Application of a Monte Carlo linac model in routine verifications of dose calculations
Bibliographic record
Abstract
The analysis of some parameters of interest in radiotherapy Medical Physics based on an experimentally validated Monte Carlo model of an Elekta Precise lineal accelerator was performed for 6 and 15 MV photon beams. The simulations were performed using the EGSnrc code. As reference for simulations, the values of the previously obtained optimal beam parameters (energy and FWHM) were used. Deposited dose calculations in water phantoms were done, on typical complex geometries commonly are used in acceptance and quality control tests, such as irregular and asymmetric fields. Parameters such as MLC scatter, maximum opening or closing position, and the separation between them were analyzed from calculations in water. Similarly simulations were performed on phantoms obtained from CT studies of real patients, making comparisons of the dose distribution calculated with EGSnrc and the dose distribution obtained from the computerized treatment planning systems used in routine clinical plans. All the results showed a great agreement with measurements, fi nding all of them within tolerance limits. These results allowed the possibility of using the developed model as a robust verifi cation tool for validating calculations in very complex situations, where the accuracy of the available TPS could be questionable.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".