Ultrafast Orthogonal Row–Column Electronic Scanning (uFORCES) With Bias-Switchable Top-Orthogonal-to-Bottom Electrode 2-D Arrays
Bibliographic record
Abstract
Top-orthogonal-to-bottom electrode (TOBE) arrays, also known as row-column arrays, have shown great promise as an alternative to fully wired 2-D arrays, owing to a considerable reduction in channels. Novel imaging schemes with bias-switchable TOBE arrays were previously shown to offer promise compared with previous nonbias-switchable row-column imaging schemes and compared with previously developed explososcan methods, however, they required significant coherent compounding. Here, we introduce ultrafast orthogonal row-column electronic scanning (uFORCES), an ultrafast coded synthetic aperture imaging method. Unlike its FORCES predecessor, uFORCES can achieve coherent compounding with only a few transmit events and may, thus, be more robust to tissue motion. We demonstrate through simulations that uFORCES can potentially offer improved resolution compared with the matrix probes having beamformers constrained by the paraxial approximation. Also, unlike current matrix probe technology incorporating microbeamforming, uFORCES with bias-switchable TOBE arrays can achieve ultrafast imaging at thousands of frames per second using only row and column addressing. We also demonstrate the experimental implementation of uFORCES using a fabricated 128 ×128 electrostrictive TOBE array on a crossed 25- [Formula: see text] gold wire phantom and a tissue-mimicking phantom. The potential for improved resolution and ultrafast imaging with uFORCES could enable new essential imaging capabilities for clinical and preclinical ultrasound.
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 | 0.000 |
| 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".