Poster — Thur Eve — 46: Monte Carlo model of the Novalis Classic 6MV stereotactic linear accelerator using the GATE simulation platform
Bibliographic record
Abstract
Monte Carlo (MC) simulation is accepted as the most accurate method to predict dose deposition when compared to other methods in radiation treatment planning. Current dose calculation algorithms used for treatment planning can become inaccurate when small radiation fields and tissue inhomogeneities are present. At our centre the Novalis Classic linear accelerator (linac) is used for Stereotactic Radiosurgery (SRS). The first MC model to date of the Novalis Classic linac was developed at our centre using the Geant4 Application for Tomographic Emission (GATE) simulation platform. GATE is relatively new, open source MC software built from CERN's Geometry and Tracking 4 (Geant4) toolkit. The linac geometry was modeled using manufacturer specifications, as well as in‐house measurements of the micro MLC's. Among multiple model parameters, the initial electron beam was adjusted so that calculated depth dose curves agreed with measured values. Simulations were run on the European Grid Infrastructure through GateLab. Simulation time is approximately 8 hours on GateLab for a complete head model simulation to acquire a phase space file. Current results have a majority of points within 3% of the measured dose values for square field sizes ranging from 6×6 mm2 to 98×98 mm2 (maximum field size on the Novalis Classic linac) at 100 cm SSD. The x‐ray spectrum was determined from the MC data as well. The model provides an investigation into GATE'S capabilities and has the potential to be used as a research tool and an independent dose calculation engine for clinical treatment plans.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.017 | 0.002 |
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".