S-sequence spatially-encoded synthetic aperture ultrasound imaging [Correspondence]
Bibliographic record
Abstract
Synthetic transmit aperture (STA) ultrasound imaging offers near-ideal reconstruction across an entire field of view. This performance comes at the cost of SNR compared with scanning using only dynamic receive focusing. SNR may be enhanced by using spatial encoding using a Hadamard sequence. An encoding based on a Hadamard sequence has two main drawbacks: the array must be capable of transmitting a pulse and an inverted pulse at the same time, and the inverted transmission must be symmetrical with respect to the non-inverted transmission. These are often not the case in practice, and thus Hadamard encoding may require twice as many transmission events and special consideration of the inverted waveform. As an alternative, we propose the use of S-sequences, which are similar to Hadamard sequences, but use half the elements and do not require an inverted pulse. This encoding is implemented on a commercial ultrasound system and compared with STA imaging using single-element emissions and Hadamard encoding in terms of SNR and resolution using a point target. We find that the two encodings perform very similarly despite the increased transmit power and doubling of transmit events in our implementation of Hadamard imaging. Both encodings give up to 19 dB signal improvement over single-element STA imaging, while maintaining resolution. Finally, we show sample in vivo human carotid images with all three methods which illustrate the suitability of S-sequence- encoded STA imaging for a clinical setting.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".