SU‐E‐T‐627: Precision Modelling of the Leaf‐Bank Rotation in Elekta's Agility MLC: Is It Necessary?
Bibliographic record
Abstract
Purpose: To demonstrate the method used to determine the leaf bank rotation angle (LBROT) as a parameter for modeling the Elekta Agility multi‐leaf collimator (MLC) for Monte Carlo simulations and to evaluate the clinical impact of LBROT. Methods: A detailed model of an Elekta Infinity linac including an Agility MLC was built using the EGSnrc/BEAMnrc Monte Carlo code. The Agility 160‐leaf MLC is modelled using the MLCE component module which allows for leaf bank rotation using the parameter LBROT. A precise value of LBROT is obtained by comparing measured and simulated profiles of a specific field, which has leaves arranged in a repeated pattern such that one leaf is opened and the adjacent one is closed. Profile measurements from an Agility linac are taken with gafchromic film, and an ion chamber is used to set the absolute dose. The measurements are compared to Monte Carlo (MC) simulations and the LBROT is adjusted until a match is found. The clinical impact of LBROT is evaluated by observing how an MC dose calculation changes with LBROT. A clinical Stereotactic Body Radiation Treatment (SBRT) plan is calculated using BEAMnrc/DOSXYZnrc simulations with different input values for LBROT. Results: Using the method outlined above, the LBROT is determined to be 9±1 mrad. Differences as high as 4% are observed in a clinical SBRT plan between the extreme case (LBROT not modeled) and the nominal case. Conclusion: In small‐field radiation therapy treatment planning, it is important to properly account for LBROT as an input parameter for MC dose calculations with the Agility MLC. More work is ongoing to elucidate the observed differences by determining the contributions from transmission dose, change in field size, and source occlusion, which are all dependent on LBROT. This work was supported by OCAIRO (Ontario Consortium of Adaptive Interventions in Radiation Oncology), funded by the Ontario Research Fund.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.002 | 0.000 |
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".