Dosimetric validation of Elekta Synergy 6 MV and 10 MV photon beam models in the Monaco
Bibliographic record
Abstract
This study assesses and compares the actual beam parameters with those calculated by the treatment planning system (TPS) on the Elekta Synergy linear accelerator at Phuc Thinh General Hospital, Vietnam. Photon beams of 6 MV and 10 MV were analyzed for varying field sizes and depths using a three-dimensional water tank phantom (48 × 48 × 48 cm 3 ) to measure the percentage depth dose (PDD) and profile for open fields (2 × 2 to 40 × 40 cm 2 ) and 60°-wedge fields (5 × 5 to 20 × 20 cm 2 ). Additionally, the same measurement configurations were accurately simulated in the Monaco TPS using a virtual water phantom of dimensions 60 × 40 × 60 cm 3 . Data were analyzed using the Monaco Commissioning Utility and IBA's MyQA-Accept software. The Gamma index method was set at 3 %/3 mm, 2 %/2 mm, and 1 %/1 mm criteria to compare calculated and measured point doses. For the 6 MV photon, excellent agreement between the measured and calculated PDD and profiles was observed across all field sizes. The gamma passing rates were nearly 100 % when using the 3 %/3 mm and 2 %/2 mm criteria for both the Monte Carlo (MC) and Collapse Cone (CC) algorithms. A similar pattern was observed for the 10 MV photon beam, demonstrating strong agreement in both the PDD and dose profiles with the 3 %/3 mm and 2 %/2 mm criteria using the CC algorithm. However, when applying the more stringent 1 %/1 mm criterion, the small field 2 × 2 cm 2 exhibited a significantly lower gamma pass rate and a higher output factor difference compared to the larger field sizes. Despite this, the output factor difference remained consistently below 1.3 % across all energies and field sizes. This study verifies the accuracy of the beam model and dose delivery while highlighting areas for improvement, such as optimizing the multi-leaf collimator (MLC). Continuous validation is recommended to maintain accuracy and treatment quality.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".