Sci-Thurs PM: Delivery-08: Investigation of the source of RF noise from a modulator for an MR-linac project
Bibliographic record
Abstract
INTRODUCTION: The next significant step in the advancement of IGRT is the integration of an MRI with a linac. The MRI-linac will provide images with exquisite soft tissue contrast in real-time during treatment. A possible problem associated with the proposed integration is the RF noise generated by the linac. This noise could interfere with the received signals of the MRI producing deleterious effects in the image quality. The work herein is concerned with understanding the processes involved in the RF noise production and the magnitude and frequency of this RF noise in the modulator of a linac. MATERIALS/METHODS: A software programming environment, MultiSIM, was used to model the electronic components of a modulator. Several Current and Voltage waveforms from the modulator were measured with an oscilloscope and compared with the corresponding results from the modulator model for validation. Finally, RF noise generated by the modulator was measured using field probes, which permits the frequency components of the measured and simulated modulator waveforms to be compared with the measured RF noise. RESULTS/DISCUSSION: The modeled PFN charging current and voltage, and klystron current show good agreement with measurements, with the exception of the tail of the klystron voltage signal. Once the model has been validated in both the time and frequency domains, future work will entail predicting pulse shape changes when, and if, modifications to the modulator are made. Specifically, modifications will be made which shift and/or reduce the RF noise in the frequency range of interest for a 0.2T MRI.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.016 | 0.005 |
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".