MétaCan
Menu
Back to cohort
Record W2023279886 · doi:10.1186/1532-429x-16-s1-p375

Non-contrast enhanced, EKG-triggered, navigator MR angiography of the thoracic aorta and proximal pulmonary arteries: initial evaluation of using an abdominal compression band to reduce acquisition times

2014· article· en· W2023279886 on OpenAlexaff
Emer Sonnex, Indrajeet Das, Richard Coulden

Bibliographic record

VenueJournal of Cardiovascular Magnetic Resonance · 2014
Typearticle
Languageen
FieldMedicine
TopicAdvanced MRI Techniques and Applications
Canadian institutionsUniversity of Alberta HospitalAlberta Hospital Edmonton
Fundersnot available
KeywordsMedicineAngiologyRadiologyImage qualityMagnetic resonance angiographyAbdominal aortaAngiographyMagnetic resonance imagingContrast (vision)Coronary arteriesCompression (physics)AortaCardiologyArteryArtificial intelligenceComputer scienceImage (mathematics)

Abstract

fetched live from OpenAlex

Non-contrast enhanced, EKG-triggered, navigator 3D-SSFP magnetic resonance angiography (NCE-MRA) is a robust technique but suffers from long scan acquisition times. Depending on heart and respiratory rates, acquisition times can still be prohibitively long. We describe the use of an abdominal compression band to reduce respiratory motion and image acquisition time with no sacrifice of image quality. Ethics committee approval and informed consent were obtained. 20 normal volunteers (mean age: 39) underwent two NCE-MRA examinations. Both examinations were performed free-breathing, one with an abdominal compression band (band) and one without (no-band). Both examinations were performed on the same 1.5T Aera (Siemens Medical Solutions) with identical scanning parameters within 10 minutes of each other. Each examination duration was recorded. All angiographic data sets were anonymized. Data sets were reviewed in random order by three cardiothoracic radiologists on a single workstation. Motion artifact and overall quality were assessed semi-quantitatively on a scale of 1 - 4 (excellent/no artifact - 1; good/minor artifact/noise - 2; moderate/some noise/artifact - 3; poor/limited diagnostic quality - 4). NCE-MRA was completed successfully with and without an abdominal compression band in all volunteers (40 data sets). There was no difference in global image quality or motion artifact at the aortic root between 'band' and 'no-band' examinations (mean image quality 1.31 and 1.51; mean aortic root artifact 1.25 and 1.44 respectively). No studies in either group were rated moderate or poor. None of the observers could reliably distinguish between 'band' or 'no-band' examinations. Scan times between the 2 groups were, however, significantly different. Scan times ranged from 4.5 - 9.1 minutes (mean 6.7; SD 1.14) in the 'no-band' group and 3.3 to 8.9 minutes (mean 5.0; SD 1.3) in the 'band' group. Mean reduction in acquisition time between the two groups was 1.7 mins or 25% of the standard technique (p = 0.0025). Only 1 data acquisition time increased with the abdominal band. Using an abdominal compression band in non-contrast enhanced, EKG-triggered, navigator 3D-SSFP MRA significantly reduces image acquisition time (25%) without sacrificing image quality. This technique has only been used in volunteers to date. Further study will be needed to see if this benefit is maintained in patients.

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)0.001
metaresearch head score (Gemma)0.003
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0010.000

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 head0.019
GPT teacher head0.327
Teacher spread0.308 · 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".

Quick stats

Citations0
Published2014
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Cardiovascular Magnetic ResonanceSame topicAdvanced MRI Techniques and ApplicationsFrench-language works237,207