Ultrasound speckle tracking for radial, longitudinal and circumferential strain estimation of the carotid artery – An in vitro validation via sonomicrometry using clinical and high-frequency ultrasound
Bibliographic record
Abstract
Ultrasound speckle tracking for carotid strain assessment has in the past decade gained interest in studies of arterial stiffness and cardiovascular diseases. The aim of this study was to validate and directly contrast carotid strain assessment by speckle tracking applied on clinical and high-frequency ultrasound images in vitro. Four polyvinyl alcohol phantoms mimicking the carotid artery were constructed with different mechanical properties and connected to a pump generating carotid flow profiles. Gray-scale ultrasound long- and short-axis images of the phantoms were obtained using a standard clinical ultrasound system, Vivid 7 (GE Healthcare, Horten, Norway) and a high-frequency ultrasound system, Vevo 2100 (FUJIFILM, VisualSonics, Toronto, Canada) with linear-array transducers (12L/MS250). Radial, longitudinal and circumferential strains were estimated using an in-house speckle tracking algorithm and compared with reference strain acquired by sonomicrometry. Overall, the estimated strain corresponded well with the reference strain. The correlation between estimated peak strain in clinical ultrasound images and reference strain was 0.91 (p<0.001) for radial strain, 0.73 (p<0.001) for longitudinal strain and 0.90 (p<0.001) for circumferential strain and for high-frequency ultrasound images 0.95 (p<0.001) for radial strain, 0.93 (p<0.001) for longitudinal strain and 0.90 (p<0.001) for circumferential strain. A significant larger bias and root mean square error was found for circumferential strain estimation on clinical ultrasound images compared to high frequency ultrasound images, but no significant difference in bias and root mean square error was found for radial and longitudinal strain when comparing estimation on clinical and high-frequency ultrasound images. The agreement between sonomicrometry and speckle tracking demonstrates that carotid strain assessment by ultrasound speckle tracking is feasible.
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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 | 0.004 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".