Sci—Thur PM: YIS — 10: Magnetically Shielding the Linac in a Bi‐Planar Linac‐MR System
Bibliographic record
Abstract
Purpose: A low field bi‐planar linac‐MR system has been proposed to achieve real‐time image guided radiotherapy. The close proximity of the linac to the MR imager necessitates magnetic shielding to reduce the transverse magnetic fringe field magnitude. Method: Optimized magnetic shielding was designed based on simulations of linac performance in transverse magnetic fields. The linac simulation consisted of an electron gun (EGN2w, COMSOL, PARMELA), and a linac waveguide (COMSOL). The radio‐frequency field within the waveguide was calculated using COMSOL. The linac simulation generated an electron phase space at the linac target which was inputted into BEAMnrc and DOSXYZnrc to simulate dose distributions. Validation of the linac simulation was performed against measurements from a Varian 600C. Passive magnetic shielding was designed in COMSOL and active shielding was designed in the form of four current carrying coil pairs surrounding the linac. Results: The linac simulation at 0 G provided excellent agreement to all electrical and dosimetric measurements. The dosimetric effects of profile asymmetry or lateral shift caused by transverse magnetic fields on the linac were corrected through a shifting of the electron focal spot (as done in linac commissioning) and through asymmetric jaw positions. With no net dosimetric effects caused by transverse magnetic fields, linac performance was only limited by beam losses which increased with magnetic field strength. Passive and active shielding was optimized meeting all design constraints. Conclusion: Magnetic decoupling of the linac in a low field bi‐planar linac‐MR system has been accomplished using either passive or active shielding.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.038 | 0.014 |
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".