Small field dosimetric characterization of a new 160-leaf MLC
Bibliographic record
Abstract
The goal of this work was to perform a 6 MV small field characterization of the new Agility 160-leaf multi-leaf collimator (MLC) from Elekta. This included profile measurement analysis and central axis relative output measurements using various diode detectors and an air-core fiber optic scintillation dosimeter (FOD). Data was acquired at a depth of 10.0 cm for field sizes of 1.0, 0.9, 0.8, 0.7, 0.6 and 0.5 cm. Three experimental data sets, comprised of five readings, were made for both the relative output and profile measurements. Average detector-specific output ratios (OR[overline](f(clin))(det))) were calculated with respect to a field size of 3.0 cm and small field replacement correction factors (k(f(clin),f(msr))(Q(clin),Q(msr))) derived for the diodes using the scintillation dosimeter readings as the baseline. The standard experimental uncertainty on OR[overline](f(clin))(det)) was calculated at a 90% confidence interval and the coefficient of variation (CV) used to characterize the detector-specific measurement precision. The positional accuracy of the collimation system was also investigated by analyzing the repeated profile measurements and field width constancy investigated as a function of collimator rotation. For comparison the output and profile measurements were repeated using the Elekta 80-leaf MLCi2 on a beam matched linac at 6 MV. The measured OR[overline](f(clin))(det)) varied as a function of detector and MLC design. At the smallest field size the standard experimental uncertainty on OR[overline](f(clin))(det)) was consistent across all detectors at approximately 0.5% and 1.0% for Agility and MLCi2 collimators respectively. The CV associated with the FOD measurements were greater than that of the diodes but did not translate into increased measurement uncertainty. At the smallest field size, the diode detector correction factors were approximately 2% greater for MLCi2 than that required for the Agility. Profile data revealed the Agility MLC to have a greater positional reproducibility than both the MLCi2 and the linac diaphragms (jaws), as also reflected in the experimental uncertainties on OR[overline](f(clin))(det)). The relative output, profile widths and associated uncertainties were all found to differ between the two MLC systems investigated, as were the field size specific diode detector replacement correction factors. The data also clearly showed that the Agility 160-leaf MLC performs to a tighter positional tolerance than the MLCi2.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".