Radio frequency noise from an MLC: a feasibility study of the use of an MLC for linac-MR systems
Bibliographic record
Abstract
Currently several groups are actively researching the integration of a megavoltage teletherapy unit with magnetic resonance (MR) imaging for real-time image-guided radiotherapy. The use of a multileaf collimator (MLC) for intensity-modulated radiotherapy for linac-MR units must be investigated. The MLC itself will likely reside in the fringe field of the MR and the motors will produce radio frequency (RF) noise. The RF noise power spectral density from a Varian 52-leaf MLC motor, a Varian Millennium MLC motor and a brushless fan motor has been measured as a function of the applied magnetic field using a near field probe set. For the Varian 52-leaf MLC system, the RF noise produced by 13 of 52 motors is studied as a function of distance from the MLC. Data are reported in the frequency range suitable for 0.2-1.5 T linac-MR systems. Below 40 MHz the Millennium MLC motor tested showed more noise than the Varian 52-leaf motor or the brushless fan motor. The brushless motor showed a small dependence on the applied magnetic field. Images of a phantom were taken by the prototype linac-MR system with the MLC placed in close proximity to the magnet. Several orientations of the MLC in both shielded and non-shielded configurations were studied. For the case of a non-shielded MLC and associated cables, the signal-to-noise ratio (SNR) was reduced when 13 of 52 MLC leaves were moved during imaging. When the MLC and associated cables were shielded, the measured SNR of the images with 13 MLC leaves moving was experimentally the same as the SNR of the stationary MLC image. When the MLC and cables are shielded, subtraction images acquired with and without MLC motion contains no systematic signal. This study illustrates that the small RF noise produced by functioning MLC motors can be effectively shielded to avoid SNR degradation. A functioning MLC can be incorporated into a linac-MR unit.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".