Relative optimized linearization for radiochromic film dosimetry with non‐uniformity correction
Bibliographic record
Abstract
BACKGROUND: Traditional radiochromic film dosimetry requires batch-specific dose-response curve measurements, which are time-consuming and add complexity to clinical workflows. While relative dosimetry techniques have been proposed to streamline the process, they have neglected film non-uniformities, limiting their accuracy. PURPOSE: To develop and validate a relative optimized linearization (ROL) method for radiochromic film dosimetry that eliminates the need for dose-response curve measurements while incorporating non-uniformity corrections for improved accuracy. METHODS: The accuracy of the linearization method proposed by Devic et al. was first evaluated through simulations using EBT4 film dose-response data, with maximum dose values ranging from 1 to 10 Gy. Based on these results, the linearization was refined with an optimized power function to reduce errors across all dose ranges. The optimized linearization was then integrated into the multichannel dosimetry (MCD) framework of Micke et al. to correct for dose-independent variations, forming the ROL method. ROL was validated against MCD using measured film data from open field, wedge field, and volumetric modulated arc therapy (VMAT) plans. To assess robustness, the VMAT test case was further evaluated under induced positional and dose delivery errors. Sensitivity to treatment planning modeling errors was also examined. RESULTS: Simulations showed that optimized linearization using ROL reduced average errors from up to 3% in the green channel and 2% in the blue channel to below 1% across all channels and dose ranges. ROL produced dose distributions comparable to MCD (within 1%), particularly in the open and VMAT fields. Only small regions in the wedge field, specifically in the toe region, exceeded 1%, but remained below 1.5%. Sensitivity tests confirmed ROL's robustness to spatial errors and to more subtle treatment planning variations in MLC modeling. Partial plan deliveries, which effectively scale the measured dose distribution, showed expected deviations from MCD. However, gamma analysis of the ROL-computed dose successfully detected the partial delivery error. CONCLUSIONS: The ROL method provides an efficient alternative to traditional film dosimetry by removing the need for time-consuming calibration curves while maintaining high accuracy through non-uniformity corrections. Its streamlined workflow makes it particularly valuable for routine clinical quality assurance.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".