Transit-time broadening in pulsed Doppler ultrasound: a generalized amplitude modulation model
Bibliographic record
Abstract
In Doppler ultrasound, transit-time broadening arises from the finite scatterer transit time through the sample volume. As a unifying description of this broadening mechanism, a generalized amplitude modulation signal model was developed to collectively account for the transit-time effects of the ultrasound beam geometry and the range gate characteristics. Simulations based on a pulsed linear-array system also were performed to study the broadening extent for different scatterer flow lines. With our signal model and simulation results, some generalized insights were obtained on the characteristics of transit-time broadening. First, as consistent with previous findings, we found that, for scatterers passing though the center of the sample volume, the broadening extent mainly depends on beam-forming characteristics at higher beam-flow angles, but it is more dependent on range gate parameters at smaller angles. Second, for the central flow line, a transition angle exists in which a significant change occurs in the governing parameters of transit-time broadening. Third, for the general case in which scatterers undertake an off-central path through the sample volume, the broadening extent depends on both the beam geometry and the range gate. Bandwidth skewing and further spectral broadening also can be seen for these off-central flow lines.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 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".