Technical Note: A novel quality assurance test to identify gantry angle inaccuracies in respiratory‐gated <scp>VMAT</scp> treatments
Bibliographic record
Abstract
PURPOSE: During respiratory-gated volumetric-modulated arc therapy (VMAT), the radiation beam is turned off each time the target exits the gating window. At the same time, the gantry slows, stops, and rewinds before the beam is turned back on. A quality assurance (QA) test was developed to detect inaccuracies in the gantry angle position between beam-off and beam-on events during respiratory-gated VMAT. METHODS: 504 phantom mounted at isocenter. A homogeneous dose was delivered to the films through a 2 cm × 10 cm slit in the jaws using a respiratory-gated VMAT arc without the multileaf collimator. A periodic breathing cycle was used. Errors in gated delivery ranging from 0.5 to 5° were simulated by delivering nongated arcs with the same field size with over- and underlapping sections of 0.5-5°. The simulated errors were used to define QA levels to analyze the gated delivery. RESULTS: The QA test was capable of detecting errors as small as 0.5°. The test was delivered to three Varian TrueBeam™ linacs, and no gantry angle inaccuracies greater than or equal to 0.5° were detected on any of the films. CONCLUSIONS: A QA test capable of detecting gantry angle inaccuracies at beam-off and subsequent beam-on as small as 0.5° was developed and implemented for Varian TrueBeam™ linacs.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".