MétaCan
Menu
Back to cohort
Record W2542653948 · doi:10.1109/iembs.2004.1404484

MR current density and conductivity imaging: the state of the Aart

2005· article· en· W2542653948 on OpenAlexaffabout
M.L.G. Joy

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicElectrical and Bioimpedance Tomography
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsCurrent (fluid)Magnetic resonance imagingCurrent densityData acquisitionSoftwareComputer sciencePhase (matter)Pulse (music)Nuclear magnetic resonancePhysicsElectrical engineeringRadiologyEngineeringMedicineTelecommunications

Abstract

fetched live from OpenAlex

Current density imaging (CDI) is an imaging technique that measures electrical current density distributions in a volume of material or tissue, which can be imaged using magnetic resonance imaging (MRI). Measurements of current density are obtained by applying an external current to the material/tissue during an MRI acquisition. The magnetic fields produced by the applied current are mapped onto the phase image of the MRI acquisition. The phase images are processed to compute the current density distribution. Performing CDI requires an MRI system, additional hardware, a modified pulse sequence (PSD) and data processing software. Greig C. Scott, Michael L.G. Joy and R. Mark Henkelman developed CDI in 1988 at the University of Toronto (Canada). The CDI Research Group is presently based at the University of Toronto and is supervised by the author. This paper describes the CDI technique, its applications by this and other groups and recently proposed methods for electrical conductivity imaging based on the technique.

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 score0.820
Threshold uncertainty score0.101

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 head0.008
GPT teacher head0.205
Teacher spread0.198 · 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

Citations39
Published2005
Admission routes2
Has abstractyes

Explore more

Same topicElectrical and Bioimpedance TomographyFrench-language works237,207