Multicontrast late gadolinium enhancement imaging enables viability and wall motion assessment in a single acquisition with reduced scan times
Bibliographic record
Abstract
PURPOSE: To determine the accuracy of multicontrast late enhancement imaging (MCLE) in the assessment of myocardial viability and wall motion compared to the conventional wall motion and viability cardiac magnetic resonance imaging (MRI) pulse sequences. MATERIALS AND METHODS: Forty-one patients with suspected myocardial infarction were studied. Patients underwent assessment of cardiac function with cine steady-state free-precession (SSFP), followed by late gadolinium enhancement (LGE) imaging using inversion recovery gradient echo scanning (IR-GRE) sequence and MCLE. MCLE was compared to cine SSFP in the assessment of wall motion, ejection fraction (EF), left ventricular (LV) mass, LV end-diastolic volume (EDV), and to IR-GRE for measuring infarct size. RESULTS: MCLE, IR-GRE, and SSFP imaging demonstrated excellent agreement in the assessment of EF, LV infarct size, and LV mass (r > 0.95, P < 0.001 for all measures), as well as in the assessment of wall motion (kappa statistic 0.75). CONCLUSION: MCLE provided coregistered images for the assessment of viability and wall motion without loss of accuracy in the assessment of quantitative cardiac parameters. MCLE provides accurate quantitative cardiac assessment with reduced scan times compared to the conventional sequences and thus may be used as an alternative to conventional cine SSFP and IR-GRE imaging.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".