Frequency-Domain Synthetic Aperture Focusing Techniques for Imaging with Single-Element Focused Transducers
Bibliographic record
Abstract
Synthetic aperture focusing techniques (SAFT) make the lateral spatial resolution of the conventional ultrasound imaging from a single-element focused transducer more uniform. In this work, two new frequency-domain SAFT (FD-SAFT) algorithms are proposed, which are based on 2D matched filtering techniques. The first algorithm is the FD-SAFT virtual disk source (FD-VDS) that treats the focus of a focused transducer as a finite sized virtual source and the diffraction effect in the far-field is accounted for in the image reconstruction. The second algorithm is the FD-SAFT deconvolution (FD-DC) that uses the simulated point spread function of the imaging system as a matched filter kernel in the image reconstruction. These algorithms were implemented for pulsed and linear frequency modulated chirp excitations. The performance of these algorithms was studied using a series of simulations and experiments, and it was compared with the conventional B-mode and time-domain virtual point source SAFT (TD-VPS) 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 tissue image quality. The results showed that the FD-VDS had the highest spatial resolution and FD-DC had the second highest spatial resolution. In addition, FD-DC had generally the highest SNR. The computation run time of the proposed methods was significantly lower than the TD-VPS. Furthermore, chirp excitation improves the SNR of all methods by about 8 dB without significantly affecting the spatial resolution and sidelobe level. Thus, the FD-VDS and FD-DC methods offer efficient solutions to make the spatial resolution of conventional B-mode imaging more uniform.
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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 | 0.001 | 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 | 0.001 | 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".