SU‐F‐BRE‐01: A Rapid Method to Determine An Upper Limit On a Radiation Detector's Correction Factor During the QA of IMRT Plans
Bibliographic record
Abstract
Purpose: Discrepancies in the verification of the absorbed dose to water from an IMRT plan using a radiation dosimeter can be wither caused by 1) detector specific nonstandard field correction factors as described by the formalism of Alfonso et al. 2) inaccurate delivery of the DQA plan. The aim of this work is to develop a simple/fast method to determine an upper limit on the contribution of composite field correction factors to these discrepancies. Methods: Indices that characterize the non‐flatness of the symmetrised collapsed delivery (VSC) of IMRT fields over detector‐specific regions of interest were shown to be correlated with IMRT field correction factors. The indices introduced are the uniformity index (UI) and the mean fluctuation index (MF). Each one of these correlation plots have 10 000 fields generated with a stochastic model. A total of eight radiation detectors were investigated in the radial orientation. An upper bound on the correction factors was evaluated by fitting values of high correction factors for a given index value. Results: These fitted curves can be used to compare the performance of radiation dosimeters in composite IMRT fields. Highly water‐equivalent dosimeters like the scintillating detector (Exradin W1) and a generic alanine detector have been found to have corrections under 1% over a broad range of field modulations (0 – 0.12 for MF and 0 – 0.5 for UI). Other detectors have been shown to have corrections of a few percent over this range. Finally, a full Monte Carlo simulations of 18 clinical and nonclinical IMRT field showed good agreement with the fitted curve for the A12 ionization chamber. Conclusion: This work proposes a rapid method to evaluate an upper bound on the contribution of correction factors to discrepancies found in the verification of DQA plans.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 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) | 0.006 | 0.002 |
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".