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Record W1530132479 · doi:10.1109/iembs.2006.259255

Noise Analysis for Multi-slice Radio Frequency Current Density Imaging

2006· article· en· W1530132479 on OpenAlexaff
Dinghui Wang, T.P. DeMonte, M.L.G. Joy, Adrian Nachman

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicElectrical and Bioimpedance Tomography
Canadian institutionsFields Institute for Research in Mathematical SciencesUniversity of Toronto
Fundersnot available
KeywordsRadio frequencyImaging phantomFinite-difference time-domain methodNoise (video)Sensitivity (control systems)Larmor precessionMagnetic resonance imagingFrequency domainPhysicsNuclear magnetic resonanceAcousticsComputer scienceElectronic engineeringOpticsMagnetic fieldTelecommunicationsImage (mathematics)Artificial intelligenceEngineering

Abstract

fetched live from OpenAlex

Radio frequency current density imaging (RF-CDI) is an imaging technique that measures current density distribution at the Larmor frequency utilizing magnetic resonance imaging (MRI). The multi-slice RF-CDI sequence has extended the ability of RF-CDI to image multiple slices and thus has enhanced its capacity for biomedical applications. In this paper, the influence of MRI random noise on the sensitivity of multi-slice RF-CDI measurement is studied. The formula of current noise is derived, which is verified by both simulation and phantom experiments. A 3-D finite-difference time-domain (FDTD) model is employed to compute the electromagnetic fields in the simulation.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.722
Threshold uncertainty score0.438

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 head0.012
GPT teacher head0.236
Teacher spread0.224 · 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

Citations3
Published2006
Admission routes1
Has abstractyes

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