Techniques for perfusion imaging with microbubble contrast agents
Bibliographic record
Abstract
The acoustic properties of ultrasound contrast agents vary widely with agent composition and insonation conditions. For contrast imaging, methods are required to match RF and Doppler processing to each combination of transmission parameters and agent and tissue properties. We propose a method that uses the measured or modeled echoes from agent and tissue to specify directly the characteristics of RF and Doppler filters for contrast imaging. The proposed method is sufficiently general to cover most common imaging techniques including harmonic greyscale, Doppler, and pulse inversion imaging. Using this method, sample filters were designed to detect myocardial perfusion with the contrast agent Optison (Mallinckrodt Medical, St. Louis, MO) under selected imaging conditions. Simplified power Doppler filtering, using a weighted sum of the Doppler samples, matched the performance of more complicated matrix filters. By coordinating the selection of RF and Doppler filters rather than designing these filters sequentially, agent-to-tissue contrast was increased by up to 3.9 dB. Under some conditions, fundamental RF filtering outperformed harmonic filtering for intermittent Doppler imaging.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".