High frame-rate visualization of blood flow with ultrasound contrast agents
Bibliographic record
Abstract
Recent breakthroughs in diagnostic ultrasound system architectures have enabled new high frame rate (kHz) capabilities, which are opening new opportunities in blood flow imaging. In order to leverage these increases in temporal resolution broader or unfocused beams are employed. The combination high frame and less focused beams with ultrasound contrast agents introduces both new opportunities and trade-offs. One new opportunity is the ability to combine Doppler based processing to visualize both lower velocity blood flow in the microcirculation and higher velocity flow in larger vasculature not possible with conventional techniques. This can be accomplished by combining nonlinear pulsing sequences to separate microbubble and tissue signals with these less focused beams to separate different microbubble flow velocities. The use of less focused beams to image nonlinear echoes from microbubbles distributes ultrasound energy to microbubbles in temporally and spatially different ways. This work explores the compromises of using these less focused beams to visualize moving microbubbles at higher temporal resolutions. The focus will be on the practical challenges of generating nonlinear signals while balancing microbubble disruption and current system reconstruction capabilities. Both in-vitro and in-vivo results will be presented.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".