MO‐F‐16A‐02: Simulation of a Medical Linear Accelerator for Teaching Purposes
Bibliographic record
Abstract
Purpose: Detailed functioning of linear accelerator physics is well known. Less well developed is the basic understanding of how the adjustment of the linear accelerator's electrical components affects the resulting radiation beam. Other than the text by Karzmark, there is very little literature devoted to the practical understanding of linear accelerator functionality targeted at the radiotherapy clinic level. The purpose of this work is to describe a simulation environment for medical linear accelerators with the purpose of teaching linear accelerator physics. Methods: Varian type lineacs were simulated. Klystron saturation and peak output were modelled analytically. The energy gain of an electron beam was modelled using load line expressions. The bending magnet was assumed to be a perfect solenoid whose pass through energy varied linearly with solenoid current. The dose rate calculated at depth in water was assumed to be a simple function of the target's beam current. The flattening filter was modelled as an attenuator with conical shape, and the time‐averaged dose rate at a depth in water was determined by calculating kerma. Results: Fifteen analytical models were combined into a single model called SIMAC. Performance was verified systematically by adjusting typical linac control parameters. Increasing klystron pulse voltage increased dose rate to a peak, which then decreased as the beam energy was further increased due to the fixed pass through energy of the bending magnet. Increasing accelerator beam current leads to a higher dose per pulse. However, the energy of the electron beam decreases due to beam loading and so the dose rate eventually maximizes and the decreases as beam current was further increased. Conclusion: SIMAC can realistically simulate the functionality of a linear accelerator. It is expected to have value as a teaching tool for both medical physicists and linear accelerator service personnel.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 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".