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Record W2035879201 · doi:10.1002/mrm.20177

Efficient high‐frequency body coil for high‐field MRI

2004· article· en· W2035879201 on OpenAlexaff
J. Thomas Vaughan, Gregor Adriany, Carl Snyder, Jinping Tian, Torsten Thiel, Lizann Bolinger, H. Liu, Lance DelaBarre, Kǎmil Uǧurbil

Bibliographic record

VenueMagnetic Resonance in Medicine · 2004
Typearticle
Languageen
FieldMedicine
TopicAdvanced MRI Techniques and Applications
Canadian institutionsNational Research Council CanadaNational Research Council Institute for Biodiagnostics
FundersNational Center for Research ResourcesNational Institutes of HealthNational Cancer InstituteW. M. Keck Foundation
KeywordsElectromagnetic coilExcitationHomogeneousRadiofrequency coilAcousticsField (mathematics)Nuclear magnetic resonanceComputer scienceArtifact (error)Body surfaceMaterials scienceBiomedical engineeringPhysicsElectrical engineeringEngineeringArtificial intelligenceMathematics

Abstract

fetched live from OpenAlex

The use of body coils is favored for homogeneous excitation, and such coils are often paired with surface coils or arrays for sensitive reception in many MRI applications. While the body coil's physical size and resultant electrical length make this circuit difficult to design for any field strength, recent efforts to build efficient body coils for applications at 3T and above have been especially challenging. To meet this challenge, we developed an efficient new transverse electromagnetic (TEM) body coil and demonstrated its use in human studies at field strengths up to 4 T. Head, body, and breast images were acquired within peak power constraints of <8 kW. Bench studies indicate that these body coils are feasible to 8 T. RF shimming was used to remove a high-field-related cardiac imaging artifact in these preliminary studies. P41RR13230

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
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 integrity0.0010.000
Insufficient payload (model declined to judge)0.0030.002

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.

Opus teacher head0.012
GPT teacher head0.310
Teacher spread0.298 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations178
Published2004
Admission routes1
Has abstractyes

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