A convolution-superposition fluence model for the Siemens HD120 multi leaf collimator with application to a 3D VMAT dose engine
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it