T2 mapping for the detection of myocardial edema in patients with acute myocarditis
Bibliographic record
Abstract
In patients with acute myocarditis, T2-weighted cardiovascular magnetic resonance (CMR) can visualize myocardial edema and is used for one of the three recommended diagnostic CMR criteria. There are, however, significant technical problems associated with the short-tau-inversion-recovery (STIR) sequence, limiting its clinical utility. Quantitative T2 mapping technique may overcome such technical limitations and thus improve the diagnostic yield of CMR. Its clinical utility, however, has not been assessed. The purpose of this study is to assess the ability of T2 mapping to detect myocardial edema in patients with acute myocarditis. Ten healthy volunteers and 17 patients with acute myocarditis as diagnosed using clinical and CMR criteria (Lake Louise criteria) and evidence for myocardial edema were studied. CMR studies included STIR images, early and late gadolinium enhanced images and T2-mapping images using a T2-prepared single-shot SSFP acquisition with three T2-prep echo times: 0, 24, and 55 msec. Three short axis T2 mapping images were obtained. Global myocardial T2 values were evaluated on each slice and the mean value of 3 slices were calculated for each subject. Images were also analyzed based on a 16-segment model. T2 values were evaluated in regions of interest in each segment. T2 maps were also visually assessed to determine the location of visually abnormal and remote segments. On the slices where no visually abnormal segments were found, all segments were considered remote. The global T2 value in myocarditis patients were significantly higher than those in volunteers (58.5 ± 3.8 vs. 52.4 ± 2.6, p<0.000). Of 402 available segments, 7 segments had to be excluded due to poor image quality. A total of 129 segments in volunteers and 266 in patients were analyzed. Ninety-three segments in patients were visually identified as abnormal on T2 maps. T2 values of visually abnormal segments were significantly higher than segments in volunteers (62.4 ± 5.6 vs. 51.0 ± 4.5, p<0.000) and remote segments (62.4 ± 5.6 vs. 53.5 ± 4.6, p <0.000). In patients, T2 values of remote segments were also significantly higher than those in volunteers (53.5 ± 4.6 vs 51.0 ± 4.5, p <0.000). For detecting myocardial edema in patients with acute myocarditis, T2 mapping may be an alternative to T2-weighted STIR 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 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".