Bibliographic record
Abstract
Abstract Ultrasound images differ from their optical counterpart in that they map the local acoustic properties of the medium such as the density and compressibility as opposed to the optical properties. They are subject to distortion from a variety of sources, including diffraction, attenuation, dispersion, and inhomogeneities in the medium. Imaging arrays enable acoustic images to be obtained without the need for mechanical scanning of single-element transducers, and they can do so at a sufficiently high frame rate that avoids significant motion distortion caused by fast-moving structures such as those in the heart. Developments in transducers and array design, together with new signal-processing techniques, have enabled major improvements to be made in ultrasound contrast, resolution, dynamic range, frame rate, and signal-to-noise ratios (SNR). This chapter is the first of two concerned with the theory, design, and application of arrays for 2-D and 3-D imaging. It begins with a brief historical overview that describes how ultrasound imaging evolved from pulse-echo metal flaw detectors and was influenced by the techniques developed for radar during World War II. Simple CW excited 1-D arrays consisting of point source elements will first be examined. This leads to the concepts of beam focusing and steering using pulse-excited array elements of finite size.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.029 | 0.016 |
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".