10B-6 A Composite Imaging Technique for High Frame-Rate and Full-View Cardiovascular Ultrasound and Elasticity Imaging
Bibliographic record
Abstract
The frame-rate of ultrasound radio-frequency (RF) data acquisition is critical for imaging of the pulse wave and electromechanical wave propagation in cardiovascular tissues as well as improving the strain estimation. Therefore, an automated method had been developed to overcome the frame-rate limitations on standard systems by retrospective multi-sector signal acquisition through an electrocardiogram (ECG) gating technique. The method achieved a frame rate of 481 Hz at a 100% field of view, 64 line densities and an imaging depth of 11 cm. The composite full-view images were reconstructed by retrospectively combining seven small-sector RF frames using the ECG-gating technique. The axial displacements of both long-axis and short-axis views of a human left ventricle and a long-axis view of the abdominal aorta were calculated using an RF based speckle-tracking technique comprising ID cross-correlation methods in a 2D search (window size of 6.9 mm and overlap of 80%). Several sequences of electromechanical waves propagating in a left ventricular long-axis and short-axis view, and long-axis view of abdominal aorta were imaged at high frame rates. Currently, the method was implemented on an Ultrasonix RP system (Ultrasonix Medical Corp. Richmond, Canada) and could be potentially implemented on other clinical systems.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.004 |
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".