High-resolution, high-contrast ultrasound imaging using a prototype dual-frequency transducer: In vitro and in vivo studies
Bibliographic record
Abstract
With recent advances in animal models of disease, there has been great interest in capabilities for highresolution contrast-enhanced ultrasound imaging. Microbubble contrast agents are unique in that they scatter broadband ultrasound energy because of their nonlinear behavior. For optimal response, it is desirable to excite the microbubbles near their resonant frequency. To date, this has been challenging with high-frequency imaging systems because most contrast agents are resonant at frequencies in the order of several megahertz. Our team has developed a unique dual-frequency confocal transducer which enables low-frequency excitation of bubbles near their resonance with one element, and detection of their emitted high-frequency content with the second element. Using this imaging approach, we have attained an average 12.3 dB improvement in contrast-to-tissue ratios over fundamental mode imaging, with spatial resolution near that of the high-frequency element. Because this detection method does not rely on signal decorrelation, it is not susceptible to corruption by tissue motion. This probe demonstrates contrast imaging capability with significant tissue suppression, enabling high-resolution contrast-enhanced images of microvascular blood flow. Additionally, this probe can readily produce radiation force on flowing contrast agents, which may be beneficial for targeted imaging or therapy.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".