Poster — Thur Eve — 27: Flattening Filter Free VMAT Quality Assurance: Dose Rate Considerations for Detector Response
Bibliographic record
Abstract
Introduction: Radiation detector responses can be affected by dose rate. Due to higher dose per pulse and wider range of mu rates in FFF beams, detector responses should be characterized prior to implementation of QA protocols for FFF beams. During VMAT delivery, the MU rate may also vary dramatically within a treatment fraction. This study looks at the dose per pulse variation throughout a 3D volume for typical VMAT plans and the response characteristics for a variety of detectors, and makes recommendations on the design of QA protocols for FFF VMAT QA. Materials and Methods: Linac log file data and a simplified dose calculation algorithm are used to calculate dose per pulse for a variety of clinical VMAT plans, on a voxel by voxel basis, as a function of time in a cylindrical phantom. Diode and ion chamber array responses are characterized over the relevant range of dose per pulse and dose rate. Results: Dose per pulse ranges from <0.1 mGy/pulse to 1.5 mGy/pulse in a typical VMAT treatment delivery using the 10XFFF beam. Diode detector arrays demonstrate increased sensitivity to dose (+./− 3%) with increasing dose per pulse over this range. Ion chamber arrays demonstrate decreased sensitivity to dose (+/− 1%) with increasing dose rate over this range. Conclusions: QA protocols should be designed taking into consideration inherent changes in detector sensitivity with dose rate. Neglecting to account for changes in detector response with dose per pulse can lead to skewed QA results.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.037 | 0.010 |
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".