Magnetic Resonance–based Assessment of Myocardial 2-Dimensional Strain Using Feature Tracking
Bibliographic record
Abstract
PURPOSE: Myocardial strain analysis is a promising tool for the detection of subtle but relevant alterations of left ventricular function, also in asymptomatic subjects. Thus, we determined the feasibility of cardiac magnetic resonance-based 2D global strain analysis using feature tracking and its association with cardiovascular risk factors in a sample from the general population. MATERIALS AND METHODS: Subjects without a history of cardiocerebrovascular disease were enrolled in a substudy of the population-based KORA (Cooperative Health Research in the Region of Augsburg) cohort. In all participants with the absence of late gadolinium enhancement, longitudinal and circumferential global strains were measured on Cine SSFP imaging (TR: 29.97 ms, TE: 1.46 ms, ST: 8 mm), using a semiautomatic segmentation algorithm (CVI42, Circle, Canada). Differences in strain values according to age, sex, body mass index, hypertension, diabetes mellitus, and hyperlipidemia were derived using linear regression analysis. RESULTS: Among 360 subjects (mean age, 56.2±9.2 y, 57% male), the average global systolic radial strain was 40.1±8.2%, circumferential 19.9±2.7%, and longitudinal 19.8±3.2%. Male sex was associated with decreased global strain values, independent of the strain direction (all P<0.001). Although many cardiovascular risk factors were correlated with strain in univariate analysis, mainly waist-to-hip ratio and HbA1c remained associated with decreased radial and circumferential strains in fully adjusted models. Similarly, higher radial and circumferential strains were observed in older subjects (β=0.14, P=0.01 and β=0.11, P=0.04, respectively). CONCLUSIONS: Strain analysis using magnetic resonance feature tracking is feasible in population-based cohort studies and shows differences with respect to age and sex as well as an independent association with markers of metabolic syndrome.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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 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".