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Record W2071743451 · doi:10.1186/1532-429x-13-s1-p40

Short axis versus axial Cine SSFP MR imaging for assessment of right and left ventricular function: intrapatient correlation with phase-contrast flow measurements

2011· article· en· W2071743451 on OpenAlexaff
Susan James, Rachel M. Wald, Bernd J. Wintersperger, Laura Jiménez‐Juan, Djeven P. Deva, Andrew Crean, Elsie T. Nguyen, Narinder Paul, Sebastian Ley

Bibliographic record

VenueJournal of Cardiovascular Magnetic Resonance · 2011
Typearticle
Languageen
FieldMedicine
TopicAdvanced MRI Techniques and Applications
Canadian institutionsToronto General Hospital
Fundersnot available
KeywordsAngiologySteady-state free precession imagingMedicineAscending aortaPhase contrast microscopyVentricular functionAortaShort axisNuclear medicineContrast (vision)Blood flowLong axisNuclear magnetic resonanceMagnetic resonance imagingCardiologyRadiologyPhysicsMathematicsGeometry

Abstract

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To compare RV and LV volumetric measurements based on short-axis-oblique (SAO) and axial orientation Cine-SSFP imaging in comparison to respective PC flow measurements in the main-pulmonary-artery (MPA) and ascending-aorta (Aorta). MRI is deemed standard of reference for assessment of ventricular volumes and function. However, it remains unclear if the RV can also be adequately assessed in SAO orientation as used for assessment of LV function or requires dedicated axial-Cine imaging. Additional axial-Cine acquisition adds 10-15 min of scan time and thus has a substantial impact on clinical workflow and patient comfort. Retrospective database analysis for patients undergoing CMR for assessment of possible cardiac shunts with MR exclusion of shunts identified 27 subjects (12male/15female) eligible for further data analysis. Based on echo no valvular disease was evident. Patients underwent CMR on a 1.5T system (Magnetom Avanto, SiemensHealthcare) with retrospective gated Cine-SSFP (25 frames/RR) in axial and SAO slice prescription. Spatial resolution for SAO and axial Cine-SSFP was 1.25x1.25x8mm , no slice gap. Semi-automated analysis of RV and LV volumes were performed on axial and SAO-Cine data by a single observer as well as PC flow analysis of MPA and Aorta-flow as a reference measure of RV and LV output (syngo ARGUS, SiemensHealthcare). RV-axial (81±18ml) and RV-SAO (76±17ml) stroke volumes showed no significant difference (p=0.1, t-test). There was a high linear correlation between MPA-PC-flow measurements and RV-stroke volume determined in SAO (r=0.80) and axial (r=0.85) orientations. Bland-Altman-Analysis revealed an offset of 1.6 ml (limits: 26 to -23ml) for RV-axial versus MPA-PC-flow and -3.9 ml (limits: 23 to -31ml) for RV-SAO versus MPA-PC-flow. Stroke volumes of the LV assessed in SAO (81±18ml) and in axial (82±22ml) orientation showed no significant difference (p=0.6). There was a high linear correlation between the LV-SV in SAO (r=0.89) and axial (r=0.88) orientations compared to Aorta-PC-flow measurements. Bland-Altman-Analysis revealed an offset of 8 ml (limits: 24 to -8ml) for LV-SAO versus Aorta-PC-flow and 9 ml (limits: 30 to -12ml) for LV-axial versus Aorta-PC-flow. There was no significant difference (p=0.9) between the SAO-LV-SV and the axial RV-SV (linear correlation r=0.88). There was no significant difference (p=0.1) between the SAO-LV-SV versus SAO-RV-SV (linear correlation r=0.75). The results of this initial study demonstrate no significant impact of the slice acquisition orientation for determination of right and left ventricular stroke volumes. CMR workflow could therefore be streamlined for most cases only acquiring SAO cine images for ventricular volumetrics.

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)0.002
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.032
GPT teacher head0.286
Teacher spread0.254 · 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 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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Citations0
Published2011
Admission routes1
Has abstractyes

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