SU-E-T-83: Potential Improvements in Nonstandard Beam Quality Correction Factor Measurements Using Radiochromic Film Mutlichannel Analysis
Bibliographic record
Abstract
Purpose: The main goal of this study is to improve the precision and accuracy to which nonstandard beam quality correction factors (kQ) are measured using a novel method of mutlichannel analysis for radiochromic film dosimetry. Methods: A method to reduce systematic errors caused by imperfections in the process of film fabrication is proposed. For each channel of the 48-bit RGB signal, the error in scanner signal is characterized as a function of the 3 channels using a multidimensional Taylor expansion. Bands of films irradiated with uniform dose are used to estimate the coefficients of the expansion. Achievable uncertainty levels are estimated using EBT and EBT2 film measurements. Results: The method is compared with a single-channel method using only the red component of the signal. Using the same set of films, results show that the method diminishes uncertainty levels by a factor of 1.5 to 2 in relative dosimetry using EBT and EBT2 films. Results also show that systematic errors are significantly reduced with the method. Uncertainty levels obtained with EBT2 fall below the ones previously obtained with EBT using the red channel only. Conclusions: The method presented in this study can potentially improve the precision and accuracy to which kQ factors are currently measured in nonstandard beams. This study shows that levels of uncertainty of about 0.25% (1-sigma) on relative dose measurements can be reached with radiochromic film. The study also shows that EBT2 film is an appropriate candidate for such measurements.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".