On the use of denoing algorithms for ultrasound beamforming
Bibliographic record
Abstract
Beamforming in receive, whose objective is to estimate an image from raw RF data acquired by the probe piezoelectric elements, plays a crucial role in ultrasound imaging. The standard method, called delay and sum (DAS) and implemented in most of the commercial scanners, consists in coherently summing the RF signals, providing the backprojection solution of the inverse problem of beamforming. Despite real-time properties, DAS results into images with limited spatial resolution and contrast. The literature of ultrasound beamforming is rich and mainly consists in alternatives to DAS based on non-adaptive or adaptive (e.g., minimum variance, coherence factor) methods or image reconstruction algorithms in the Fourier domain. Furthermore, inverse problem formulations have been shown to be well-adapted to ultrasound beamforming. They consist in minimizing a cost function formed by two terms: a data fidelity term modelling the acquisition setup, and a regularization term. The choice of the latter is not straightforward in ultrasound imaging, mainly because of the need to conserve statistical properties of the speckle. In this paper, denoising algorithms are shown to be good regularizers for ultrasound beamforming, providing a good performance in spatial resolution and contrast gain, without deteriorating the quality of the speckle texture.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".