Experimental verification of a split-aperture transmit beamforming technique for suppressing grating lobes in large pitch phased arrays
Bibliographic record
Abstract
Phased array ultrasound transducers are desirable in some applications where large field-of-view is required from a small aperture. Fabrication of high-frequency phased array transducers is challenging due to the inter-element pitch constraint (~0.5 λ) to avoid large grating lobe artifacts. Phase coherence imaging (PCI) has been introduced as a method for suppressing grating lobes in large-pitch arrays, however, it is not as effective when the grating lobe echoes have large overlap in the time-domain. In previous work we suggested a technique called split-aperture transmit beamforming to increase the effectiveness of PCI in grating lobe suppression of these arrays and used computer simulation to evaluate its effectiveness. In the present work we report on the experimental verification of the technique using a commercially available high-frequency ultrasound linear array system (Vevo 2100, Visualsonics). We demonstrate that by using only two split-aperture transmit beamforming events along with PCI we can effectively suppress the grating lobes resulting from a 50 MHz, 64-element, 1.26 λ pitch phased array to less than 60 db for a wire-phantom placed at 25 degrees from the center of transducer. We further show that this grating lobe suppression greatly improves the contrast in tissue phantom. Finally, we show that the measured beamformed radiation patterns are consistent with simulations.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".