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Record W2063121246 · doi:10.1109/tmag.2012.2196051

Simulation and Verification of Magnetic Field Gradient Waveforms in the Presence of a Metallic Vessel in Magnetic Resonance Imaging

2012· article· en· W2063121246 on OpenAlexaff
Frédéric G. Goora, Hui Han, Bruce G. Colpitts, Bruce J. Balcom

Bibliographic record

VenueIEEE Transactions on Magnetics · 2012
Typearticle
Languageen
FieldEngineering
TopicNon-Destructive Testing Techniques
Canadian institutionsUniversity of New Brunswick
Fundersnot available
KeywordsEddy currentMagnetic fieldMaterials scienceNuclear magnetic resonanceMagnetic resonance imagingWaveformField (mathematics)Magnetic pressureMechanicsAcousticsComputational physicsPhysicsMagnetization

Abstract

fetched live from OpenAlex

We present simulation and experimental results regarding the generation of eddy currents due to switched magnetic field gradients during the use of metallic vessels in high-pressure magnetic resonance imaging (MRI) applications. The induced eddy currents result in a corruption of the applied magnetic field gradient as experienced by the object being imaged. Simulations using CST EM Studio™ have revealed the spatial distribution and temporal evolution of eddy currents, the resulting magnetic fields, and their dependence on vessel material and orientation of the applied switched magnetic field gradient. The simulation results have been compared to measured data with excellent agreement. Agreement between simulated and measured data permits increasingly sophisticated models to be developed such that simulation results reliably guide the design of improved metal vessels.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.017
GPT teacher head0.249
Teacher spread0.232 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations9
Published2012
Admission routes1
Has abstractyes

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