SU‐GG‐J‐161: Radiofrequency Interference Between a Linac and MRI
Bibliographic record
Abstract
Purpose: Modern radiotherapy relies heavily on 3‐D image based guidance to reduce treatment margins and to conform the dose distribution to the target shape. Due to the poor soft‐tissue contrast and non‐real‐time nature of x‐ray and ultrasound based image guidance, several groups are integrating an MRI and linear accelerator. One of several unresolved technical issues is RF interference between the two devices. The purpose of this presentation is to show that LINAC RF correlates with the operation of certain components of its pulsed power modulator (PPM). Method and Materials: Electric (E) and magnetic (H) field temporal patterns (RF signals) were measured using separate E‐field and H‐field probes on three medical LINAC configurations: klystron and PPM in the same room; klystron and PPM in different rooms; and magnetron and PPM in the same room. Time resolved high voltage power supply (HVPS) current, pulse forming network (PFN) voltage, klystron current and klystron voltage, and magnetron current were all measured coinciding with the RF signals. All measured signals were compared in time and frequency domains. Results: For a klystron powered LINAC, correlation between the HVPS current, and E and H field signals were observed in both the time and frequency domains. No correlation was observed between RF signals and the klystron current pulse. For a magnetron powered LINAC, correlation among HVPS current, magnetron current and RF signals was observed. RF signals coincided in time with the magnetron current, but the frequency spectrum were dissimilar. Conclusion: The results suggest that charging of the PFN by the HVPS current is a mechanism of RF noise generation in the klystron based LINAC operation. For magnetron based PPMs the results further suggest the magnetron current pulse generates RF noise, however dissimilar frequency spectrum indicates an indirect and more complicateds mechanism requiring further investigation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 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".