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Record W1979098869 · doi:10.1002/jmri.20872

MR compatibility of EEG scalp electrodes at 4 tesla

2007· article· en· W1979098869 on OpenAlexafffund
Todd K. Stevens, John R. Ives, L. Martyn Klassen, Robert Bartha

Bibliographic record

VenueJournal of Magnetic Resonance Imaging · 2007
Typearticle
Languageen
FieldMedicine
TopicAdvanced MRI Techniques and Applications
Canadian institutionsRobarts Clinical TrialsWestern University
FundersRobarts Research InstituteNatural Sciences and Engineering Research Council of Canada
KeywordsElectrodeMaterials scienceElectromagnetic shieldingShim (computing)Imaging phantomBiomedical engineeringNuclear magnetic resonanceOpticsPhysicsComposite materialMedicine

Abstract

fetched live from OpenAlex

PURPOSE: To design and apply a method to quantitatively evaluate the MR compatibility of electroencephalographic (EEG) scalp electrodes based on pulse sequence-independent metrics. MATERIALS AND METHODS: Three types of electrodes (constructed primarily of brass, silver, and conductive plastic, respectively) were tested. B0 field distortions, B1 shielding, and heat induction was measured in adjacent agarose and oil phantoms at 4 T. B0 field maps were corrected for distortions caused by the measurement apparatus and passive shim heating, and projections perpendicular to the surfaces of the electrodes were fit, generating cubic coefficients representing the electrode distortion severity. Signal loss in T2-weighted images was used to determine B1 shielding by the electrodes. Temperature measurements were recorded during the application of a high-power pulse sequence. RESULTS: Significantly different B0 distortions were observed in the three types of electrodes. The B1 shielding detected in all three electrodes is minimal for most human MRI, and no significant heating was detected in the electrodes or adjacent phantom. CONCLUSION: The three types of electrodes were successfully differentiated in terms of MR compatibility based on pulse sequence-independent B0 field distortions.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.614
Threshold uncertainty score0.342

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.011
GPT teacher head0.317
Teacher spread0.306 · 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 designObservational
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

Citations26
Published2007
Admission routes2
Has abstractyes

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