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Record W2973950201 · doi:10.1016/j.jmr.2019.106638

A fast MOSFET rf switch for low-field NMR and MRI

2019· article· en· W2973950201 on OpenAlexaff
Pierre-Jean Nacher, Sashika Kumaragamage, Geneviève Tastevin, Christopher P. Bidinosti

Bibliographic record

VenueJournal of Magnetic Resonance · 2019
Typearticle
Languageen
FieldMedicine
TopicAdvanced MRI Techniques and Applications
Canadian institutionsUniversity of WinnipegUniversity of Manitoba
FundersCentre National de la Recherche Scientifique
KeywordsRF switchCapacitorMOSFETAmplifierRadio frequencyDiodeElectromagnetic coilPhysicsVoltageMaterials scienceEnergy (signal processing)Pulse (music)OptoelectronicsElectrical engineeringNuclear magnetic resonanceTransistorCMOSEngineering

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.905
Threshold uncertainty score0.245

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.008
GPT teacher head0.285
Teacher spread0.277 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations14
Published2019
Admission routes1
Has abstractyes

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