Poster — Thur Eve — 07: Measurement of Radiation‐Light Congruence Using a Photodiode Array
Bibliographic record
Abstract
Many factors affect treatment delivery, including setup errors during the simulation process, dose calculation uncertainty, patient setup errors during treatment, and errors that result from incorrect calibration and geometric setup of the linear accelerator. Among these, the radiation‐light congruence is especially important because the light field is used to simulate the radiation beam. The light field is an integral part of patient setup and jaw calibration, thus radiation‐light congruence is essential for accurate treatment delivery. We have developed a novel device that enables precise and automated measurement of radiation‐light congruence. Using the device, we show that the radiation‐light field congruence for both 6 and 15 MV beams is within standard tolerance limits. However, our results indicate that radiation‐light congruence is dependent on collimator angle and energy. The maximum measured disagreement between radiation field edge and light field edge was 1.290 ± 0.004 mm at collimator angle 270 (X1, 15 MV) and 0.932 ± 0.003 mm at collimator angle 90 (Y2, 15MV). The minimum disagreement was 0.016 ± 0.003 mm for 270 (X2, 6 MV) and 0.102 ± 0.004 mm for 90 (X2, 6MV). This detector and measurement method will give us a better understanding of the radiation‐light congruence dependence on collimator angle and energy. It could also be used to determine location of the x‐ray source within the linear accelerator.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".