Comparison of cardiac MR feature tracking and myocardial MR tagging for assessment of regional ventricular function
Bibliographic record
Abstract
Abstract Background Quantification of regional myocardial function allows risk stratification in heart disease. CMR tagging (TAG) enables the evaluation of segmental cardiac deformation, but it has not reached clinical routine due to the long acquisition and post-processing times. Conversely, CMR feature-tracking (FT) is a post-processing method based on standard cine-MR imaging. Purpose To compare myocardial strain and torsion obtained with CMR-TAG and CMR-FT in healthy volunteers and myocardial infarction (MI). Methods 42 subjects (18 healthy; 24 MI) underwent CMR (1.5T, cine/TAG sequences). Global and segmental (16-segment) circumferential strain (CS), and torsion were measured using FT (CVI42, Canada) and tagging (InTag, France). Inter-method agreement was assessed using 2-way-mixed intraclass correlation coefficient (ICC). Results The agreement for segmental and global CS measurements was good to excellent in both groups (Table). Torsion angle showed excellent (0.763) and good (0.697) agreement for healthy and MI. Conclusion CMR-FT strain and torsion measurements showed high agreement with CMR-tagging. Thus, CMR-FT provides a potential clinical alternative in the assessment of regional ventricular function. Funding Acknowledgement Type of funding source: Public grant(s) – National budget only. Main funding source(s): Carlos III Health Institute, Spanish Ministry of Economy and Competiveness; Agencia Valenciana de la Innovaciόn, Generalitat Valenciana
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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 | 0.006 | 0.009 |
| 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 | 0.001 | 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".