Delay-encoded transmission and image reconstruction method in synthetic transmit aperture imaging
Bibliographic record
Abstract
Synthetic transmit aperture (STA) imaging systems usually have a lower SNR compared with conventional Bmode ultrasound systems because only one or a small number of elements are selected for each transmission in STA. Here we propose delay-encoded synthetic transmit aperture (DE-STA) imaging to encode all the transmission elements to increase the SNR of the pre-beamformed RF signals. The encoding scheme is similar to the Hadamard encoding. However, in each transmission of DE-STA imaging, selected transmitting elements are delayed by a half period of the ultrasound wave relative to the rest transmitting elements, rather than using a pulse inversion as in the Hadamard encoding sequence. After all the transmission events, a decoding process in the temporal frequency domain is applied to the acquired RF signals to recover the equivalent traditional STA signals with a better SNR. The proposed protocol is tested with simulated data (using Field II) and experimental data acquired with a commercial linear array imaging system (Ultrasonix RP). The results from both the simulations and the experiments demonstrate increased SNR of pre-beamformed RF signals and improved image quality in terms of peak signal-to-noise ratio (PSNR), resolution and contrast-to-noise ratio compared with traditional STA. The lateral resolution (as assessed by a wire target) of DESTA imaging is improved by 28% and the PSNR of the wire is increased by 7 dB, respectively, compared with traditional STA imaging. The proposed image reconstruction framework can also be extended to other transmission protocols.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".