TU-D-224C-02: Monte Carlo Direct Aperture Optimization (MC-DAO) for IMRT
Bibliographic record
Abstract
Purpose: To improve the accuracy/efficiency of IMRT planning by combining Monte Carlo (MC) dose calculation with direct aperture optimization (DAO). Method and Materials: A 6 MV beam arrangement is applied to an IMRT phantom and patient examples. A phase space is calculated below the secondary jaws of a virtual Varian 21EX linac by MC simulation (BEAMnrc code (NRC, Canada)). The phase space is subdivided into 2.5×5.0 mm2 beamlets and the dose distribution from each beamlet is calculated to organs‐of‐interest within the patient/phantom using DOSXYZnrc. This information is input into DAO inverse planning software. The DAO includes multileaf collimator transmission and leaf motion limitations as it modifies the shape/weight of the treatment apertures. The optimized leaf sequence requires no additional leaf motion calculation step. A final forward MC dose calculation is performed. The MC doses are verified with ion chamber and film measurement. MC‐DAO is applied to a difficult phantom geometry, namely a c‐shaped target with embedded organ‐at‐risk located directly adjacent to a 5.0cm‐thick air slab. Clinical sites include nasopharynx and lung.Results: The MC optimization allows for accurate modeling of the electronic disequilibrium introduced by the air cavities. For the phantom example, MC reveals that the plan optimized with a pencil beam (PB) algorithm fails to provide adequate coverage to the PTV close to the air cavity, whereas the MC‐DAO plan demonstrates adequate coverage. For the nasopharynx, the PB plan showed errors during ion chamber/film verification, probably due to the small (∼5×4 cm2) fields whereas the MC‐DAO plan showed good agreement. The reduction in monitor units for MC‐DAO plans is 20 – 40% compared to a commercial fluence‐based (PB) treatment planning system. Conclusion:MC simulation generates accurate input data for IMRT inverse treatment planning in difficult‐to‐calculate regions. The addition of DAO results in a more efficient treatment plan delivery.
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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 | 0.000 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".