Impact of Ultrasound Scanning Plane on Common Carotid Artery Longitudinal Wall Motion
Bibliographic record
Abstract
OBJECTIVE: The arterial wall not only moves in the radial direction to expand circumferentially but also moves in the axial (longitudinal) direction in a predictable bidirectional pattern during a normal cardiac cycle. While common carotid artery (CCA) longitudinal wall motion (CALM) has been described previously, there is a lack of evidence-based method standardization to align practices for human measurement. The purpose of this study was to evaluate whether different scanning planes impact CALM outcomes in healthy males and females to provide clarity on data collection strategies. METHODS: Thirty-one healthy adults (16 females, 23 ± 3 y of age) underwent ultrasound scanning of the right CCA in the anterior, lateral, and posterior imaging planes. CALM was evaluated using a custom speckle-tracking algorithm and was analyzed as segmental motion outcomes (anterograde, retrograde, maximum displacement and radial-axial path length). RESULTS: No differences in any CALM outcome were observed between imaging planes (p > 0.05), and equivalence testing indicated that retrograde CALM displacement was similar between anterior and posterior distal walls (p = 0.04). We observed no differences (p > 0.05) in CALM outcomes between the proximal (free-wall, adjacent to the internal jugular vein [IJV]) and distal wall in the posterior imaging plane. Qualitatively, it was more difficult to successfully track vascular tissue between the IJV and CCA due to the thin wall components and highly mobile wall in the radial direction. CONCLUSION: In the absence of clear differences between scanning planes, we recommend standardizing acquisition in the lateral plane and avoiding the IJV free-wall when evaluating CALM in humans.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".