Efficient dose‐rate correction of silicon diode relative dose measurements
Bibliographic record
Abstract
PURPOSE: Silicon diodes are often the detector of choice for relative dose measurements, particularly in the context of radiotherapy involving small photon beams. However, a major drawback lies in their dose-rate dependency. Although ionization chambers are often too large for small field output factor (OF) measurements, they are valuable instruments to provide reliable percent-depth dose (PDD) curves in reference beams. The aim of this work is to propose a practical and accurate method for the characterization of silicon diode dose-rate dependence correction factors using ionization chamber measurements as a reference. METHODS: The robustness of ionization chambers for PDD measurements is used to quantify the dose-rate dependency of a diode detector. A mathematical formalism, which exploits the error induced in percent-depth ionization (PDI) curves for diodes by their dose-rate dependency, is developed to derive a dose-rate correction factor applicable to diode relative measurements. The method is based on the definition of the recombination correction factor given in the addendum to TG 51 and is applied to experimental measurements performed on a CyberKnife M6 radiotherapy unit using a PTW 60012 diode detector. A measurement-based validation is provided by comparing corrected PDI curves to measurements performed with a PTW 60019 diamond detector, which does not exhibit dose-rate dependence. RESULTS: Results of dose-rate correction factors for PDI curves, off-axis ratios (OARs), tissue-phantom ratios, and small field OFs are coherent with the expected behavior of silicon diode detectors. For all considered setups and field sizes, the maximum correction and the maximum impact of the uncertainties induced by the correction are obtained for OARs for the 60 mm collimator, with a correction of 2.5% and an uncertainty of 0.34%. For OFs, corrections range from 0.33% to 0.82% for all field sizes considered, and increase with the reduction of the field size. Comparison of PDI curves corrected for dose-rate and for in-depth beam quality variations illustrates excellent agreement with measurements performed using the diamond detector. CONCLUSION: The proposed method allows the efficient and precise correction of the dose-rate dependence of silicon diode detectors in the context of clinical relative dosimetry.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 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".