Delay-encoded transmission in synthetic transmit aperture (DE-STA) imaging
Bibliographic record
Abstract
Synthetic transmit aperture (STA) ultrasound imaging offers dynamic focusing in both transmission and receiving, leading to high image resolution. The major problem of this technique is the low signal to noise ratio (SNR) compared to the conventional B-mode ultrasound method. Hadamard encoded transmission is an approach to overcome this difficulty, but it requires transducer array to transmit a pulse in some array elements and the inverted pulse in the other elements simultaneously, which is not compatible with many commercial scanners. In this paper, we propose delay-encoded synthetic transmit aperture (DE-STA) imaging to encode the transmission elements to increase the SNR of the radiofrequency (RF) echo signals. In this technique, selected transmitting elements are encoded with half period delay and then a decoding process is applied to the acquired RF signals to obtain the equivalent traditional STA signals with a better SNR. The proposed protocol (DE-STA) was tested with both 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 demonstrated improved image quality compared with the traditional STA images with the same amount of measurement noise in the RF data. This SNR improvement in the RF data is comparable with the Hadamard encoded method.
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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 | 0.000 | 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 | 0.000 | 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".