Array Transducer Design: A Vibrant Research Theme in Medical Ultrasonics
Bibliographic record
Abstract
Since the 1970s, when portable transducer arrays were first introduced for medical ultrasound imaging, they have undergone substantial technological developments. The development of advanced arrays is often motivated by the need to achieve high diagnostic and therapeutic efficacy and to serve new fields of application. In the past few decades, medical ultrasound array design has been an active research area with challenging technical requirements that continually seek to reduce physical size, improve sensitivity, optimize the number of array elements, realize wide bandwidth, and achieve high output power. The need to devise arrays with increased performance has concurrently stimulated advances in transducer technologies, microelectronics, and array layout design. Nowadays, representative examples can be found for both 2-D and 3-D applications such as high-intensity-focused ultrasound arrays, very-high-frequency or dual-frequency probes, kerf-less arrays, 2-D sparse arrays, and probes with embedded application-specific integrated circuits. The emergence of these advanced arrays has, in turn, stimulated the development of novel, customized transmission and reception approaches, image reconstruction algorithms, and data recovery strategies to exploit or deal with the peculiarities of a specific array.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".