Robust and high-frame-rate visualization of arterial pulse wave propagation dynamics
Bibliographic record
Abstract
Crucial to the facilitation of early detection of vascular diseases is the development of new imaging techniques that can track vascular dynamics at sub-millisecond time resolution. Previously, we have designed new high-frame-rate ultrasound imaging solutions that can track complex flow dynamics at >1000 fps frame rate, thereby providing hemodynamic insights for vascular diagnostics. Here, we have expanded our suite of vascular diagnostic tools by developing new imaging modes for mapping the structural dynamics of arterial walls. We have particularly devised a novel framework for robust ultrasound-based mapping of the spatiotemporal dynamics of arterial pulse wave (PW) propagation. Our framework involves: 1) plane wave data acquisition; 2) eigen-based data processing; and 3) phase-based motion estimation. The eigen-processing module represents a novel application of singular value decomposition, and its purpose is to isolate the desired forward PW through identifying the corresponding left singular vector through a frequency analysis algorithm, thus making our framework robust against the biasing impact of PW reflections. Also, to visualize PW propagation, positions of PW front were mapped and trailed with its past positions. Using this new eigen-based PW visualization framework, we can now accurately identify differences in PW propagation dynamics in arterial segments of different wall thicknesses and wall elasticity. Corresponding cineloops will be shown at the meeting.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".