Magnetic resonance myocardial perfusion imaging at 3.0 Tesla for the identification of myocardial ischaemia: comparison with coronary catheter angiography and fractional flow reserve measurements
Why this work is in the frame
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Bibliographic record
Abstract
AIMS: To assess image quality and diagnostic performance of 3.0 Tesla (3T) cardiac magnetic resonance (CMR) myocardial perfusion imaging with a dual radiofrequency source to detect functional relevant coronary artery disease (CAD), using coronary angiography and invasive pressure-derived fractional flow reserve (FFR) as reference standard. METHODS AND RESULTS: We included 116 patients with suspected or known CAD, who underwent 3T adenosine myocardial perfusion CMR (resolution 2.97 × 2.97 mm) and coronary angiography plus FFR measurements in intermediate lesions. Image quality of myocardial perfusion CMR was graded on a 4-point scale (1 = poor to 4 = excellent). Diagnostic accuracy was assessed by ROC analyses using a 16-myocardial segment-based summed perfusion score (0 = normal to 3 = transmural perfusion defect) and by determining sensitivity, specificity, positive and negative predictive value on the coronary vessel territory and the patient level. Diagnostic image quality was achieved for all stress myocardial perfusion CMR studies with an average quality score of 2.5, 3.1, and 3.0 for LAD, LCX, and RCA territories. The ability of the myocardial perfusion CMR perfusion score to detect significant coronary artery stenosis yielded an area under the curve of 0.93 on ROC analysis. Values for sensitivity, specificity, positive and negative predictive value on a vessel territory level and the patient level were 89, 95, 87, 96% and 85, 87, 77, 92%, respectively. CONCLUSION: In patients with suspected or known significant CAD, 3T myocardial perfusion CMR with standard perfusion protocols provides consistently high image quality and an excellent diagnostic performance.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it