Multifrequency Interlaced CMUTs for Photoacoustic Imaging
Bibliographic record
Abstract
Multifrequency capacitive micromachined ultrasound transducers (CMUTs) are introduced consisting of interlaced 82- [Formula: see text] (low frequency) and 36- [Formula: see text] (high frequency) membranes. The membranes have been interlaced on a scale smaller than the shortest wavelength of operation allowing several advantages over other multifrequency transducer designs including aligned beam profiles, optimal imaging resolution, and minimal grating lobes. The low- and high-frequency CMUTs operate at 1.74 and 5.04 MHz in immersion, respectively. Multifrequency transducers have applications in wideband photoacoustic (PA) imaging where multifrequency transducers are better able to detect both high- and low-frequency PA frequency content. The PA frequency content is target size dependent, which means traditional high-frequency transducers have less sensitivity to larger objects such as diffuse contrast agents. We demonstrate that the low-frequency subarrays are able to better visualize diffuse agent distributions, while the high-frequency subarrays offer fine-resolution imaging important for microvascular imaging and structural navigation. Spectroscopically unmixed images superimpose high sensitivity images of agent concentrations (acquired using low-frequency subarrays) onto high-resolution images of microvessel-mimicking phantoms (acquired using high-frequency subarrays).
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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) | 0.002 | 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".