Examining Beam Matching in the Commissioning of a Halcyon Accelerator
Bibliographic record
Abstract
This study aims to describe the steps needed to be made in developing a commissioning report of a Halcyon linear accelerator utilizing the manufacturer’s golden beam data (GBD) as a reference in making the evaluation. The platform herein has determined the performance alignment of our local machine with the GBD obtained through comprehensive analyses. This made use of the gamma index and relative dose difference. This paper details the methodologies and outcomes of comparing local measurements against GBD during commissioning.For the Halcyon linear accelerator, dosimetric data, including percentage depth doses, dose profiles, and output factors, were acquired using a three-dimensional scanning water tank and various ionization chambers. The GBD were exported from the treatment planning system and compared to the measurements. To evaluate the agreement between the GBD and measurements, gamma index and relative dose difference analyses were conducted.For field sizes greater than 4 × 4 cm2, percentage depth doses and beam profiles, the gamma indices between GBD and measurements were less than 1%/1 mm. The gamma indices were found to be slightly greater for field sizes 2 × 2 cm2 and 4 × 4 cm2, remaining within 2%/2 mm, satisfying the American Association of Physicists in Medicine Medical Physics Practice Guideline 5 for commissioning and quality assurance of mega-volt photon beams. Deviations in the output factor between the GBD and measurements were not significant, remaining within 1%.The GBD data were evaluated in the commissioning of a Halcyon linear accelerator, with analyses being made of the gamma index and relative dose difference. The gamma index analysis is shown to be an effective method for comprehensively evaluating deviations between the GBD and measurements in the beam matching process.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".