T1 Mapping for the Diagnosis of Acute Myocarditis Using CMR: Comparison to T2-Weighted and Late Gadolinium Enhanced Imaging
Bibliographic record
Abstract
OBJECTIVES: This study sought to test the diagnostic performance of native T1 mapping in acute myocarditis compared with cardiac magnetic resonance (CMR) techniques such as dark-blood T2-weighted (T2W)-CMR, bright-blood T2W-CMR, and late gadolinium enhancement (LGE) imaging. BACKGROUND: The diagnosis of acute myocarditis on CMR often requires multiple techniques, including T2W, early gadolinium enhancement, and LGE imaging. Novel techniques such as T1 mapping and bright-blood T2W-CMR are also sensitive to changes in free water content. We hypothesized that these techniques can serve as new and potentially superior diagnostic criteria for myocarditis. METHODS: We investigated 50 patients with suspected acute myocarditis (age 42 ± 16 years; 22% women) and 45 controls (age 42 ± 14 years; 22% women). CMR at 1.5-T (median 3 days from presentation) included: 1) dark-blood T2W-CMR (short-tau inversion recovery); 2) bright-blood T2W-CMR (acquisition for cardiac unified T2 edema); 3) native T1 mapping (shortened modified look-locker inversion recovery); and 4) LGE. Image analysis included: 1) global T2 signal intensity ratio of myocardium compared with skeletal muscle; 2) myocardial T1 relaxation times; and 3) areas of LGE. RESULTS: Compared with controls, patients had significantly higher global T2 signal intensity ratios by dark-blood T2W-CMR (1.73 ± 0.27 vs. 1.56 ± 0.15, p < 0.01), bright-blood T2W-CMR (2.02 ± 0.33 vs. 1.84 ± 0.17, p < 0.01), and mean myocardial T1 (1,010 ± 65 ms vs. 941 ± 18 ms, p < 0.01). Receiver-operating characteristic analysis showed clear differences in diagnostic performance. The areas under the curve for each method were: T1 mapping (0.95), LGE (0.96), dark-blood T2 (0.78), and bright-blood T2 (0.76). A T1 cutoff of 990 ms had a sensitivity, specificity, and diagnostic accuracy of 90%, 91%, and 91%, respectively. CONCLUSIONS: Native T1 mapping as a novel criterion for the detection of acute myocarditis showed excellent and superior diagnostic performance compared with T2W-CMR. It also has a higher sensitivity compared with T2W and LGE techniques, which may be especially useful in detecting subtle focal disease and when gadolinium contrast imaging is not feasible.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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".