Coupling Myocardium and Vortex Dynamics in Diverging-Wave Echocardiography
Bibliographic record
Abstract
Echocardiography is widely used to provide critical left ventricular indices describing myocardial motion and blood inflow velocity. Tissue motion and blood flow are strongly connected and interdependent in the ventricle. During cardiac relaxation, rapid filling leads to the formation of a vortical blood flow pattern. In this paper, we introduce a high-frame-rate method to track vortex dynamics alongside myocardium motion, in a single heartbeat. Cardiac triplex imaging (B-mode + tissue Doppler + color Doppler) was obtained by insonating the left ventricle with diverging waves. We used coherent compounding with integrated motion compensation to obtain high-quality B-mode images. Tissue Doppler was retrieved and the septal and lateral velocities of the mitral annulus were deduced. A rate of ~80 triplex images/s was obtained. Vortex dynamics was analyzed by Doppler vortography. Blood vortex signature maps were used to track the vortex and compute core vorticities. The sequence was implemented in a Verasonics scanner with a 2.5-MHz phased array and tested in vivo in 12 healthy volunteers. Two main peaks appeared on the vorticity curves. These peaks were synchronized with the mitral inflow velocities with a small delay. We observed a relationship between the tissue and vortex waveforms, though also with a delay, which denoted the lag between the wall and the flow motion. Clinical diastolic indices combining basal and mitral inflow velocities (E/A ratio and E/e' ratio) were determined and compared with those measured using a conventional ultrasound scanner; a good correlation was obtained (r2= 0.96). High-frame-rate Doppler echocardiography enabled us to retrieve time-resolved dynamics of the myocardium and vortex flow within the same cardiac cycle. Coupling wall-flow analysis could be of clinical relevance for early diagnosis of filling impairment.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".