Hybrid adaptive/nonadaptive beamforming for ultrasound imaging
Bibliographic record
Abstract
We propose and evaluate a simple yet effective technique of combining nonadaptive and adaptive beamforming methods aimed to achieve high quality of ultrasound images at low computational cost. Our hybrid beamformer automatically switches between nonadaptive and adaptive beamforming of input data vectors, based on the outcome of the comparison of the input coherence factor against a certain threshold. For illustrative purposes, we used the delay-and-sum (DAS) beamformer as an example of a nonadaptive method, while the Generalized Sidelobe Canceller (GSC) and Adaptive Single Snapshot Beamformer (ASSB) served as two examples of an adaptive method. We have applied our technique to simulated ultrasound images of a 12-point phantom and a point-scattering-cyst phantom, demonstrating substantial computational savings without a significant degradation in the image resolution and contrast, in comparison to the standard GSC-based or ASSB-based beamforming methods.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".