A fast MOSFET rf switch for low-field NMR and MRI
Bibliographic record
Abstract
TRansmit Array Spatial Encoding (TRASE) MRI uses trains of rf pulses alternately produced by distinct transmit coils. Commonly used coil switching involving PIN diodes is too slow for low- and ultra-low-field MRI and would introduce wait times between pulses typically as long as each individual pulse in a few mT. A MOSFET-based rf switch is described and characterised. Up to hundreds of kHz, it allows for sub-μs switching of rf currents from a single amplifier to several coils with sufficient isolation ratio and negligible delay between pulses. Additionally, current switching at null current and maximum voltage can be used to abruptly stop or start pulses in series-tuned rf coils, therefore avoiding the rise and fall times associated with the Q-factors. RF energy can be efficiently stored in tuning capacitors for times as long as several seconds. Besides TRASE MRI, this energy storage approach may find applications in fast repeated spin-echo experiments. Here, a threefold acceleration of TRASE phase-encoding is demonstrated when MOSFET switches are used instead of fast reed relays.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".