Development and validation of a BEAMnrc component module for accurate Monte Carlo modelling of the Varian dynamic Millennium multileaf collimator
Bibliographic record
Abstract
A new component module (CM), designated DYNVMLC, was developed to fully model the geometry of the Varian Millennium 120 leaf collimator using the BEAMnrc Monte Carlo code. The model includes details such as the leaf driving screw hole, support railing groove and leaf tips. Further modifications also allow sampling of leaf sequence files to simulate the movement of the multileaf collimator (MLC) leaves during an intensity modulated radiation therapy (IMRT) delivery. As an initial validation of the code, the individual leaf geometries were visualized by tracing particles through the component module and recording their position each time a leaf boundary was crossed. A model of the Varian CL21EX linear accelerator 6 MV photon beam incorporating the new CM was built with the BEAMnrc user code. The leaf material density and abutting leaf air gap were chosen to match simulated leaf leakage profiles with film measurements in a solid water phantom. Simulated depth dose and off-axis profiles for a variety of MLC defined static fields agreed to within 2% with ion chamber and diode measurements in a water phantom. Simulated dose distributions for IMRT intensity patterns delivered using both static and dynamic techniques were found to agree with film measurements to within 4%. A comparison of interleaf leakage profiles for the new CM and an equivalent leaf model using the existing VARMLC CM demonstrated that the simplified geometry of VARMLC is not able to accurately predict the details of the MLC leakage for the 120 leaf collimator.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".