Simulation of high frame rate spread-spectrum color Doppler imaging of pulsatile flow
Bibliographic record
Abstract
Spread-spectrum Doppler, a method introduced by our lab, preserves the maximum unaliased velocity of ultrafast Doppler while retaining some of the image quality benefits of compounding plane waves transmitted at different angles. The technique employs a sequence of pulses transmitted at M angles that are repeated L times in different random orders. Shuffling the slow-time samples so the angles repeat in the ascending order concentrates echoes from stationary off-focus targets in M harmonic frequency bins while spreading the in-focus signal across all frequencies. Off-focus echoes are suppressed, without compounding, by applying a notching comb filter, while the portion of the in-focus signal spread to the other (L − 1)M bins is retained for velocity estimation. Field II simulations were used to assess the method’s ability to track pulsatile velocity fluctuations in a straight vessel. The angle-corrected peak velocity was accurate to within ±10% of the true value when imaging at a Doppler frame rate of approximately 115 frames per cardiac cycle. Further improvement of the method will require a filter to attenuate off-focus echoes from non-stationary tissue.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".