Transparent capacitive micromachined ultrasound transducer lineararrays for combined realtime optical and ultrasonic imaging
Bibliographic record
Abstract
Transparent ultrasound transducers could enable many novel applications involving both ultrasonics and optics. Recently, we reported transparent capacitive micromachined ultrasound transducers (CMUTs) and demonstrated through-illumination photoacoustic imaging. This work presents the feasibility of transparent CMUTs for combined ultrasound imaging and through-array white-light imaging with a miniature camera placed behind the array. Transparent CMUT devices are fabricated with an adhesive wafer bonding technique and provide high transparency up to 90% in visible wavelengths. Fabricated linear arrays have a central operating frequency of 9 MHz with 128 active elements. Realtime plane-wave imaging is performed for ultrasound imaging, and lateral and axial resolutions of, respectively, 234 and 338 µm are achieved. Transparent CMUT has demonstrated a high transmit sensitivity of 1.4 kPa/V per channel with a 100 VDC bias voltage. The signal-to-noise ratio for a beamformed image of wire targets is determined to be 28.4 dB. To the best of our knowledge, this is the first report of combined realtime optical and ultrasonic imaging with transparent arrays. This technology may enable one to visually see what is being scanned and scan what one sees without co-registration errors. Future applications could include multi-modality probes for interventional and surgical procedures.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".