Evaluation of Elekta Agility multi‐leaf collimator performance using statistical process control tools
Bibliographic record
Abstract
PURPOSE: To evaluate the performance and stability of Elekta Agility multi-leaf collimator (MLC) leaf positioning using a daily, automated quality control (QC) test based on megavoltage (MV) images in combination with statistical process control tools, and identify special causes of variations in performance. METHODS: Leaf positions were collected daily for 13 Elekta linear accelerators over 11-37 months using the automated QC test, which analyzes 23 MV images to determine the location of MLC leaves relative to radiation isocenter. Leaf positioning stability was assessed using individual and moving range control charts. Specification levels of ±0.5, ±1, and ±1.5 mm were tested to determine positional accuracy. The durations between out-of-control and out-of-specification events were determined. Peaks in out-of-control leaf positions were identified and correlated to servicing events recorded for the whole duration of data collection. RESULTS: Mean leaf position error was -0.01 mm (range -1.3-1.6). Data stayed within ±1 mm specification for 457 days on average (range 3-838) and within ±1.5 mm for the entire date range. Measurements stayed within ±0.5 mm for 1 day on average (range 0-17); however, our MLC leaves were not calibrated to this level of accuracy. Leaf position varied little over time, as confirmed by tight individual (mean ±0.19 mm, range 0.09-0.43) and moving range (mean 0.23 mm, range 0.10-0.53) control limits. Due to sporadic out-of-control events, the mean in-control duration was 2.8 days (range 1-28.5). A number of factors were found to contribute to leaf position errors and out-of-control behavior, including servicing events, beam spot motion, and image artifacts. CONCLUSIONS: The Elekta Agility MLC model was found to perform with high stability, as evidenced by the tight control limits. The in-specification durations support the current recommendation of monthly MLC QC tests with a ±1 mm tolerance. Future work is on-going to determine if performance can be optimized further using high-frequency QC test results to drive recalibration frequency.
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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 | 0.008 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".