SU-E-T-573: Philips Pinnacle Photon Beam Modeling for Elekta Agility 160 Leaf Multi-Leaf Collimator
Bibliographic record
Abstract
Purpose: The new Elekta Agility 160 leaf multi-leaf collimator was modeled for three photon energies using Philips Pinnacle treatment planning software. Methods: The modeling data set was acquired on the Agility for three energies (6, 10 and 18 MV) based on Pinnacle requirements. In addition, a validation data set was also acquired based on the recommendation of Venselaar et al. (2001). Most model parameters were optimized using Pinnacle automodeling tools. Custom made software was used to analyse the different beam models performance. The analysis uses a statistical approach as described by Venselaar et al. (2001).The model was also tested for VMAT using plans generated for TG119 (Ezzell et al., 2009) cases and a series of reference patients (prostate, head-and-neck and anal canal). Quality assurance measurements were acquired with both Gafchromic film EBT3 and Sun Nuclear ArcCHECK and analysed using the gamma index. Results: The model went to a series of iterations until good agreement was achieved. The automodeling sequence could not provide proper parameters for the electron contamination component of the 18 MV beam model. Splitting the 18 MV model by different field size gave better results. The Gaussian height and width of the scatter source were adjusted manually in order to improve output factor agreement. It was also necessary to shift the rounded leaf end spec by 1 mm in order to improve VMAT QAs. It was discovered that the guard leaf option in Pinnacle needed to be turned off or the planning system would otherwise provide undeliverable plans. Conclusion: The new Elekta Agility 160 leaf linac was successfully modeled using Philip Pinnacle software. The final model calculation was in good agreement with the modeling and validation data sets. The only exception is for asymmetric wedge fields for which clinical limitations must be implemented.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".