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Magnetic resonance imaging inside cylindrical metal containers with an eddy current self-compensated method

2011· article· en· W2054008828 on OpenAlexafffund
Hui Han, Bruce J. Balcom

Bibliographic record

VenueMeasurement Science and Technology · 2011
Typearticle
Languageen
FieldMedicine
TopicAdvanced MRI Techniques and Applications
Canadian institutionsUniversity of New Brunswick
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsEddy currentCurrent (fluid)Materials scienceResonance (particle physics)Nuclear magnetic resonanceMagnetic resonance imagingAcousticsEddy-current testingPhysicsElectrical engineeringAtomic physicsMedicineEngineeringRadiology

Abstract

fetched live from OpenAlex

Magnetic resonance imaging (MRI) measurements inside cylindrical metal structures have recently been proposed and form the basis for new high-pressure MRI studies. The critical problem for MRI inside cylindrical metal structures is significant eddy currents induced by the switched magnetic field gradients, which usually corrupt spatial and motion encoding without appropriate correction. In this work a so-called standard SPRITE (single point ramped imaging with T1 enhancement) technique is applied for imaging inside cylindrical metal structures. We show that the standard SPRITE technique is fundamentally immune to large-scale eddy current effects and yields artifact-free high-quality images with no eddy current correction required. Standard SPRITE image acquisition avoids the complications involved in the measurement and compensation of eddy current effects for MRI with cylindrical metal structures. This work is a substantial advance toward the extension of MRI to new challenging systems, which are of practical importance.

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)0.001
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.773
Threshold uncertainty score0.444

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
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 head0.051
GPT teacher head0.324
Teacher spread0.273 · 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 designBench or experimental
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

Citations7
Published2011
Admission routes2
Has abstractyes

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