TU‐D‐352‐05: The Effect of Magnetic Interference On a Coupled MR‐Linac System: 3D Modeling of RF in a Linac
Bibliographic record
Abstract
Purpose: In order to provide real‐time Image Guided Radiotherapy (IGRT), the coupling of a 0.2T bi‐planar Magnetic Resonance Imager (MRI) and medical linear accelerator (linac) is proposed. This integration forces the linac to be within the fringe fields of the bi‐planar magnets causing magnetic interference. Appropriate shielding must be designed and optimized in order to reduce the fringe field magnitudes to a point where a clinically useful radiation beam is produced. Method and Materials: A first step to designing shielding is the full 3D radio‐frequency (rf) modeling of the linac waveguide using the Finite Element Method (FEM). The main accelerating cavities were modeled in 3D using COMSOL Multiphysics and compared to the widely used program Poisson Superfish. The full waveguide was then modeled in 3D and the coupling coefficients were determined. Results: A comparison of linac important parameters (i.e. resonant frequency, power dissipation, stored energy, Q value, shunt impedance and transit time factor) were made between the 3D model in COMSOL and that of Poisson Superfish. All values were found to be within 0.8% where the differences are attributed to differences in meshing between the two programs. A linac model consisting of 5 and one half accelerating cavities with 5 coupling cavities was then generated. A 3D instead of a 2D model was used since it is more accurate in determining the coupling coefficients between the accelerating and coupling cavities. Conclusion: This work is the first step to determining the magnetic shielding required in producing a clinically useful radiation beam from a coupled MR‐Linac system. The extracted parameters from the 3D model were found to be less than 1% different from the benchmarked program Poisson Superfish. This 3D model is however inherently more accurate since the full modeling of coupling between accelerating and coupling cavities has been achieved.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".