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Record W1912286526 · doi:10.1002/mrm.24688

3D myocardial <i>T</i><sub>1</sub> mapping at 3T using variable flip angle method: Pilot study

2013· article· en· W1912286526 on OpenAlexaff
Hélène Clique, Hai‐Ling Margaret Cheng, Pierre‐Yves Marie, Jacques Felblinger, Marine Beaumont

Bibliographic record

VenueMagnetic Resonance in Medicine · 2013
Typearticle
Languageen
FieldMedicine
TopicAdvanced MRI Techniques and Applications
Canadian institutionsHospital for Sick ChildrenUniversity of Toronto
Fundersnot available
KeywordsFlip angleReproducibilitySpin echoMagnetic resonance imagingNuclear magnetic resonanceEcho-planar imagingNuclear medicineFast spin echoPlanarPhysicsMaterials scienceBiomedical engineeringMedicineMathematicsComputer scienceRadiologyStatistics

Abstract

fetched live from OpenAlex

PURPOSE: Myocardial T1 mapping is an emerging technique that could improve cardiovascular magnetic resonance diagnostic accuracy. In this study, a variable flip angle approach with B1 correction is proposed at 3T on the myocardium, employing standard 3D spoiled fast gradient echo and echo planar imaging sequences. METHODS: The method was tested on phantoms to determine the set of standard 3D spoiled fast gradient echo angles adapted to myocardial T1 measurements and was compared to the inversion-recovery spin-echo reference T1 method. Seven volunteers underwent magnetic imaging resonance to acquire myocardial T1 maps and T1 values of the human heart. RESULTS: This original method demonstrated good reproducibility in phantoms and a significant correlation between variable flip angle T1 values and reference inversion-recovery spin-echo T1 values. It yielded myocardial T1 values consistent with expected T1 and an increasing homogenization of myocardial segments owing to B1 correction. The mean myocardial T1 value was 1341 ± 42 ms. CONCLUSION: Myocardial 3D T1 mapping using the variable flip angle approach can potentially be useful for evaluating fibrosis on the entire myocardium using a standard clinical sequence.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.735
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.045
GPT teacher head0.328
Teacher spread0.282 · 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 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

Citations31
Published2013
Admission routes1
Has abstractyes

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