Doppler ultrasound bandwidth imaging: A new technique for mapping unstable flow
Bibliographic record
Abstract
Unstable flow plays an important role in the initiation and progression of atherosclerosis. Yet, mapping flow instability in arteries is challenging because such event is dynamic over both space and time. In this work, we present a new framework called Doppler ultrasound bandwidth imaging (DUBI) that makes use of high-frame-rate plane wave excitation and Doppler bandwidth analysis principles to identify unstable flow regions within an image view. DUBI works by performing autoregressive modeling at every pixel position to estimate the instantaneous Doppler bandwidth, which in principle is broadened by the wider range of velocities attributed to the emergence of unstable flow. DUBI’s performance in mapping unstable flow was tested with laminar and turbulent flow conditions in a nozzle-flow phantom. Results showed that DUBI was able to quantify the difference in Doppler bandwidth magnitude (increased from 2.1 to 5.2 kHz) as the flow condition changed from laminar to turbulent. Also, DUBI was found to be able to identify and map the unstable flow region with the image view, outperforming conventional Doppler variance imaging. This observation was substantiated by receiver operating characteristic analysis, in which DUBI achieved a sensitivity of 0.72 and specificity of 0.83 (vs 0.66 and 0.68, respectively, for conventional Doppler variance imaging). We anticipate that DUBI can be applied in vivo to obtain useful information for clinical diagnosis of atherosclerosis.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 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".