Effects of the wall filter on the estimation of high blood velocity
Bibliographic record
Abstract
In pulsed wave Doppler ultrasonic measurements, a high-pass wall filter is used to remove the clutter signal prior to the blood velocity estimation. For high velocity measurements, the wall filter creates dead zones where the Doppler frequency equals multiples of the pulse repetition frequency (PRF). In this work, the effect of the wall filter has been studied for two different blood velocity estimators; the crosscorrelation method (CCM) and the extended autocorrelation method (EAM). When the pulse bandwidth is sufficiently high, the Doppler signal bandwidth will exceed the wall filter cut-off frequency due to the transit-time effect, and the dead zones are partially removed. However, the chance of velocity aliasing is increased in these zones due to the filtering, both for the CCM and EAM method. The effects of the wall filter have been studied by simulations with rectilinear velocities up to four times the Nyquist limit (/spl nu//sub NY/). In this simulation, the pulse bandwidth is 2.5 MHz. When the cut-off frequency of the wall filter is 0.1/sup */PRF, no velocity aliasing has been observed. When the wall filter is increased to 0.2*PRF, there is 15% aliasing error occurring at velocity=2/sup *//spl nu//sub NY/ and no velocity aliasing at v=4*/spl nu//sub NY/. When the wall filter is increased to 0.25*PRF, there is about 70% velocity aliasing error at twice /spl nu//sub NY/, and 15% velocity aliasing error at velocity four times /spl nu//sub NY/. The simulation results have further been verified by experimental data from subclavian artery measurements with velocities up to twice the Nyquist limit.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".