Magnetic decoupling of the linac in a low field biplanar linac‐MR system
Bibliographic record
Abstract
PURPOSE: The integration of a low field biplanar magnetic resonance (MR) imager and linear accelerator (linac) causes magnetic interference at the linac due to the MR fringe fields. In order to eliminate this interference, passive and active magnetic shielding designs are investigated. METHODS: The optimized design of passive magnetic shielding was performed using the finite element method. The design was required to achieve no greater than a 20% electron beam loss within the linac waveguide and electron gun, no greater than 0.06 T at the multileaf collimator (MLC) motors, and generate a distortion of the main MR imaging volume of no greater than 300 ppm. Through the superposition of the analytical solution for a single current carrying wire loop, active shielding designs in the form of three and four sets of coil pairs surrounding the linac waveguide and electron gun were also investigated. The optimized current and coil center locations that yielded the best cancellation of the MR fringe fields at the linac were determined using sequential quadratic programming. RESULTS: Optimized passive shielding in the form of two steel cylinders was designed to meet the required constraints. When shielding the MLC motors along with the waveguide and electron gun, the thickness of the cylinders was less than 1 mm. If magnetically insensitive MLC motors are used, no MLC shielding would be required and the waveguide shield (shielding the waveguide and electron gun) became 1.58 mm thick. In addition, the optimized current and coil spacing for active shielding was determined for both three and four coil pair configurations. The results of the active shielding optimization produced no beam loss within the waveguide and electron gun and a maximum MR field distortion of 91 ppm over a 30 cm diameter spherical volume. CONCLUSIONS: Very simple passive and active shielding designs have been shown to magnetically decouple the linac from the MR imager in a low field biplanar linac-MR system. The MLC passive shielding produced the largest distortion of the MR field over the imaging volume. With the use of magnetically insensitive motors, the MR field distortion drops substantially since no MLC shield is required. The active shielding designs yielded no electron beam loss within the linac.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".