Top-orthogonal-to-bottom-electrode (TOBE) CMUT arrays for 3-D ultrasound imaging
Bibliographic record
Abstract
Two-dimensional ultrasound arrays hold great promise for 3-D imaging; however, wiring of each channel becomes impractical for large arrays or for small-footprint catheter probes for which the number of wires must be limited. Capacitive micromachined ultrasound transducers offer a promising solution for such 2-D array applications, but channel routing is still non-trivial. A top-orthogonal-to-bottom-electrode (TOBE) 2-D CMUT array architecture is presented along with row-column addressing schemes for low-channel-count 3-D ultrasound imaging. An N × N TOBE array is capable of obtaining 3-D images using only 2N channels. An interfacing scheme is presented in which transmit-receive signals are routed along rows while bias voltages are applied along columns, effectively allowing for single-element transmit/receive control. Simulations demonstrated potentially finer resolution and improved side lobe suppression over a previously published row-column-based imaging method. Laser vibrometer testing was done to measure membrane displacement in air and confirmed that single-element air-coupled actuation in transmit mode could be achieved using our proposed interfacing scheme. Acoustic testing was also performed in both transmit and receive modes to characterize the ability of the proposed interfacing scheme to achieve dominant-element transmission and reception in immersion operation. It was seen that membrane displacement in both modes was indeed largely confined to the active area.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".