Ultrafast myocardial elastography using coherent compounding of diverging waves during simulated stress tests: An in vitro study
Bibliographic record
Abstract
Objective myocardial deformation assessment during stress tests could help clinicians to better diagnose myocardial ischemia. However, the use of conventional focused echocardiography is compromised at increased heart rates due to its limited lateral field of view and frame rate. Ultrafast echocardiography using coherent compounding of diverging waves improves temporal resolution while maintaining a large field of view and could be a valuable alternative during stress tests. This study aimed to illustrate the feasibility of estimating myocardial strain using ultrafast echocardiography combined with a Lagrangian speckle model estimator (LSME) at increased heart rates. Myocardial strain assessment was tested on a dynamic cardiac phantom at heart rates ranging from 60 to 180 beats-per-minute (bpm). Ultrafast echocardiography was obtained with a Verasonics platform equipped with a 2.5 MHz phased array transducer (PRF: 4500 Hz). Negative effects of side lobes and phase delays during the large tilted transmission and compounding of diverging waves were suppressed through a triangle transmit sequence and motion compensation strategy. The robustness and accuracy of affine strain estimation were then enhanced using radiofrequency least-squares-based LSME combined with a coarse-to-fine strain estimation and a time-ensemble estimation strategy. 2D myocardial strain images at systole and early-diastole as well as regional strain curves were estimated. Myocardial strains with high contrast-to-noise and signal-to-noise ratios were obtained at all simulated heart rates. Regional strain curves were accurately estimated and periods matched those of the phantom pump cycles. These preliminary results suggest that the use of ultrafast echocardiography combined with the modified LSME could be useful clinically to provide an accurate and objective method of myocardial strain assessment at high heart rates.
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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) | 0.001 | 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 | 0.001 | 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".