WE‐G‐214‐01: Investigation of Magnetic Shielding for a 6MV In‐Line Linac for a Parallel Linac‐MR Configuration
Bibliographic record
Abstract
Purpose: To achieve real‐time tumor tracking, our group is integrating a 6 MV in‐line linac with an MR imager, where the linac is placed on the MR magnetˈs central axis to create a linac‐MR system. Because of the linacˈs location, it experiences large fringe magnetic fields parallel to the electron trajectories in the waveguide. To minimize the adverse affects of these fields on the linac, both passive and active magnetic shielding was investigated. Methods: COMSOL was used to model 3D fringe magnetic fields from a 0.5T PARAmed openMRTM magnet with and without magnetic shielding in place. These fields were added to our electron gun model (created previously using OPERA/SCALA‐3D), and to our waveguide model (created previously using COMSOL and PARMELA) to optimize both passive and active shielding. Results: The unshielded linac electron gun experiences up to a 0.011 T longitudinal field. The injection electron beamˈs rms‐emittance increases from 0.299pi mm‐mrad at 0 T to 2.834pi mm‐mrad at 0.011 T. This non‐laminar beam results in a decreased target current to 84% of nominal. A 5 mm thick and 141.5 mm long passive steel shield reduces the beamˈs rms‐emittance to 0.450pi mm‐mrad. In this configuration, >99% of the target current is recovered. With an optimized active shield (two current rings) in place, the rms‐emittance was reduced to 0.308pi mm‐mrad and the target current was recovered to 100.2% of nominal. Homogenous fringe magnetic field strengths up to 0.2 T were also modeled to test the passive shielding under larger‐field conditions. A 5 mm thick passive shield is sufficient to magnetically shield the linac at 0.04 T at which the target current decreased to only 97.6%. Conclusions: Both passive and active magnetic shielding can be used in our linac‐MR system to recover any beam loss resulting from MR fringe magnetic fields.
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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) | 0.000 | 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".