Efficient Frequency-Domain Synthetic Aperture Focusing Techniques for Imaging With a High-Frequency Single-Element Focused Transducer
Bibliographic record
Abstract
Synthetic aperture focusing techniques (SAFT) make the spatial resolution of the conventional ultrasound imaging from a single-element focused transducer more uniform in the lateral direction. In this work, two new frequency-domain (FD-SAFT) algorithms are proposed, which are based on the synthetic aperture radar's wavenumber algorithm, and 2-D matched filtering technique for the image reconstruction. The first algorithm is the FD-SAFT virtual source (FD-SAFT-VS) that treats the focus of a focused transducer as a virtual source having a finite size and the diffraction effect in the far-field is taken into consideration in the image reconstruction. The second algorithm is the FD-SAFT deconvolution (FD-SAFT-DE) that uses the simulated point spread function of the imaging system as a matched filter kernel in the image reconstruction. The performance of the proposed algorithms was studied using a series of simulations and experiments, and it was compared with the conventional B-mode and time-domain SAFT (TD-SAFT) imaging techniques. The image quality was analyzed in terms of spatial resolution, sidelobe level, signal-to-noise ratio (SNR), contrast resolution, contrast-to-speckle ratio, and ex vivo image quality. The results showed that the FD-SAFT-VS had the smallest spatial resolution and FD-SAFT-DE had the second smallest spatial resolution. In addition, FD-SAFT-DE had generally the largest SNR. The computation run time of FD-SAFT-VS and FD-SAFT-DE, depending on the image size, was lower by 4 to 174 times and 4 to 189 times, respectively, compared to the TD-SAFT-virtual point source.
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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".