High-volume-rate ultrasound 3-D flow imaging with a 2-D spiral array: A simulation study
Bibliographic record
Abstract
High-volume-rate ultrasound imaging enables the rendering of complex flow dynamics in 3-D; however, high data rates pose significant hurdles towards real-time implementation. A sparsely populated 256-element 2-D spiral array coupled with unfocused transmissions is a potential solution to these challenges. This work analyzes the impact of sparse element distribution on flow velocity estimation with spherical waves (virtual point source 20mm behind the probe) to extend the field of view beyond the array footprint. Field II was used to simulate straight tube flow (6mm-diameter; 45° tilt; 30cm/s plug-flow; Transmission: 7.5 MHz, 3-cycle) to analyze the velocity estimation bias without the influence of surrounding tissue. It was found that the bias at the inlet, mid-section, and outlet were 20.3%, 8.7%, and 4%, respectively; this variation in bias is due to the change in beam-flow angle from 57.8 deg to 34.9 deg. To evaluate the wide-angle and grating-lobe performance of this configuration, the median Doppler power was compared between the center flow region, flow at the extended regions, and the grating lobe region; the relative power to the center flow region was found to be -12.3dB and -19.5dB for extended and grating-lobe regions respectively. These results suggest that the flow estimation region can be extended beyond the array footprint area but extra considerations on the beam-flow angle must be taken, especially in the case of vector flow.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".