A novel phase assignment protocol and driving system for a high-density focused ultrasound array
Bibliographic record
Abstract
Currently, most phased-array systems intended for therapy are one-dimensional (1-D) and use between 5 and 200 elements, with a few two-dimensional (2-D) systems using several hundred elements. The move toward lambda/2 interelement spacing, which provides complete 3-D beam steering, would require a large number of closely spaced elements (0.15 mm to 3 mm). A solution to the resulting problem of cost and cable assembly size, which this study examines, is to quantize the phases available at the array input. By connecting elements with similar phases to a single wire, a significant reduction in the number of incoming lines can be achieved while maintaining focusing and beam steering capability. This study has explored the feasibility of such an approach using computer simulations and experiments with a test circuit driving a 100-element linear array. Simulation results demonstrated that adequate focusing can be obtained with only four phase signals without large increases in the grating lobes or the dimensions of the focus. Experiments showed that the method can be implemented in practice, and adequate focusing can be achieved with four phase signals with a reduction of 20% in the peak pressure amplitude squared when compared with the infinite-phase resolution case. Results indicate that the use of this technique would make it possible to drive more than 10,000 elements with 33 input lines. The implementation of this method could have a large impact on ultrasound therapy and diagnostic devices.
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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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".