Biaxial Excitation and Reception Method Applied to Ultrasound Imaging
Bibliographic record
Abstract
The biaxial method consists of the utilization of orthogonal electric fields in single-element piezoceramics both in transmission and reception. This study demonstrates the application of the biaxial method to broadband transducers. We developed a three-element biaxial transducer array to demonstrate the feasibility of biaxial method in imaging applications. Finite element analysis was used to model the response of a single transducer element. An electric characterization was performed at each transducer element to determine their driving frequency. Each transducer was driven at 6.25 MHz and tested in different phases to determine the phase that produced the maximum pressure amplitude and shortest pulsewidth. Both simulations and experimental results showed that the acoustic pressure and half-pulsewidth followed a sinusoidal response as a function of the difference in phase applied to the lateral electrodes, as it has been described in our previous work. An imaging test was performed by placing a 0.36-mm diameter nylon wire 20 mm away from the transducer while driving and receiving each element with different combinations of conventional and biaxial driving. By applying a biaxial rephasing at the receiving electrodes during the data analysis, we obtained a maximum reduction in the axial resolution from 4.6 to 1.3 mm and signal-to-noise ratio (SNR) improvements from 15.2 to 24.4 dB, when compared to conventional driving.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".