Gantry angle determination during arc IMRT: evaluation of a simple EPID‐based technique and two commercial inclinometers
Bibliographic record
Abstract
The increasing popularity of intensity-modulated arc therapy (IMAT) treatments requires specifically designed linac quality assurance (QA) programs. Gantry angle is one of the parameters that has a major effect on the outcome of IMAT treatments since dose reconstruction for patient-specific QA relies on the gantry angle; therefore, it is essential to ensure its accuracy for correct delivery of the prescribed dose. In this study, a simple measurement method and algorithm are presented for QA of gantry angle measurements based on integrated EPID images acquired at distinct gantry angles and cine EPID images during an entire 360° arc. A comprehensive study was carried out to evaluate this method, as well as to evaluate two commercially available inclinometers (NG360 and IBA GAS supplied in conjunction with popular array dosimeters Delta4 and MatriXXEvolution, respectively) by comparing their simultaneous angle measurement results with the linac potentiometer readouts at five gantry speeds. In all tested measurement systems, the average differences with the reference angle data were less than 0.3° in static mode. In arc mode, at all tested gantry speeds the average difference was less than 0.1° for the IBA GAS and the proposed EPID-based method, and 0.6° for the NG360 after correction for the inherent systematic time delay of the inclinometer. The gantry rotation speed measured by the three independent systems had an average deviation of about 0.01°/s from the nominal gantry speed.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".