Investigation of Crossbeam Multi-receiver Configurations for Accurate 3-D Vector Doppler Velocity Estimation
Bibliographic record
Abstract
An accurate estimation of low blood velocities whose Doppler shifts span the wall filter cutoff, such as near the wall in recirculation or disturbed flow regions, is important for accurate mapping of velocities to achieve improved estimations of wall shear stress and turbulence, which are known risk factors for atherosclerosis and stroke. This paper presents the comparative benefit of increasing the number of receiver beams above three for an improved estimation of low 3-D velocities. The 3-D crossbeam vector Doppler ultrasound configurations were studied in terms of the number of receiver beams, interbeam angle, and beam selection method (criterion for discriminating between tissue and blood Doppler signals) for a range of velocity orientations, which may prove useful in the design of a future 2-D array for vascular imaging. For maximum velocity resolution, a shallow gradient of low flow velocities up to 5 cm/s was generated across a large-diameter (2.46 cm) straight vessel. Data were acquired using a linear array rotated around the central transmit beam axis to generate three- to eight-receiver (3R-8R) configurations;the rotation of each configuration relative to the flow axis was used to mimic a broad range of velocity vector orientations. Accuracy and precision for ≥5 receivers were consistently better over all velocity orientations and for all selection methods. For a velocity magnitude of 2 cm/s, the best accuracy and precision in both magnitude and direction (~21% ± 13%, <1° ± 9°, respectively) were seen with a 5R configuration using a weighted least-squares selection method. Asymmetry in the 5R configuration led to an improved accuracy and precision compared with that in symmetrical 6R and 8R configurations. The results demonstrated relatively little to no benefit from more than five receiver beams.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".