Calculation of Carotid Artery Flow Rates Using Doppler Ultrasound: Implications of Velocity Profile Skewing
Bibliographic record
Abstract
Doppler ultrasound (DUS) is a non-invasive means of obtaining patient-specific flow boundary conditions in computational modelling studies [1] or estimating volumetric flow in clinical studies [2, 3]. To convert velocity information to a flow waveform, three related assumptions are often applied, 1) that the peak velocity lies in the centre of a cylindrical vessel, 2) that a centrally-located sample volume will thus detect the peak velocity, and 3) that the velocity profile is fully-developed and axisymmetric, being well-approximated by a parabolic (Poiseuille) or Womersley profile. These assumptions may not always be valid, however, even for nominally straight vessels like the common carotid artery (CCA) [4, 5]. While one might expect that flow estimated from DUS would become increasingly inaccurate as the profile becomes less axisymmetric, the scale of such errors and their relation to the true profile shape have not been quantified for the CCA. Moreover, for a heavily skewed velocity profile, the peak velocity may not lie within the DUS sample volume, and hence the choice of sample volume or beam-vessel orientation may also affect the accuracy of flow calculations. In this study, we investigate these issues by performing an idealized virtual DUS on data from image-based computational models of the carotid bifurcation.
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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 | 0.007 | 0.069 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".