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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 OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

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.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.409
Threshold uncertainty score0.721

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 head0.012
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