Assessing the accuracy of electronic portal imaging device (EPID)‐based dosimetry: I. Quantities influencing long‐term stability
Bibliographic record
Abstract
PURPOSE: The aim of this study is to reduce the uncertainty associated with determining dose-to-water using an amorphous silicon electronic portal imaging detector (EPID) under reference conditions by identifying and accounting for operational and environmental factors influencing the long-term stability of EPID response. METHODS: Measurements of the EPID relative response, corrected for variations in linear accelerator (linac) output, were performed regularly over a period of 12 months. For every acquired image set, measurements of detector supply voltages, internal operating temperature, and ambient environmental conditions were obtained. Pearson r correlation coefficients were then calculated for each pair of variables, a subset of which were fitted using multiple linear regression to develop a predictive model of EPID response. Principal component analysis was performed on the dataset to reveal the internal structure of the data in a way that best accounts for the observed variations. RESULTS: The +5.5 V power supply voltage, internal operating temperature, and the accumulated dose absorbed in EPID were identified as having the greatest influence on the long-term stability of EPID response. By correcting for the combined effect of these variables, the mean difference in linac output as measured by the EPID relative to a reference class chamber improved from -0.46% to 0.23% over the period of the study. CONCLUSIONS: This work suggests that the stability of an EPID over a period of a year can be improved by a factor of two by monitoring and accounting for the effects of variations in power supply voltage, internal temperature of the detector, and accumulated absorbed dose.
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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 | 0.004 | 0.011 |
| 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".