Poster - Thur Eve - 10: Long term stability of VMAT quality assurance parameters using an EPID
Bibliographic record
Abstract
The rapidly growing use of volumetric modulated arc therapy (VMAT) treatments in radiation therapy calls for a quantitative, automated, and reliable quality assurance (QA) procedure that can be used routinely in the clinical setting. In this work, we present a series VMAT QA procedures used to assess dynamic multi-leaf collimator (MLC) positional accuracy, variable dose-rate accuracy, and MLC leaf speed accuracy. The QA procedures were performed using amorphous silicon electronic portal imaging devices (EPID) to determine the long term stability of the measured parameters on two Varian linear accelerators. The measurements were repeated weekly on both linear accelerators for a period of three months and the EPID images were analyzed using custom Matlab software. The results of the picket fence tests indicate that MLC leaf positions can be identified to within 0.11 mm and 0.15 mm for static gantry delivery and VMAT delivery respectively. In addition, the dose-rate, gantry speed and MLC leaf speed tests both show very good stability over the measurement period. The measurements thus far, suggest that a number of the dosimetry tests may be suitable for quarterly QA for Varian iX and Trilogy linacs. However, additional measurements are required to confirm the frequency with which each test is required for safe and reliable VMAT delivery at our centre.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".