Fast Orthogonal Row–Column Electronic Scanning With Top-Orthogonal-to-Bottom Electrode Arrays
Bibliographic record
Abstract
Recently, top-orthogonal-to-bottom electrode 2-D arrays were introduced as a practical design for 3-D ultrasound imaging without requiring the wiring of a 2-D grid of elements. However, previously proposed imaging schemes suffered from speed or image-quality limitations. Here, we propose a new imaging scheme which we call Fast Orthogonal Row-Column Electronic Scanning (FORCES). This new approach takes advantage of bias sensitivity to enable high-quality and fast B-scan imaging. We compare this imaging scheme with an equivalent linear array, a previously proposed row-column imaging scheme, as well as with the Explososcan imaging scheme for 2-D arrays through simulations. In a point phantom simulation, the lateral (azimuthal) resolution of a 64 ×64 element 6.67-MHz λ /2-pitch array using the FORCES imaging scheme with an f-number of 1.7 was 0.52 mm with similar in-plane image quality to an equivalent linear array but with improved and electronically steerable elevational resolution. When compared with other 3-D imaging schemes in point phantom simulations, the FORCES imaging scheme showed an azimuthal resolution improvement of 54% compared with Explososcan. Compared with a previously introduced row-column method, the FORCES imaging scheme had similar resolution but a 25-dB decrease in sidelobe amplitude, significantly impacting contrast to noise in scattering phantoms.
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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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".