MétaCan
Menu
Back to cohort
Record W2136033789 · doi:10.1109/tmi.2010.2078513

Radio-Frequency Current Density Imaging Based on a 180$^\circ$ Sample Rotation With Feasibility Study of Full Current Density Vector Reconstruction

2010· article· en· W2136033789 on OpenAlexaff
Dinghui Wang, Weijing Ma, T.P. DeMonte, Adrian Nachman, M.L.G. Joy

Bibliographic record

VenueIEEE Transactions on Medical Imaging · 2010
Typearticle
Languageen
FieldEngineering
TopicElectrical and Bioimpedance Tomography
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsCurrent (fluid)Current densitySample (material)Rotation (mathematics)Iterative reconstructionRadio frequencyNuclear magnetic resonancePhysicsComputer scienceArtificial intelligenceTelecommunications

Abstract

fetched live from OpenAlex

Radio-frequency current density imaging (RF-CDI) is a technique that noninvasively measures current density distributions at the Larmor frequency utilizing magnetic resonance imaging. Previously implemented RF-CDI methods reconstruct the applied current density component J(z) along the static magnetic field of the imager [(B)\vec](0) (the z direction) based on the assumption that the z-directional change of the magnetic field component H(z) can be ignored compared to J(z). However, this condition may be easily violated in biomedical applications. We propose a new reconstruction method for RF-CDI, which does not rely on the aforementioned assumption. Instead, the sample is rotated by 180 (°) in the horizontal plane to collect magnetic resonance data from two opposite positions. Using simulations and experiments, we have verified that this approach can fully recover one component of current density. Furthermore, this approach can be extended to measure three dimensional current density vectors by one additional sample orientation in the horizontal plane. We have therefore demonstrated for the first time the feasibility of imaging the magnitude and phase of all components of a radio-frequency current density vector field.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.872
Threshold uncertainty score1.000

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 integrity0.0000.001
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 head0.010
GPT teacher head0.249
Teacher spread0.238 · 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.

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

Citations4
Published2010
Admission routes1
Has abstractyes

Explore more

Same venueIEEE Transactions on Medical ImagingSame topicElectrical and Bioimpedance TomographyFrench-language works237,207