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
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".