Non-contrast T1-mapping detects acute myocardial edema with high diagnostic accuracy: a comparison to T2-weighted cardiovascular magnetic resonance
Bibliographic record
Abstract
BACKGROUND: T2w-CMR is used widely to assess myocardial edema. Quantitative T1-mapping is also sensitive to changes in free water content. We hypothesized that T1-mapping would have a higher diagnostic performance in detecting acute edema than dark-blood and bright-blood T2w-CMR. METHODS: We investigated 21 controls (55 ± 13 years) and 21 patients (61 ± 10 years) with Takotsubo cardiomyopathy or acute regional myocardial edema without infarction. CMR performed within 7 days included cine, T1-mapping using ShMOLLI, dark-blood T2-STIR, bright-blood ACUT2E and LGE imaging. We analyzed wall motion, myocardial T1 values and T2 signal intensity (SI) ratio relative to both skeletal muscle and remote myocardium. RESULTS: All patients had acute cardiac symptoms, increased Troponin I (0.15-36.80 ug/L) and acute wall motion abnormalities but no LGE. T1 was increased in patient segments with abnormal and normal wall motion compared to controls (1113 ± 94 ms, 1029 ± 59 ms and 944 ± 17 ms, respectively; p < 0.001). T2 SI ratio using STIR and ACUT2E was also increased in patient segments with abnormal and normal wall motion compared to controls (all p < 0.02). Receiver operator characteristics analysis showed that T1-mapping had a significantly larger area-under-the-curve (AUC = 0.94) compared to T2-weighted methods, whether the reference ROI was skeletal muscle or remote myocardium (AUC = 0.58-0.89; p < 0.03). A T1 value of greater than 990 ms most optimally differentiated segments affected by edema from normal segments at 1.5 T, with a sensitivity and specificity of 92 %. CONCLUSIONS: Non-contrast T1-mapping using ShMOLLI is a novel method for objectively detecting myocardial edema with a high diagnostic performance. T1-mapping may serve as a complementary technique to T2-weighted imaging for assessing myocardial edema in ischemic and non-ischemic heart disease, such as quantifying area-at-risk and diagnosing myocarditis.
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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 | 0.002 |
| 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".