MétaCan
Menu
Back to cohort
Record W2806093913 · doi:10.1002/cmr.b.21365

Design of a high power <scp>PIN</scp>‐diode controlled switchable <scp>RF</scp> transmit array for <scp>TRASE RF</scp> imaging

2018· article· en· W2806093913 on OpenAlexafffund
Eric T. Der, Vyacheslav Volotovskyy, Hongwei Sun, Bogusław Tomanek, Jonathan C. Sharp

Bibliographic record

VenueConcepts in Magnetic Resonance Part B · 2018
Typearticle
Languageen
FieldMedicine
TopicAdvanced MRI Techniques and Applications
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsElectromagnetic coilPower (physics)DiodeElectronic circuitRadio frequencyComputer scienceElectrical engineeringElectronic engineeringEngineeringPhysics

Abstract

fetched live from OpenAlex

Abstract Some MRI applications require the generation of a series high power RF pulses in which the spatial transmit B1 field pattern over the sample is modified between one pulse and the next. This requirement may be realized by a RF transmit array with the capability to enable and disable individual elements to switch between field patterns with switching times of the order 10 μs. Our application is for a TRASE (“Transmit Array Spatial Encoding”) array for which short high power pulses are necessary to achieve high resolution (mm‐level) spatial encoding. We present designs for coil array, coil switching circuits, and a high power PIN diode driver together capable of robust and rapid switching of short (~120 μs) high power pulses for a 24 cm TRASE phase gradient coil suitable for imaging extremities at 8 MH z. We describe in detail the selection of suitable coil components and switch circuit designs to satisfy a specific requirement for maximum B1 field strength, and provide all circuit designs.

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)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.679
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.018
GPT teacher head0.301
Teacher spread0.283 · 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.

Study designNot applicable
Domainnot available
GenreMethods

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

Citations9
Published2018
Admission routes2
Has abstractyes

Explore more

Same venueConcepts in Magnetic Resonance Part BSame topicAdvanced MRI Techniques and ApplicationsFrench-language works237,207