Quality assurance using a photodiode array
Bibliographic record
Abstract
Improved treatment techniques in radiation therapy provide incentive to reduce treatment margins, thereby increasing the necessity for more accurate geometrical setup of the linear accelerator and accompanying components. In the present paper, we describe the development of a novel device that enables precise and automated measurement of geometric parameters for the purpose of improving initial setup accuracy, and for standardizing repeated quality control activities. The device consists of a silicon photodiode array, an evaluation board, a data acquisition card, and a laptop. Measurements that demonstrate the utility of the device are also presented. Using the device, we show that the radiation light field congruence for both 6 and 15 MV beams is within 1.3 mm. The maximum measured disagreement between radiation field edges and light field edges was 1.290 ± 0.002 mm, while the smallest disagreement between the light field and radiation field edge was 0.016 ± 0.003 mm. Because measurements are automated, ambiguities resulting from interobserver variability are removed, greatly improving the reproducibility of measurements across observers. We expect the device to find use in consistency measurements on linear accelerators used for stereotactic radiosurgery, during the commissioning of new linear accelerators, or as an alternative to film or other commercially available devices for performing monthly or annual quality control checks.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".