TU‐H‐BRA‐03: Performance of a Clinical Gridded Electron Gun in Magnetic Fields: Implications for MRI‐Linac Therapy
Bibliographic record
Abstract
Purpose: Recent advances towards MRI Linac radiotherapy have motivated a wide range of studies characterizing electromagnetic interactions between the two devices. One of the most sensitive components is the linac electron gun. To data, only non gridded (diode) guns have been investigated however, most linac vendors utilize gridded (triode) guns, which enable efficient and robust beam gating. The purpose of this study was to develop a realistic model of a gridded gun used clinically, and to characterize its performance in magnetic fields. Methods: The gridded electron gun used on Varian high energy machines was measured using 3D laser scanning quoted as accurate to 0.1mm. Based on the scane, a detailed CAD mode was developed. From this, key geometry was extracted and a FEM model was developed (Opera/SCALA). Next, the high voltage (HV), grid voltage, and emission current were read from six dose matched TrueBeam linacs for the 6X, 10X and 15X photon modes (0 B‐field). The mean values were used to represent each mode, which was simulated I constant magnetic fields from 0–200G in‐line, and 0–35G perpendicular. Results: Experimentally measured HV, grid voltage, and emission current from 6X, 10X and 15X modes were respectively: 15±.03kV, 10±.08kV, 11±.03kV; 93±7V, 41±3V, and 70±6V; 327±27mA, 129±10mA, and 214±19mA. The error in simulated emission current of each mode was 3%,6%, and 3%. For in‐line fields, 50% beam loss occurred at 114, 96, and 97G; for perpendicular; at 12, 13 and 14G. Sensitivity for a given geometry is primarily determined by HV setting. Conclusion: Future MRI‐Linac systems will almost certainly use gridded guns. We present the first model of a clinical gridded gun, and match the experimental emission current to within 6% across three different operating modes. This clinical gun shows increased sensitivity to magnetic fields than previous work,and different modes show different sensitivity.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".