Papillary muscle involvement in myocardial infarction: Initial results using multicontrast late‐enhancement MRI
Bibliographic record
Abstract
We hypothesized that multicontrast late-enhancement (MCLE) MRI would improve the identification of papillary muscle involvement (PM-MI) in patients with myocardial infarction (MI), compared with conventional late gadolinium enhancement (LGE) MRI using the inversion recovery fast gradient echo (IR-FGRE) technique. Cardiac LGE-MRI studies using both MCLE and IR-FGRE pulse sequences were performed on a 1.5 Tesla (T) MRI system in 23 patients following MI. In all patients, PM-MI was confirmed by the diagnostic criteria as outlined below: (a) the increased signal intensity of PM was the same or similar to that of adjacent hyper-enhanced left ventricular (LV) infarct segments; and (b) the hyper-enhanced PM region was limited to the PM area defined by precontrast cine images of steady-state free precession (SSFP). Visual contrast score was rated according to the differentiation between LV blood pool and hyper-enhanced infarct myocardium. Quantitative contrast-noise ratios (CNR) of infarct relative to blood pool and viable myocardium were also measured on MCLE and IR-FGRE images. Of these 23 patients, 13 studies demonstrated primarily involvement of the territories of the right coronary (RCA, 8 patients) and/or left circumflex (LCX, 5 patients) arteries and 10 involved the territories of left anterior descending artery (LAD) with some LCX involvement. Although both IR-FGRE and MCLE determined the presence and extent of LV MI, better visual contrast scores were achieved in MCLE (2.9 ± 0.3) compared with IR-FGRE (1.6 ± 0.8, P < 0.001). The CNRs of infarct relative to LV blood pool showed a significant statistical difference (n = 23, P < 0.00001) between MCLE (16.2 ± 7.2) and IR-FGRE images (4.8 ± 4.1), which is consistent with the result of visual contrast scores between infarct and LV blood pool. The CNRs of infarct versus viable myocardium did not demonstrate a significant statistical difference (n = 23, P = 0.61) between MCLE (14.4 ± 7.0) and IR-FGRE images (13.6 ± 6.1). MCLE clearly demonstrated PM-MI in all cases (100%, 23/23) while only 39% (9/23) could be visualized on the corresponding IR-FGRE images. In conclusion, MCLE imaging provides better contrast between blood pool and infarct myocardium, thus improving the determination of PM-MI.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 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".