Improved peripheral dose calculation accuracy for a small MLC field brought by the latest commercial treatment planning system
Bibliographic record
Abstract
A recently released Pinnacle treatment planning system software, v7.4f includes some new physics features such as modeling of the rounded multi-leaf collimator (MLC) leaf ends and the tongue-and-groove structure between leaves. In this study, the above physics modeling improvements were verified by comparing the peripheral dose profiles for the small MLC fields calculated by the new Pinnacle v7.4f and the old Pinnacle v6.2b with those obtained from measurements experimentally. Three test MLC fields with different jaw sizes were prepared, and specific dose profiles (along cross-line, in-line and diagonal axis) at different depths were measured using a Varian 21 EX linear accelerator with 120-leaf Millennium MLC, big scanning water tank and photon diode. Estimated dose profiles for the test fields were calculated using Pinnacle v6.2b and v7.4f. By comparing the measured and calculated results, it was found that both v6.2b and v7.4f performed well in calculating the cross-line (along the gap between the longitudinal lengths of two leaves) and diagonal axis dose profiles at different depths. However, v7.4f gave calculated dose values closer to the measured field for in-line (gap between junctions of two rounded leaf ends) axis dose profiles at different depths. For the shape of the profile along the in-line axis, v7.4f calculated a flat “platform” dose profile of about 34.3% (inter-bank leakage) at depth dmax> beyond the MLC field edge using a clinical dose grid size of 0.4×0.4×0.4cm3, compared to the “zigzag” dose profile varying between 35.4% and 42.1% measured using the water tank and diode. However, both Pinnacle v6.2b and v7.4f calculated the percentage depth dose for the test fields well compared to the measurements.
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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 | 0.004 |
| 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) | 0.005 | 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".