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Record W3081396931 · doi:10.1002/jmri.27334

Fetal Flow Quantification in Great Vessels Using Motion‐Corrected Radial Phase Contrast <scp>MRI</scp>: Comparison With Cartesian

2020· article· en· W3081396931 on OpenAlexaff
Datta Singh Goolaub, Jiawei Xu, Eric Schrauben, Liqun Sun, Christopher Roy, Davide Marini, Mike Seed, Christopher K. Macgowan

Bibliographic record

VenueJournal of Magnetic Resonance Imaging · 2020
Typearticle
Languageen
FieldMedicine
TopicFetal and Pediatric Neurological Disorders
Canadian institutionsHospital for Sick ChildrenUniversity of Toronto
Fundersnot available
KeywordsCartesian coordinate systemImaging phantomContrast (vision)Nuclear medicineMagnetic resonance imagingPhysicsMedicineMathematicsRadiologyArtificial intelligenceComputer scienceGeometry

Abstract

fetched live from OpenAlex

Background Phase contrast MRI in the great vessels is a potential clinical tool for managing fetal pathologies. One challenge is the uncontrollable fetal motion, potentially corrupting flow quantifications. Purpose To demonstrate improvements in fetal blood flow quantification in great vessels using retrospectively motion‐corrected golden‐angle radial phase contrast MRI relative to Cartesian phase contrast MRI. Study Type Method comparison. Phantom/Subjects Computer simulation. Seventeen pregnant volunteers. Field Strength/Sequence 1.5T and 3T . Cartesian and golden‐angle radial phase contrast MRI . Assessment Through computer simulations, radial (with and without retrospective motion correction) and Cartesian phase contrast MRI were compared using flow deviations. in vivo Cartesian and radial phase contrast MRI measurements and reconstruction qualities were compared in pregnancies. Cartesian data were reconstructed into gated reconstructions (CINEs) after cardiac gating with metric optimized gating (MOG). For radial data, real‐time reconstructions were performed for motion correction and MOG followed by CINE reconstructions. Statistical Tests Wilcoxon signed‐rank test. Linear regression. Bland–Altman plots. Student's t ‐test. Results Simulations showed significant improvements ( P &lt; 0.05) in flow accuracy and reconstruction quality with motion correction ([mean/peak] flow errors with ±5 mm motion corruption: Cartesian [35 ± 1/115 ± 7] mL/s, motion uncorrected radial [25 ± 1/75 ± 2] mL/s and motion‐corrected radial [1.0 ± 0.5/−5 ± 1] mL/s). in vivo Cartesian reconstructions without motion correction had lower quality than the motion‐corrected radial reconstructions ( P &lt; 0.05). Across all fetal mean flow measurements, the bias [limits of agreement] between the two measurements were −0.2 [−76, 75] mL/min/kg, while the linear regression coefficients were (M radial = 0.81 × M Cartesian + 29.8 [mL/min/kg], r 2 = 0.67). The corresponding measures for the peak fetal flows were −23 [−214, 167] mL/min/kg and ( P radial = 0.95 × P Cartesian –1.2 [mL/min/kg], r 2 = 0.80). Cartesian reconstructions of low quality showed significantly higher estimated mean and peak ( P &lt; 0.05) flows than the corresponding radial reconstructions. Data Conclusion Simulations showed that radial phase contrast MRI with motion compensation improved flow accuracy. For fetal measurements, motion‐corrected radial reconstructions showed better image quality than, and different flow values from, Cartesian reconstructions. Level of Evidence 1. Technical Efficacy Stage 1. J. MAGN. RESON. IMAGING 2021;53:540–551.

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: Empirical
Teacher disagreement score0.585
Threshold uncertainty score0.622

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 integrity0.0000.001
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 head0.026
GPT teacher head0.276
Teacher spread0.250 · 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".

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Citations15
Published2020
Admission routes1
Has abstractyes

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