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Record W4399055688 · doi:10.1136/heartjnl-2024-bcs.205

211 High resolution non-contrast magnetic resonance coronary angiography for coronary artery disease assessment

2024· article· en· W4399055688 on OpenAlexaboutno aff
Simon Littlewood, Grégory Wood, Karl Kunze, Michael Crabb, Won Yong Kim, Claudia Prieto, René M. Botnar

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMedicine
TopicAdvanced MRI Techniques and Applications
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineCoronary artery diseaseMagnetic resonance imagingRadiologyCoronary arteriesIodinated contrastRight coronary arterySteady-state free precession imagingCoronary angiographyCardiologyArteryInternal medicineComputed tomography

Abstract

fetched live from OpenAlex

Introduction Coronary artery disease (CAD) is the leading cause of morbidity and mortality in the developed world. Computed tomography coronary angiography (CTCA), with a spatial resolution of approximately 0.6 mm, is the gold standard in both American and European guidelines for non-invasive anatomical assessment low and intermediate risk patients with suspected coronary artery disease. However, CTCA involves use of ionising radiation and iodinated contrast agents. Coronary magnetic resonance angiography (CMRA) offers a promising radiation- and contrast-free alternative. However, clinical adoption has been hampered by challenges such as low spatial resolution, prolonged and unpredictable scan durations, and susceptibility to motion artefacts. Recent advances utilising image navigators (iNAV) have allowed for 100% respiratory scan efficiency and predicable scan times. In this work we sought to perform high resolution (0.7 mm3) CMRA acquisitions on patients presenting with suspected CAD. Methods Individuals referred for a CTCA to investigate suspected CAD were enrolled in the study. An ECG-triggered, free-breathing, 3D whole-heart, balanced steady-state free precession (bSSFP) research sequence with an under sampled 3D variable density spiral-like Cartesian trajectory with golden-angle rotation was utilised. Low-resolution 2D image navigators (iNAV) were incorporated into the sequence to enable 100% respiratory scan efficiency with predictable scan times. Patient preparation involved sublingual nitrates for coronary dilatation +/- intravenous beta-blockers to achieve a target resting heart rate of 50–60 beats-per-minute (bpm). The acquisition window was defined by the diastolic rest period of the right coronary artery (RCA). Imaging was performed using a 1.5T MRI scanner (MAGNETOM Sola, Siemens Healthineers, Erlangen, Germany). Multiplanar reconstruction and image analysis was performed using cvi42 software (Circle Cardiovascular Imaging Inc., Calgary, Alberta, Canada). CMRA was compared with CTCA for presence of atherosclerotic plaque disease. Results Twenty-two patients were successfully scanned utilising the described CMRA technique. The average heart rate was 55 bpm, and all were in sinus rhythm. The average scan time for the 0.7 mm3 CMRA acquisition was 13 minutes 42 seconds. All acquisitions provided clear visualisation of the proximal and mid portions of all three coronary arteries. An example is shown in figure 1. When compared with the corresponding CTCA there was good agreement for presence of plaque stenosis (figure 2). Discussion This study demonstrates feasibility of free-breathing whole heart CMRA at 0.7 mm3 isotropic spatial resolution with a predictable scan time. The high-resolution acquisition showed excellent stenosis detection when compared with CTCA. This study lends support to CMRA as a radiation and contrast agent-free alternative to CTCA for imaging of coronary anatomy which has the potential to enter clinical practice. Further work is required to assess the negative predictive value of CMRA vs CTCA on a larger cohort of patients. Conflict of Interest Nil

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)0.000
metaresearch head score (Gemma)0.000
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 score0.009
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0090.003

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 head0.012
GPT teacher head0.305
Teacher spread0.292 · 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
Published2024
Admission routes1
Has abstractyes

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