Free‐breathing, nongated real‐time delayed enhancement MRI of myocardial infarcts: A comparison with conventional delayed enhancement
Bibliographic record
Abstract
PURPOSE: To compare a free-breathing, nongated, and black-blood real-time delayed enhancement (RT-DE) sequence to the conventional inversion recovery gradient echo (IR-GRE) sequence for delayed enhancement MRI. MATERIALS AND METHODS: Twenty-three patients with suspected myocardial infarct (MI) were examined using both the IR-GRE and RT-DE imaging sequences. The sensitivity and specificity of RT-DE for detecting MI, using IR-GRE as the gold standard, was determined. The contrast-to-noise ratios (CNR) between the two techniques were also compared. RESULTS: RT-DE had a high sensitivity and specificity (94% and 98%, respectively) for identifying MI. The total acquisition time to image the entire left ventricle was significantly shorter using RT-DE than IR-GRE (5.6+/-0.9 versus 11.5+/-1.9 min). RT-DE had a slightly lower infarct-myocardium CNR but a higher infarct-blood CNR than IR-GRE imaging. Compared with IR-GRE, RT-DE accurately measured total infarct sizes. CONCLUSION: RT-DE can be used for delayed enhancement imaging during free-breathing and without cardiac gating.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".