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Record W2120896176 · doi:10.1109/iembs.2002.1106205

Current density imaging of electrical current pathways inside the pig torso

2002· article· en· W2120896176 on OpenAlexaff
Richard S. Yoon, T.P. DeMonte, K.F. Hasanov, Dawn Jorgenson, M.L.G. Joy

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicElectrical and Bioimpedance Tomography
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsTorsoCurrent (fluid)Current densityElectrical impedance tomographyElectrical currentImaging phantomDefibrillationElectric currentNuclear magnetic resonanceMagnetic fieldStreamlines, streaklines, and pathlinesElectrical impedanceBiomedical engineeringAcousticsPhysicsElectrical engineeringMedicineMechanicsEngineeringOpticsAnatomy

Abstract

fetched live from OpenAlex

Low frequency current density imaging (LFCDI) using a magnetic resonance (MR) imager has been shown to accurately measure electrical current density inside a phantom. CDI measures the magnetic field generated by the current and converts it to current density (CD) by computing its curl. Therefore, CDI avoids both the inverse problem and invasiveness of other electrical measurement techniques such as electrical impedance tomography and direct electrode measurement. This makes CDI an ideal technique for studying the current flow inside the body during electrical therapies such as defibrillation where the current density in tissue is closely associated with the efficacy. Here we report simultaneous measurements of current density at all points within the pig torso during an electrical current application through defibrillation electrodes. Current flow was visualized by computing streamlines from the current density vectors. We observed current flow over the chest walls in agreement with the current literature. However, complex and unexpected current flow patterns were seen inside the heart as well as in the surrounding vasculature. This study represents the first noninvasive volume current measurement inside the pig torso during an electrical current application.

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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.953
Threshold uncertainty score0.374

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.016
GPT teacher head0.206
Teacher spread0.190 · 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 designOther design
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

Citations0
Published2002
Admission routes1
Has abstractyes

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