Development of a Small-Footprint 50 MHz Linear Array: Fabrication and Micro-Ultrasound Imaging Demonstration
Bibliographic record
Abstract
Compact high-frequency arrays are of interest for clinical and preclinical applications in which a small-footprint or endoscopic device is needed to reach the target anatomy. However, the fabrication of compact arrays entails the connection of several dozens of small elements to the imaging system through a combination of flexible printed circuit boards at the array end and micro-coaxial cabling to the imaging system. The methods currently used, such as wire bonding, conductive adhesives, or a dry connection to a flexible circuit, considerably increase the array footprint. Here, we propose an interconnection method that uses vacuum-deposited metals, laser patterning, and electroplating to achieve a right-angle, compact, reliable connection between array elements and flexible-circuit traces. The array elements are thickened at the edges using patterned copper traces, which increases their cross-sectional area and facilitates the connection. We fabricated a 2.3 mm by 1.7 mm, 64-element linear array with elements at a 36 μm pitch connected to a 4 cm long flexible circuit, where the interconnect adds only 100 μm to each side of the array. Pulse-echo measurements yielded an average center frequency of 55 MHz and a -6 dB bandwidth of 41%. We measured an imaging resolution of 35 μm in the axial direction and 114 μm in the lateral direction and demonstrated the ex vivo imaging of porcine esophageal tissue and the in vivo imaging of avian embryonic vasculature.
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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 | 0.000 |
| 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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".