculated to be <4% at the standard EPID imaging distance of 140 cm. Thickness distributions were then measured for several types of attenuators including flat, test, and clinical compensators. Although uncertainties associated with compensator manufacturing were non-negligible and made assessment of thickness measurement uncertainty difficult, we estimate the latter to be approximately 0.5 mm for steel shot compensators of thickness <4 cm.
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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 | 0.005 |
| 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 | 0.001 |
| 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".