Sci—Wed PM: Delivery—12: The Radiofrequency Noise from MLCs for a Linac‐MR System
Bibliographic record
Abstract
Introduction: This work reports on the feasibility of using an MLC for a linac‐MR system. The radiofrequency)RF(noise produced by a functioning MLC is a possible interference to an MR imaging system. The RF noise spectral density produced by two brushed motors used to drive MLC leaves and one brushless fan motor has been investigated as a function of applied magnetic field. Materials/methods: An electromagnet was used to subject two brushed motors from two MLC assemblies)a Varian 52‐leaf and a Millennium 120 leaf assembly(and one brushless fan motor to a variable magnetic field. The RF noise was measured while the motors moved. The RF noise was measured with a set of commercially available near field electromagnetic probes. In a separate investigation the 0.22 T MR of our linac‐MR system was used to image a phantom. Images were taken with the MLC not present and then again with the MLC present and thirteen leaves moving. Results/Discussion: When viewing the RF noise in the time domain we could see small spikes of measured noise when the two brushed MLC motors were running; no noise could be seen from the brushless fan motor. In the frequency domain, the Millennium MLC motor showed some noise above background. Images with a phantom showed no degradation from possible RF noise interference when 13 MLC leaves from a Varian 52‐leaf MLC assembly were brought close to the MR coil and were moved.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.332 | 0.122 |
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".