MétaCan
Menu
Back to cohort
Record W2007515996 · doi:10.1097/rli.0b013e31827752b4

Dynamic and Static Magnetic Resonance Angiography of the Supra-aortic Vessels at 3.0 T

2012· article· en· W2007515996 on OpenAlex
J. Harald Kramer, Elisabeth Arnoldi, Christopher J. François, Andrew L. Wentland, Konstantin Nikolaou, Bernd J. Wintersperger, Thomas M. Grist

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueInvestigative Radiology · 2012
Typearticle
Languageen
FieldMedicine
TopicAdvanced MRI Techniques and Applications
Canadian institutionsUniversity of Toronto
FundersNational Institute of General Medical SciencesNational Heart, Lung, and Blood Institute
KeywordsGadobutrolMagnetic resonance angiographyNuclear medicineMedicineMagnetic resonance imagingGadoliniumRadiologyMaterials science

Abstract

fetched live from OpenAlex

PURPOSE: The purpose of this study was the intraindividual comparison of a 1.0 M and two 0.5 M gadolinium-based contrast agents (GBCA) using equimolar dosing in dynamic and static magnetic resonance angiography (MRA) of the supra-aortic vessels. MATERIALS AND METHODS: In this institutional review board-approved study, a total of 20 healthy volunteers (mean ± SD age, 29 ± 6 years) underwent 3 consecutive supra-aortic MRA examinations on a 3.0 T magnetic resonance system. The order of GBCA (Gadobutrol, Gadobenate dimeglumine, and Gadoterate meglumine) was randomized with a minimum interval of 48 hours between the examinations. Before each examination and 45 minutes after each examination, circulatory parameters were recorded. Total GBCA dose per MRA examination was 0.1 mmol/kg with a 0.03 mmol/kg and 0.07 mmol/kg split for dynamic and static MRA, respectively, injected at a rate of 2 mL/s. Two blinded readers qualitatively assessed static MRA data sets independently using pairwise rankings (superior, inferior, and equal). In addition, quantitative analysis was performed with signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) evaluation as well as vessel sharpness analysis of static MRA using an in-house-developed semiautomated tool. Dynamic MRA was evaluated for maximal SNR. Statistical analysis was performed using the Cohen κ, the Wilcoxon rank sum tests, and mixed effects models. RESULTS: No significant differences of hemodynamic parameters were observed. In static MRA, Gadobutrol was rated superior to Gadoterate meglumine (P < 0.05) and equal to Gadobenate dimeglumine (P = 0.06) with good to excellent reader agreement (κ, 0.66-0.83). In static MRA, SNR was significantly higher using 1.0 M Gadobutrol as compared with either 0.5 M agent (P < 0.05 and P < 0.05) and CNR was significantly higher as compared with Gadoterate meglumine (P < 0.05), whereas CNR values of Gadobutrol data sets were not significantly different as compared with Gadobenate dimeglumine (P = 0.13). Differences in CNR between Gadobenate dimeglumine and Gadoterate meglumine were not significant (P = 0.78). Differences in vessel sharpness between the different GBCAs were also not significant (P > 0.05). Maximal SNR in dynamic MRA using Gadobutrol was significantly higher than both comparators at the level of the proximal and distal internal carotid artery (P < 0.05 and P < 0.05; P < 0.05 and P < 0.05). CONCLUSIONS: At equimolar doses, 1.0 M Gadobutrol demonstrates higher SNR/CNR than do Gadobenate dimeglumine and Gadoterate meglumine, with superior image quality as compared with Gadoterate meglumine for dynamic and static carotid MRA. Despite the shortened bolus with Gadobutrol, no blurring of vessel edges was observed.

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.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.278
Threshold uncertainty score0.560

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.002
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 head0.017
GPT teacher head0.289
Teacher spread0.272 · 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