Echo-Lagrangian particle tracking: an ultrasound-based method for extracting path-dependent flow quantities
Bibliographic record
Abstract
Abstract Eulerian, ultrasound-based velocimetry has become a popular tool for evaluating non-optically accessible flows, and has demonstrated great potential for medical flows. In contrast, the current study presents a Lagrangian method of extracting path-dependent dynamics from time-resolved ultrasound images referred to here as echo-Lagrangian particle tracking (echoLPT). Ultrasound system parameters specific to Lagrangian tracking are detailed for recording pulsatile flow through an idealized stenosis model. Furthermore, seeding materials and image processing procedures are discussed in order to improve signal-to-noise ratio and minimize particle image ambiguity. The pathlines that result from echoLPT reveal mixing downstream of the stenosis, and yield time-resolved, path-dependent information. As a means to demonstrate the value of echoLPT, particle residence time (PRT) in the post-stenotic region is calculated. PRT is the length of time a fluid parcel remains within a region of interest, and is used to highlight the effects of pulsatility. For the pulsatile flows tested, PRT is shown to increase with the frequency of pulsation as fluid is swept into the recirculation region, while PRT is decreased with increasing mean Reynolds number and amplitude ratio.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".