Exploring the Potentials of polymer-based CMUTs for 3D Ultrasound Computed Tomography
Bibliographic record
Abstract
This work presents a quantitative evaluation of polymer-based Capacitive Micromachined Ultrasonic Transducers (polyCMUTs) for 3D Ultrasound Computed Tomography (3D USCT). The study was motivated by limitations of the currently used PZT fiber technology in terms of bandwidth and transmit sensitivity. We developed finite element models of polyCMUT elements consisting of 127 cells to predict the acoustic performance. We fabricated prototype transducers using a novel method for microstructuring polymer layers. The produced samples reach a fractional bandwidth of 116%, an opening angle of 44° and increase the transmit sensitivity by 54%, compared to the PZT fiber transducers. The developed models allow for accurate predictions of the acoustic field over a large range of angles and frequencies. More work is required to improve the reliability and reduce sample-to-sample variations. Based on the measured performance and the general properties of the technology, polyCMUTs are very promising for 3D USCT.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".