Sci‐Fri AM: YIS‐09: The effect of magnetic interference on a coupled MR‐linac system: Optimization of 3D FEM linac model
Bibliographic record
Abstract
PURPOSE: The coupling of a 0.2T bi-planar Magnetic Resonance Imager and medical linear accelerator (linac) is proposed to provide real-time Image Guided Radiotherapy. This coupling necessitates the linac to be within the fringe fields of the bi-planar magnets causing magnetic interference. The design and optimization of the minimum required shielding is necessary to reduce the fringe field magnitudes to a point where a clinically useful radiation beam is produced. METHOD: A first step to designing shielding is the full 3D radio-frequency modeling of the linac waveguide using the Finite Element Method. Various optimizations were performed on the linac model in order to achieve a desired resonant frequency, π/2 phase shift per cavity and other desired properties. An accelerating cavity (AC) and coupling cavity (CC) was first optimized in 3D to have identical resonant frequencies before the full 3D model was generated. RESULTS: In order to increase the capture efficiency of the injected electrons, the electric field in the first AC was reduced by shifting the first CC towards the gun end of the linac. The input waveguide AC dimensions were adjusted to account of the additional coupling iris and the last full AC had its gap length decreased. CONCLUSION: This work is the first step to determining the minimum magnetic shielding required to produce a clinically useful radiation beam from a coupled MR-Linac system. The fully optimized 3D model more accurately calculates the electric and magnetic field values since it includes the effects of coupling.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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".