Reducing the number of receiving channels using transmit-receive symmetry in synthetic transmit aperture imaging
Bibliographic record
Abstract
Synthetic transmit aperture (STA) imaging has been widely studied in ultrasound imaging. Usually the number of receiving channels is the same as the number of the array elements (N). When the number of receiving channels is large, such as the matrix array for 3D imaging, the system cost will be high due to the receiving electronics for each element. Therefore, it is desirable to reduce the number of receiving channels while keeping a large number of transmit channels. In this paper, we studied with Field II Hadamard-encoded synthetic transmit aperture imaging systems with about N/2, N/4 and N/8 receiving channels. There were N Hadamard-encoded transmission events for one frame of image. The pseudoinverse was applied to the acquired RF data to estimate the equivalent signal in the traditional STA. We found that applying the transmit-receive symmetry of RF signals can reduce the receiving channels by half without compromising image quality. We also compared different methods to encode the receivers when the receiving channels were reduced to about N/4 and N/8.
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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 | 0.003 |
| 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 | 0.001 |
| 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".