Extending the Transmit and Receive Bandwidths of Dual-Frequency Transducers Toward Clinical Acoustic Angiography: In Vitro and In Vivo Studies
Bibliographic record
Abstract
The acoustic angiography leverages the superharmonic response of microbubbles against linear tissue to generate 3-D maps of microvasculature. This contrast-enhanced ultrasound imaging approach uses dual-frequency (DF) transducers that transmit at frequencies less than 5 MHz and receive at frequencies three times or greater than the fundamental frequency to selectively detect microbubble signals. Previous iterations of the hardware were designed mainly to image preclinical models. In pilot clinical imaging studies, these transducers suffered from poor microbubble sensitivity and shallow imaging depths. Here, we investigate multiple DF transducers operating at varying transmit frequencies less than 2 MHz and center receive frequencies ranging from 7 to 18 MHz designed for deeper imaging and greater bubble sensitivity than earlier generation devices. We assess the superharmonic imaging (SpHI) performance of these transducers in vitro and in vivo by characterizing contrast sensitivity and resolution. We demonstrate improvements in sensitivity at lower transmit (<1 MHz) and receive (<10 MHz) frequencies, measuring contrast signal enhancement up to 31.8 dB. At these lower frequencies, we also achieve imaging depths up to 50-55 mm-the deepest application of acoustic angiography to date. These advances in imaging sensitivity and depth address the primary barriers to the clinical translation of acoustic angiography.
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 | 0.003 |
| 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 | 0.000 | 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".