Preliminary Demonstration of Pulse-Echo Imaging With a Long Monolithic Flexible CMUT Array
Bibliographic record
Abstract
Conformal ultrasound imaging using large-area transducer arrays is an emerging technology with significant potential for real-time, continuous, functional, and health monitoring applications. This study addresses the challenge of fabricating such transducer arrays by presenting the development and preliminary imaging performance of a monolithic flexible capacitive micromachined ultrasonic transducer (CMUT) array—the longest reported to date. A 128-element, 91 mm long flexible array was designed and fabricated using a lithography process, with SU-8 CMUT structures on a bendable polyimide substrate. The array was then packaged by mounting it onto a flexible printed circuit board and coated with a thin polydimethylsiloxane (PDMS) layer for in vivo testing. Electrical impedance measurements confirmed the full functionality of all transducer elements, with an average center resonant frequency of 5.84 MHz (SD: 0.14 MHz). Pulse-echo imaging experiments demonstrated the array’s capability to detect specular reflections and resolve fine features under various curvatures. In vivo scans revealed outlines of a finger and superficial tissues in the forearm. Despite the limitations associated with the lack of custom front-end electronics and low signal-to-noise ratio beamforming strategies, this work demonstrates the feasibility of manufacturing a flexible CMUT array suitable for large-area conformal sonography.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".