A viscoelastic model of arterial wall motion in pulsatile flow: implications for Doppler ultrasound clutter assessment
Bibliographic record
Abstract
The existing computational model studies of pulsatile blood flow in arteries have assumed either rigid wall characteristics or elastic arterial wall behavior with wall movement limited to the radial direction. Recent in vivo studies have identified significant viscoelastic wall properties and longitudinal wall displacements over the cardiac cycle. Determining the nature of these movements is important for predicting the effects of ultrasound clutter in Doppler ultrasound measurements. It is also important for developing an improved understanding of the physiology of vessel wall motion. We present an analytically-based computational model based on the Womersley equations for pulsatile blood flow within elastic and viscoelastic arteries. By comparison with published in vivo data of the human common carotid artery as well as uncertainty and sensitivity analyses, it is found that the predicted waveforms are in reasonable quantitative agreement. Either a pressure, pressure gradient or volumetric flow rate waveform over a single cardiac cycle is used as an input. Outputs include the pressure, pressure gradient, radial and longitudinal fluid velocities and arterial wall displacements, volumetric flow rate and average longitudinal velocity. It is concluded that longitudinal wall displacements comparable to the radial displacements can be present and should be considered when studying the effects of tissue movement on Doppler ultrasound clutter.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".