Inter-study repeatability of circumferential strain and diastolic strain rate by CMR tagging, feature tracking and tissue tracking in ST-segment elevation myocardial infarction
Bibliographic record
Abstract
Abstract Strain assessment allows accurate evaluation of myocardial function and mechanics in ST-segment elevation myocardial infarction (STEMI). Strain using cardiovascular magnetic resonance (CMR) has traditionally been assessed with tagging but limitations of this technique have led to more widespread use of alternative methods, which may be more robust. We compared the inter-study repeatability of circumferential global peak-systolic strain ( Ecc ) and peak-early diastolic strain rate (PEDSR) derived by tagging with values obtained using novel cine-based software: Feature Tracking (FT) (TomTec, Germany) and Tissue Tracking (TT) (Circle cvi 42 , Canada) in patients following STEMI. Twenty male patients (mean age 56 ± 10 years, mean infarct size 13.7 ± 7.1% of left ventricular mass) were randomised to undergo CMR 1–5 days post-STEMI at 1.5 T or 3.0 T, repeated after ten minutes at the same field strength. Ecc and PEDSR were assessed using tagging, FT and TT. Inter-study repeatability was evaluated using Bland–Altman analyses, coefficients of variation (CoV) and intra-class correlation coefficient (ICC). Ecc (%) was significantly lower with tagging than with FT or TT at 1.5 T (− 9.5 ± 3.3 vs. − 17.5 ± 3.8 vs. −15.5 ± 5.2, respectively, p < 0.001) and 3.0 T (− 13.1 ± 1.8 vs. − 19.4 ± 2.9 vs. − 17.3 ± 2.1, respectively, p = 0.001). This was similar for PEDSR (.s −1 ): 1.5 T (0.6 ± 0.2 vs. 1.5 ± 0.4 vs. 1.0 ± 0.4, for tagging, FT and TT respectively, p < 0.001) and 3.0 T (0.6 ± 0.2 vs. 1.5 ± 0.3 vs. 0.9 ± 0.3, respectively, p < 0.001). Inter-study repeatability for Ecc at 1.5 T was good for tagging and excellent for FT and TT: CoV 16.7%, 6.38%, and 8.65%, respectively. Repeatability for Ecc at 3.0 T was good for all three techniques: CoV 14.4%, 11.2%, and 13.0%, respectively. However, repeatability of PEDSR was generally lower than that for Ecc at 1.5 T (CoV 15.1%, 13.1%, and 34.0% for tagging, FT and TT, respectively) and 3.0 T (CoV 23.0%, 18.6%, and 26.2%, respectively). Following STEMI, Ecc and PEDSR are higher when measured with FT and TT than with tagging. Inter-study repeatability of Ecc is good for tagging, excellent for FT and TT at 1.5 T, and good for all three methods at 3.0 T. The repeatability of PEDSR is good to moderate at 1.5 T and moderate at 3.0 T. Cine-based methods to assess Ecc following STEMI may be preferable to tagging.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".