Transmit beamforming techniques for suppressing grating lobes in large pitch ultrasonic phased arrays
Bibliographic record
Abstract
To date, clinical implementation of high-frequency ultrasound has been limited due to the difficulties in fabricating sufficiently small micro-array transducers. Specifically, if an array is desired with the ability to beam-steer to large angles, an inter-element pitch of approximately .5λ is required to avoid grating lobe artifacts. At high-frequencies (30-70MHz), this introduces major fabrication challenges since the required element pitch is between 10 and 25 microns. A new technique called Phase Coherence Imaging has been introduced in the literature for suppressing grating lobes in large-pitch arrays by calculating a weighting factor proportional to the instantaneous phase coherence of the received element echoes. If the reflected echoes in the grating lobe region are relatively broadband, only some of the echoes will overlap and the resulting weighting factor will be less. Unfortunately, most beamforming techniques result in relatively narrowband echoes in the grating lobe region, making this technique less effective. We have developed a technique that splits the N-element transmit aperture into N/K transmit elements and N receive elements in order to better suppress grating lobes by increasing the bandwidth of the grating lobe echoes. We have also developed a technique that uses a probing pulse from a virtual point source behind the array in order to pre-calculate weighting factors from broadband echoes before conventional transmit beamforming is used. Radiation patterns have been simulated and the amount of grating lobe suppression has been quantified using the proposed techniques. It has been shown that these techniques are very effective in suppressing grating lobes in large-pitch phased-arrays, potentially simplifying high-frequency array fabrication.
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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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".