Non-invasive measurement of pressure gradients using ultrasound
Bibliographic record
Abstract
A non-invasive method for estimating 2-D pressure gradients from ultrasound vector velocity data is presented. The method relies on in-plane vector velocity elds acquired using the Transverse Oscillation method. The pressure gradients are estimated by applying the Navier-Stokes equations for isotropic uids to the estimated velocity elds. The velocity elds were measured for a steady ow on a carotid bifurcation phantom (Shelley Medical, Canada) with a 70% constriction on the internal branch. Scanning was performed with a BK8670 linear transducer (BK Medical, Denmark) connected to a BK Medical 2202 UltraView Pro Focus scanner. The results are validated through nite element simulations of the carotid ow model where the geometry is determined from MR images. This proof of concept study was conducted at nine ultrasound frames per second. Estimated pressure gradients along the longitudinal direction of the constriction varied from 0 kPa/m to 10 kPa/m with a normalized bias of -9.1% for the axial component and -7.9% for the lateral component. The relative standard deviation of the estimator, given in reference to the peak gradient, was 28.4% in the axial direction and 64.5% in the lateral direction. A study made across the constriction was also conducted. This yielded magnitudes from 0 kPa/m to 7 kPa/m with a normalized bias of -5.7% and 13.9% for the axial and lateral component, respectively. The relative standard deviations of this study were 45.2% and 83.2% in the axial and lateral direction, respectively.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".