A probabilistic approach for determining Monte Carlo beam source parameters: I. Modeling of a CyberKnife M6 unit
Bibliographic record
Abstract
Abstract Objective. During Monte Carlo modeling of external radiotherapy beams, models must be adjusted to reproduce the experimental measurements of the linear accelerator being considered. The aim of this work is to propose a new method for the determination of the energy and spot size of the electron beam incident on the target of a linear accelerator using a maximum likelihood estimation. Approach. For that purpose, the method introduced by Francescon et al (2008 Med. Phys. 35 504–13) is expanded upon in this work. Simulated tissue-phantom ratios and uncorrected output factors using a set of different detector models are compared to experimental measurements. A probabilistic formalism is developed and a complete uncertainty budget, which includes a detailed simulation of positioning errors, is evaluated. The method is applied to a CyberKnife M6 unit using four detectors (PTW 60012, PTW 60019, Exradin A1SL and IBA CC04), with simulations being performed using the EGSnrc suite. Main results. The likelihood distributions of the electron beam energy and spot size are evaluated, leading to E ˆ = 7.42 ± 0.17 MeV and F ˆ = 2.15 ± 0.06 mm . Using these results and a 95% confidence region, simulations reproduce measurements in 13 out of the 14 considered setups. Significance. The proposed method allows an accurate beam parameter optimization and uncertainty evaluation during the Monte Carlo modeling of a radiotherapy unit.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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".