Experimental verification of pulse-probing technique for improving phase coherence grating lobe suppression
Bibliographic record
Abstract
Fabrication of high-frequency phased-array ultrasound transducers is challenging because of the small element- to-element pitch required to avoid large grating lobes appearing in the field-of-view. Phase coherence imaging (PCI) was recently proposed as a highly effective technique to suppress grating lobes in large-pitch arrays for synthetic aperture beamforming. Our previous work proposed and theoretically validated a technique called pulse probing for improving grating lobe suppression when transmit beamforming is used with PCI. The present work reports the experimental verification of the proposed technique, in which the data was collected using a high-frequency ultrasound system and the processing was done offline. The data was collected with a 50-MHz, 256-element, 1.26 λ-pitch linear array, for which only the central 64-elements were used as the full aperture while the beam was steered to various angles. By sending a defocused pulse, the PCI weighting factors could be calculated, and were subsequently applied to the conventional transmit-receive beamforming. The experimental two-way radiation patterns showed that the grating lobe level was suppressed approximately 40 dB using the proposed technique, consistent with the theory. The suppression of overlapping grating lobes in reconstructed phased array images from multiple wire-phantoms in a water bath and tissue phantoms further validated the effectiveness of the proposed technique. The application of pulse probing along with PCI should simplify the fabrication of large-pitch phased arrays at high frequencies.
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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) | 0.000 | 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 | 0.000 |
| 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".