Two-pass beamforming for ultrasound imaging
Bibliographic record
Abstract
We propose a simple two-pass beamforming scheme that combines adaptive and nonadaptive methods to achieve high-quality ultrasound images at low computational cost. During the first pass, our beamformer processes buffered input vectors using an inexpensive nonadaptive method and computes the corresponding envelope. Based on that envelope information, selected outputs may be recomputed during the second pass (to improve beamforming performance) using an expensive adaptive method. The purpose of the first pass is to identify which nonadaptively-beamformed outputs can be spared from a heavy computational load of adaptive beamforming taking place during the second pass. In t his work, our beamforming scheme combines the nonadaptive delay-and-sum (DAS) beamformer and the adaptive Generalized Sidelobe Canceller (GSC), and it selects outputs for recomputation based on a threshold window. We have evaluated our scheme using simulated ultrasound images of a 12-point phantom and a point-scatterer-cyst phantom, achieving substantial threshold-dependent computational savings while maintaining acceptable image resolution and contrast, compared to pure GSC beamforming.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".