High-Frame-Rate Synthetic Aperture Ultrasound Imaging Using Mismatched Coded Excitation Waveform Engineering: A Feasibility Study
Bibliographic record
Abstract
Mismatched coded excitation (CE) can be employed to increase the frame rate of synthetic aperture ultrasound imaging. The high autocorrelation and low cross correlation (CC) of transmitted signals enables the identification and separation of signal sources at the receiver. Thus, the method provides B-mode imaging with simultaneous transmission from several elements and capability of spatial decoding of the transmitted signals, which makes the imaging process equivalent to consecutive transmissions. Each transmission generates its own image and the combination of all the images results in an image with a high lateral resolution. In this paper, we introduce two different methods for generating multiple mismatched CEs with an identical frequency bandwidth and code length. Therefore, the proposed families of mismatched CEs are able to generate similar resolutions and signal-to-noise ratios. The application of these methods is demonstrated experimentally. Furthermore, several techniques are suggested that can be used to reduce the CC between the mismatched codes.
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 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 | 0.000 |
| 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".