A convolution-superposition fluence model for the Siemens HD120 multi leaf collimator with application to a 3D VMAT dose engine
Bibliographic record
Abstract
Abstract Purpose . To construct a fast-calculating fluence modelfor the Siemens HD120 multi leaf collimator (MLC) using convolution-superposition techniques, and to develop a 3D VMAT dose engine using this fluence model. This work offers analternative to time-consuming open-source Monte Carlo simulations for thosedeveloping in-house dose-calculating software for research or clinical needs. Methods . EPID-acquired images of sweeping-window and sweeping-checker field profiles were used to commission transmission, 2 Dinterleaf leakage, and tongue-and-groove maps specific to the HD120 MLC. These maps, along with a 2D head-scattermodel were incorporated into a convolution-superposition algorithm to provide a fluence model for the HD120 MLC. This fluence model was used to develop a 3D VMAT dose engine, where 3D pre-computed 6MV dose kernels (EGSnrc) and a 3D fluence curvature-correction map were incorporatedto calculate 3D VMAT doses in a 22 cm diameter cylindrical phantom. Four VMAT patient plans witha large range of PTV sizes (36 cc to 604 cc) were chosen to test the fluence model and dose engine. Results . Excellent agreement was observed between the simulated commissioning fields and measured EPID-responses. 2D 2%/2 mm gamma analysis yielded a 98.9% pass rate for 1 cm, 2 cm, and 4 cm sweeping-window fields. 2D 2%/2mm gamma analysis for outer/inner MLC leaves yielded 89.1%/77.0% and 95.2%/91.1% pass rates from 1 cm and 2 cm sweeping-checker fields. Mean 3%/3 mm gamma analysis showed excellent agreement between our dose engine and Eclipse (Acuros) regardless of PTV size: 98.7% pass rate, with 95.1% pass rate in the high-dose volume. Fluence calculation times were13.6 seconds per dynamic MLC field and 1.4 minutes/arc for 3D VMAT dose on a standard PC. Conclusions . A fast-calculating convolution-superposition fluence model has been commissioned for the Siemens HD120 MLC and incorporatedinto a 3D VMAT dose engine. This work can be used to facilitate the development of fast in-house dose-calculating software.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".