Benchmarking of Monte Carlo model of 6 Mv photon beam produced by Siemens Oncor® linear accelerator: determination of initial electron beam parameters in comparison with measurement
Bibliographic record
Abstract
Introduction: The aim of this study was to investigate the initial electron beam parameters for Monte Carlo model of 6MV photon beam produced by Siemens Oncor® linear accelerator. Materials and Methods: In this study, the EGSnrc Monte Carlo user codes BEAMnrc and DOSXYZnrc were used. The beamnrc code were used for modelling of a 6 MV photon beam produced by Siemens Oncor® linac’s head and DOSXYZnrc code utilized for calculating dose distributions in a virtual water phantom. The simulations were started for 10×10 cm2 and 40×40 cm2 field sizes. First the electron energy was changed to match percent depth dose curves of simulations with those of measurements. Second the beam width of primary electron source was tweaked to match between dose profile curves of simulation with those of measurement. For data comparison a 1-dimensional Gamma analyses were used with criteria of 3%/ 3mm using an inhouse-matlab script. The gamma analyses were performed while dose distributions of Monte Carlo and measurements were set as an evaluated and reference dose. Results: The results of gamma analyses showed that for percent depth dose curves a passing rate of close to 100% evaluated points. Also for profile curves, the passing rates were above 95% of evaluated points. Therefore, all depth dose and dose profile curves were in good agreement. The agreements were found at 5.75 MeV primary electron and 0.35 cm beam width respectively. Conclusion: The Monte Carlo model of 6 MV photon beam produced by Siemens Oncor® linear accelerator was accurately benchmarked using measured data. Since the profile curves of large field sizes are much more sensitive to the variations of beam width than small field size, it is recommended that for tuning process both field sizes are considered.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".