High-resolution, high-contrast ultrasound imaging using a prototype dual-frequency transducer in-vitro and in-vivo studies
Bibliographic record
Abstract
Recently, there has been a great interest in the capabilities of high-resolution ultrasound imaging. One of the applications is imaging blood vessels to assess tumor growth and response to therapy. Due to their nonlinear response, microbubble contrast agents scatter ultrasound energy at frequencies higher and lower than the imaging frequency. To maximize ultrasound scatter, the imaging frequency should be near the microbubble resonant frequency. Previously, this was not possible with high-frequency imaging systems because most contrast agents resonate at 1-10 MHz and the systems pulse at higher frequencies. We have developed a unique dual-frequency confocal transducer which excites microbubbles at low frequencies, near their resonance, and detects their emitted high-frequency energy at greater than 15 MHz. With this imaging approach, we have attained an average improvement in contrast-to-tissue ratio of 12.3 dB over standard b-mode imaging for MI between 0.5 and 0.65 with spatial resolution near that of the high-frequency element (30 MHz). This method is less susceptible to tissue motion corruption than power-Doppler because it does not rely on signal-decorrelation. Because of this, dual-frequency imaging can be used for flow imaging without respiratory gating.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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 | 0.000 |
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".