Rational design of flow phantoms for evaluation of new Doppler ultrasound techniques
Bibliographic record
Abstract
With various advances in Doppler ultrasound technology such as high-frame-rate flow imaging and vector flow estimation, there is a growing need to devise appropriate phantoms that can holistically assess the accuracy of the derived flow estimates. Straight-tube models simply cannot serve this purpose, nor can spinning discs that simulate tissue motion instead of flow dynamics. In this presentation, we shall highlight a series of innovations in devising flow phantoms that can foster meticulous performance evaluation of new Doppler ultrasound techniques. First to be discussed is the design of a novel wall-less spiral phantom with omnidirectional flow (i.e., from 0 to 360 deg). With a three-loop spiral geometry with 4 mm lumen diameter and 5 mm pitch, this phantom was developed using a combination of computer-aided design, 3-D printing of vessel core, and lost-core casting with polyvinyl alcohol cryogel. It is useful for testing the performance of flow vector estimators. The design of anatomically realistic flow phantoms will also be described, and a representative example based on patient-specific aneurysm geometry will be highlighted. In addition, methodological innovations in developing walled arterial phantoms will be presented. Their application will be demonstrated in the context of testing new vascular imaging techniques that simultaneously track wall motion and blood 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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".