Monitor unit calculation in electron therapy using Monte Carlo Simulation: a GUI for the phase-space field trimming
Bibliographic record
Abstract
Abstract Purpose: We developed a graphical user interface (GUI) for electron phase-space field trimming using Monte Carlo simulations. This GUI can be used for monitor unit (MU) calculation in electron therapy. Methods: The GUI and electron field trimming algorithm were developed using MATLAB and C code. Phase-space files for the electron fields were generated using the EGSnrc code based on a Varian 21EX Linac with variables of applicator size, field size and energy. Verification of the alogrithm was carried out by comparing the relative output factor, which was used for MU calculation, predicted by Monte Carlo simulations and from actual measurements. Results: Our electron field trimming algorithm was found to be about five times faster than the original Monte Carlo simulation. Clinically, the GUI performed best when using voxel size ≥ 0.3 × 0.3 × 0.3 cm 3 , and field size larger than 2 cm in radius based on an acceptable deviation of 2%. Conclusion: A GUI for generating irregular field for MU calculation using Monte Carlo simulations was created as a user-friendly tool in electron therapy.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.030 | 0.005 |
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".