Comparison of MEMS switches and PIN diodes for switched dual tuned RF coils
Bibliographic record
Abstract
Purpose To evaluate the performance of micro‐electromechanical systems (MEMS) switches against PIN diodes for switching a dual‐tuned RF coil between 19F and 1H resonant frequencies for multi‐nuclear lung imaging. Methods A four‐element fixed‐phase and amplitude transmit–receive RF coil was constructed to provide homogeneous excitation across the lungs, and to serve as a test system for various switching methods. The MR imaging and RF performance of the coil when switched between the 19F and 1H frequencies using MEMS switches, PIN diodes and hardwired configurations were compared. Results The performance of the coil with MEMS or PIN diode switching was comparable in terms of RF measurements, transmit efficiency and image SNR on both 19F and 1H nuclei. When the coil was not switched to the resonance frequency of the respective nucleus being imaged, reductions in the transmit efficiency were observed of 32% at the 19F frequency and 12% at the 1H frequency. The coil provides transmit field homogeneity of ±12.9% at the 1H frequency and ±14.4% at the 19F frequency in phantoms representing the thorax with the air space of the lungs filled with perfluoropropane gas. Conclusion MEMS and PIN diodes were found to provide comparable performance in on‐state configuration, while MEMS were more robust in off‐state high‐powered operation (>1 kW), providing higher isolation and requiring a lower DC switching voltage than is needed for reverse biasing of PIN diodes. In addition, clear benefits of switching between the 19F and 1H resonances were demonstrated, despite the proximity of their Larmor frequencies.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".