Dose–response linearization in radiochromic film dosimetry based on multichannel normalized pixel value with an integrated spectral correction for scanner response variations
Bibliographic record
Abstract
Purpose To introduce a model that reproducibly linearizes the response from radiochromic film (RCF) dosimetry systems at extended dose range. To introduce a correction method, generated from the same scanned images, which corrects for scanner temporal response variation and scanner bed inhomogeneity. Methods Six calibration curves were established for different lot numbers of EBT3 GAFCHROMIC™ film model based on four EPSON scanners [10000XL (2 units), 11000XL, 12000XL] at three different centers. These films were calibrated in terms of absorbed dose to water based on TG51 protocol or TRS398 with dose ranges up to 40 Gy. The film response was defined in terms of a proposed normalized pixel value ( ) as a summation of first‐order equations based on information from red, green, and blue channels. The fitting parameters of these equations are chosen in a way that makes the film response equal to dose at the time of calibration. An integrated set of correction factors (one per color channel) was also introduced. These factors account for the spatial and temporal changes in scanning states during calibration and measurements. The combination of and this “fingerprint” correction formed the basis of this new protocol and it was tested against net optical density ( ) single‐channel dosimetry in terms of accuracy, precision, scanner response variability, scanner bed inhomogeneity, noise, and long‐term stability. Results Incorporating multichannel features (RGB) into the normalized pixel value produced linear response to absorbed dose (slope of 1) in all six RCF dosimetry systems considered in this study. The “fingerprint” correction factors of each of these six systems displayed unique patterns at the time of calibration. The application of to all of these six systems could achieve a level of accuracy of ± 2.0% in the dose range of interest within modeled uncertainty level of 2.0%–3.0% depending on the dose level. Consistent positioning of control and measurement film pieces and integrating the multichannel correction into the response function formalism mitigated possible scanner response variations of as much as ± 10% at lower doses and scanner bed inhomogeneity of ± 8% to the established level of uncertainty at the time of calibration. The system was also able to maintain the same level of accuracy after 3 and 6 months post calibration. Conclusions Combining response linearity with the integrated correction for scanner response variation lead to a sustainable and practical RCF dosimetry system that mitigated systematic response shifts and it has the potential to reduce errors in reporting relative information from the film response.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".