Dual-Frequency CMUT Arrays for Multiband Ultrasound Imaging Applications
Bibliographic record
Abstract
Dual-frequency capacitive micromachined ultrasonic transducers (CMUTs) are introduced for multiscale imaging applications, where a single array transducer can be used for both deep low-resolution imaging and shallow high-resolution imaging. These transducers consist of low- and high-frequency membranes interlaced within each subarray element. They are fabricated using a modified sacrificial release process. Successful performance is demonstrated using wafer-level vibrometer testing, as well as acoustic testing on wirebonded dies consisting of arrays of 2- and 9-MHz elements of up to 64 elements for each subarray. The arrays are demonstrated to provide multiscale, multiresolution imaging using wire phantoms and can span frequencies from 2 MHz up to as high as 17 MHz. Peak transmit sensitivities of 27 and 7.5 kPa/V are achieved with the low- and high-frequency subarrays, respectively. At 16-mm imaging depth, lateral spatial resolution achieved is 0.84 and 0.33 mm for low- and high-frequency subarrays, respectively. The signal-to-noise ratio of the low-frequency subarray is significantly higher for deep targets compared to the high-frequency subarray. The array achieves multiband imaging capabilities difficult to achieve with current transducer technologies and may have applications to multipurpose probes and novel contrast agent imaging schemes.
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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) | 0.000 | 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 | 0.001 |
| 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".