Functional myocardial assessment in cine cardiac computerized tomographic angiography using echocardiographic feature-tracking software in patients with and without significant coronary disease
Bibliographic record
Abstract
Introduction: Cardiac computerized tomographic angiography (CCTA) is perceived as a non-invasive tool for assessment of coronary vessel anatomy. Feature tracking echocardiography has recently emerged as a tool for assessment of regional and global left ventricular function. We aimed to explore the applicability of echocardiographic strain on CCTA cine clips and assess whether global and regional strain parameters are associated with the extent of coronary stenosis. Methods: CCTA studies of 61 consecutive patients were reconstructed to yield cine images in classic echocardiographic long and short views. Siemens Velocity Vector Imaging (VVI) software was applied to generate strain and displacement results. Volumetric and mechanics parameters were compared among patients with no or non-significant coronary artery disease (CAD) and patients with significant CAD. Finally, a comparison of the degree of coronary stenosis to regional segmental strain was performed. Results: Myocardial mechanics parameters could be generated in 60 cases. Ejection fraction (EF) and left ventricular end diastolic volume (LVEDV) were within the normal range in both groups. VVI values were lower in the CAD group (VVI LVEF 59 ± 6 vs. 50 ± 11, p = 0.0002). Global longitudinal and global circumferential strain both were significantly lower in this group. Regional segmental strain was lower in segments affected by coronary stenosis in comparison to unaffected segments. Conclusion: While CT segmentation derived LVEF did not differ among groups, patients with significant coronary stenosis had reduced longitudinal and circumferential contraction. This suggests that application of VVI to CCTA cine clips tracking may help to differentiate significant and non-significant coronary stenosis, adding functional value to anatomic findings in CCTA.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| 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".