Ultrafast Ultrasound Imaging in Pediatric and Adult Cardiology
Bibliographic record
Abstract
Ultrasound techniques currently used in echocardiography are limited by conventional frame rates. Ultrafast ultrasound imaging is able to capture images at frame rates up to 100 times faster compared with conventional imaging. Specific applications of this technology have been developed and tested for clinical use in pediatric and adult cardiac imaging. These include ultrafast Doppler or vector flow imaging, shear wave imaging, electromechanical wave imaging, and backscatter tensor imaging. The principles of these applications are explained in this manuscript with illustrations on how these methods could be applied in clinical practice. Ultrafast ultrasound has great clinical potential in the assessment of cardiac function, in noninvasive hemodynamic analysis, while providing novel techniques for imaging coronary perfusion and evaluating rhythm disorders. • Ultrafast ultrasound imaging could be a central noninvasive imaging tool, particularly in congenital and pediatric cardiology. • Myocardial stiffness assessment by ultrafast ultrasound imaging has the potential to become a cornerstone of ultrasound imaging in cardiology, particularly for the noninvasive assessment of systolic and diastolic physiology. • Further clinical developments could potentially reduce the need for cardiac magnetic resonance or imaging techniques requiring radiation.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".