Evaluation of a novel triple-channel radiochromic film analysis procedure using EBT2
Bibliographic record
Abstract
A novel approach to read out radiochromic film was introduced recently by the manufacturer of GafChromic film. In this study, the performance of this triple-channel film dosimetry method was compared against the conventional single-red-channel film dosimetry procedure, with and without inclusion of a pre-irradiation (pre-IR) film scan, using EBT2 film and kilo- and megavoltage photon beams up to 10 Gy. When considering regions of interest averaged doses, the triple-channel method and both single-channel methods produced equivalent results. Absolute dose discrepancies between the triple-channel method, both single-channel methods and the treatment planning system calculated dose values, were no larger than 5 cGy for dose levels up to 2.2 Gy. Signal to noise in triple-channel dose images was found to be similar to signal to noise in single-channel dose images. The accuracy of resulting dose images from the triple- and single-channel methods with inclusion of pre-IR film scan was found to be similar. Results of a comparison of EBT2 data from a kilovoltage depth dose experiment to corresponding Monte Carlo depth dose data produced dose discrepancies of 9.5 ± 12 cGy and 7.6 ± 6 cGy for the single-channel method with inclusion of a pre-IR film scan and the triple-channel method, respectively. EBT2 showed to be energy sensitive at low kilovoltage energies with response differences of 11.9% and 15.6% in the red channel at 2 Gy between 50-225 kVp and 80-225 kVp photon spectra, respectively. We observed that the triple-channel method resulted in non-uniformity corrections of ±1% and consistency values of 0-3 cGy for the batches and dose levels studied. Results of this study indicate that the triple-channel radiochromic film read-out method performs at least as well as the single-channel method with inclusion of a pre-IR film scan, reduces film non-uniformity and saves time with elimination of a pre-IR film scan.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".