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Record W2155920620 · doi:10.1002/mrm.20159

Rapid <i>T</i><sub>2</sub> estimation with phase‐cycled variable nutation steady‐state free precession

2004· article· en· W2155920620 on OpenAlexafffund
Sean Deoni, Heidi A. Ward, Terry M. Peters, Brian K. Rutt

Bibliographic record

VenueMagnetic Resonance in Medicine · 2004
Typearticle
Languageen
FieldMedicine
TopicAdvanced MRI Techniques and Applications
Canadian institutionsRobarts Clinical TrialsWestern University
FundersHeart and Stroke Foundation of Canada
KeywordsSteady-state free precession imagingNutationArtifact (error)Nuclear magnetic resonancePrecessionPhase (matter)IsotropyRelaxation (psychology)PhysicsImage resolutionResolution (logic)Steady state (chemistry)Magnetic resonance imagingChemistryOpticsComputer scienceArtificial intelligenceCondensed matter physics

Abstract

fetched live from OpenAlex

Variable nutation SSFP (DESPOT2) permits rapid, high-resolution determination of the transverse (T2) relaxation constant. A limitation of DESPOT2, however, is the presence of T2 voids due to off-resonance banding artifacts associated with SSFP images. These artifacts typically occur in images acquired with long repetition times (TR) in the presence of B0 inhomogeneities, or near areas of magnetic susceptibility difference, such that the transverse magnetization experiences a net phase shift during the TR interval. This places constraints on the maximum spatial resolution that can be achieved without artifact. Here, a novel implementation of DESPOT2 is presented incorporating RF phase-cycling which acts to shift the spatial location of the bands, allowing reconstruction of a single, reduced artifact-image. The method is demonstrated in vivo with the acquisition of a 0.34 mm3 isotropic resolution T2 map of the brain with high precision and accuracy and significantly reduced artifact.

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: none
Teacher disagreement score0.865
Threshold uncertainty score0.751

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 head0.305
Teacher spread0.292 · 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

Citations49
Published2004
Admission routes2
Has abstractyes

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