MO-F-BRB-05: Monte Carlo Modeling of the Novalis TX Stereotactic Radiosurgery Mode
Bibliographic record
Abstract
Purpose: To model the stereotactic mode of the Varian-Brainlab Novalis TX linear accelerator using the BEAMnrc Monte Carlo user code Methods: The EGSnrc Monte Carlo user codes BEAMnrc and DOSXYZnrc were used for photon simulations and dose calculations, respectively. A Monte Carlo model of a Varian Clinac 21 EX was modified to model the stereotactic radiosurgery (SRS) mode of the Novalis, taking into account the smaller dimensions of the SRS flattening filter and limited field sizes. The parameters of source such as energy, size and angular spread, were readjusted following a new procedure outlined by Almberg et al, 2012. A component module, DYNVMLC, previously used to model the Varian Millennium 120 multi-leaf collimator (MLC), was reprogrammed to include the four leaf types of the Varian high definition 120 leaf MLC. Interleaf air-gap and leaf density were adjusted to match interleaf leakage profiles measured with EBT2 film. Subsequent validation included profiles, percent depth dose curves and output factors measured with ion chambers, and other film measurements. Results: From PDD measurements, the energy of the incident electron beam was determined to be 6.6 MeV. From penumbra measurements, the electron radial intensity distribution, given as the full width at half maximum of a Gaussian distribution, was found to be 0.7 mm (cross-plane) and 0.8 mm (in-plane). From profiles in water, the mean angular spread had to be adjusted to 1.27° to achieve an acceptable match. The interleaf air-gap and the density of the leaves of the HDMLC were determined to be 0.0047 cm and 18.5 g/cm3, respectively. Conclusions: The Almberg procedure was successfully implemented in determining the electron beam parameters to model the Novalis Tx's SRS mode. Dose profiles simulated with the new HDMLC component module agreed with measurements within 2%.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".