Large-pitch Synthetic Transmit Aperture Imaging with micro-beamforming technique
Bibliographic record
Abstract
We propose to combine synthetic transmit aperture imaging with the micro-beamforming technique (MB-STA) to improve the image quality of the large-pitch synthetic transmit aperture imaging (LPSTA). In LPSTA, nearby array elements are combined as sub-aperture (SAP) without pre-steering delays in both transmit and receive to reduce hardware complexity. In MB-STA , pre-steering delays or microbeamforming within the transmit and receive sub-aperture is introduced to steer the transmission and receiving beams to a focal point. Focusing for the points away from the focal point is then achieved through the image reconstruction using the micro-beamformed RF data in synthetic transmit aperture imaging. Spatial response functions (SRF) are applied in the image reconstruction to compensate for the contrast loss, especially when the targets are located outside the focal region. The simulation results demonstrated that MB-STA can attain an image quality comparable to the standard STA of a fullyindexed array, but with a 66-fold reduction in the number of measurement channels, at the cost of a smaller field of view. Moreover, our experimental results demonstrated that for targets located outside of the focal region, the proposed MBSTA method has better image quality than that of the conventional B-mode imaging with a similar microbeamforming configuration. The proposed method can be extended to 3D ultrasound imaging to reduce its excessive hardware complexity and cost.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".