Nyquist velocity extension in ultrafast color Doppler
Bibliographic record
Abstract
Conventional color Doppler is limited by a low frame rate (~15-20 fps in echocardiography) as a consequence of the focused transmit beamforming approach. Another inconvenience in color Doppler is the maximum unambiguous velocity that can be estimated. Aliasing occurs in color Doppler when the flow speed exceeds the Nyquist velocity (VN). Such Nyquist velocity is proportional to the pulse repetition frequency (PRF): either the maximum image depth or the Nyquist velocity can be increased at the expense of the other one. In this study, we use ultrafast ultrasound imaging (with diverging circular beams) along with a staggered multiple-PRF scheme to obtain color Doppler images at very high frame rates and solve the velocity ambiguity dilemma. The ultrafast multiple-PRF scheme for color Doppler was tested in two in vitro models. Staggered dual- or triple-PRF sequences were used to delay transmits. RF signals were dynamically focused and demodulated, then the 2-D autocorrelator was used to provide Doppler velocity estimates. The Nyquist velocity was extended using the full information provided by the dual- or triple-PRF velocity data. The de-aliased (unambiguous) velocity estimates were compared to ground truth values: for the spinning disc set up, the NRMSE ranged between 8.2% (at 94 cm.s-1) and 4.8% (at 140 cm.s-1), showing an accurate agreement. An adequate fit (r2= 0.97) was also obtained between the estimated and theoretical maximum velocities for the free water jet set up. This study shows that ultrafast staggered-PRF can increase the frame rate and extend the Nyquist velocity of color Doppler imaging.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| 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".