Radio frequency noise from clinical linear accelerators
Bibliographic record
Abstract
There is a great deal of interest in image-guided radiotherapy (IGRT), and to advance the state of IGRT, an integrated linear accelerator-magnetic resonance (linac-MR) system has been proposed. Knowledge of the radiofrequency (RF) emissions near a linac is important for the design of appropriate RF shielding to facilitate the successful integration of these two devices. The frequency spectra of both electric and magnetic fields of RF emission are measured using commercially available measurement probes near the treatment couch in three clinical linac vaults with distinct physical layouts. The magnitude spectrum of the RF power emitted from these three linacs is then estimated. The electric field spectrum was also measured at several distances from the linac modulator in order to assess the effects of variations in spatial location in the treatment vault. A large fraction of RF power is emitted at frequencies below 5 MHz. However, the measured RF power at the Larmor frequency (8.5 MHz) of the proposed 0.2 T MR in the linac-MR (0.4-14.6 microW m(-2)) is still large enough to cause artifacts in MR images. Magnetron-based linacs generally emit much larger RF power than klystron-based linacs. In the frequency range of 1-50 MHz, only slight variation in the measured electric field is observed as a function of measurement position. This study suggests that the RF emissions are strong enough to cause image artifacts in MRI systems.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.008 | 0.003 |
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".