Eigencolor radiochromic film dosimetry
Bibliographic record
Abstract
PURPOSE: The goal of this work is to propose a new multichannel method correcting for systematic thickness disturbances and to evaluate its precision in relevant radiation dosimetry applications. METHODS: film irradiated with cobalt-60 and 6 MV photon beams and digitized with an Epson 10000XL photo scanner. Dose profiles and output factors of different field sizes are measured and analyzed. Variance analysis of the previous method of Bouchard et al. ["On the characterization and uncertainty analysis of radiochromic film dosimetry" Med Phys. 2009;36:1931-1946] is adapted to the new approach. Uncertainties are predicted for relevant applications. RESULTS: Results show that systematic disturbances attributed to thickness variations are efficiently corrected. The method is shown efficient to identify and correct for dark spots which cause systematic errors in single-channel distributions. Applications of the method in the context of relative dosimetry yields standard uncertainties ranging between 0.8% and 1.9%, depending on the region of interest (ROI) size and the film irradiation. Variance analysis predicts that uncertainty levels between 0.3% and 0.6% are achievable with repeated measurements. Uncertainties are found to vary with absorbed dose and ROI size. CONCLUSIONS: The proposed multichannel method is efficient for accurate dosimetry, reaching uncertainty levels comparable to previous publications with EBT film. The method is also promising for applications beyond clinical QA, such as machine characterization and other advanced dosimetry applications.
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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) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".