Large-Pitch Synthetic Transmit Aperture Imaging: A Feasibility Study
Bibliographic record
Abstract
A 3-D or large-aperture 2-D synthetic transmit aperture (STA) ultrasound imaging system with a fully sampled array usually leads to high hardware complexity and cost since each element in the array is individually controlled.To reduce the hardware complexity,we propose a large-pitch method for STA (LPSTA) imaging integrated with a spatial response function (SRF) in the image reconstruction to improve image quality. To achieve this, we decreased the total number of measurement channels M (the product of the number of transmissions IT and the number of the receive channels in each transmission IR). We combined L adjacent elements in transmission and Kadjacent elements in receive into subapertures (SAPs), where L and K were coprime (no common factors) integers to suppress the grating lobes. We denoted it as an (N/L, N/K) system, where N is the number of transducer elements. In this article, first, we derived the beam pattern of the LPSTA using a far-field approximation. We demonstrated that the coprime selection and SRF can significantly reduce the grating lobes level (GLL) by using a beam pattern analysis. We also found that the LPSTA can have a similar beam pattern as that of a full array when the target is located along the steering direction. Second, the imaging performance of LPSTA was evaluated and validated with Field II simulations and experiments. The simulation results demonstrated that the proposed LPSTA with (N/3, N/5) can achieve on average ~25% improvement in lateral resolution, ~24.6% and ~42.3% improvement in contrast-to-noise ratio (CNR) and contrast ratio (CR), respectively, over B-mode with a large-pitch receiver with (N, N/5). LPSTA achieved comparable image contrast to the standard STA with the full array (N, N) at the cost of a reduced field of view. The experiment results were consistent with the simulation results. Finally, in addition to reducing the system (hardware) complexity, the LPSTA was more computationally efficient than the standard STA with a full array. The proposed method may help in realizing clinical applications of real-time 2-D or 3-D ultrasound imaging using large arrays.
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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 | 0.001 |
| 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 | 0.001 | 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".