Decorrelated compounding methods in synthetic transmit aperture ultrasound imaging and its application
Bibliographic record
Abstract
Speckles exist in most ultrasound images. They are generated due to the interference of the ultrasound waves scattered from multiple scattering particles in one resolution cell. Although they are very useful in many ultrasound applications, they limit the detection of low-contrast targets in a scattering medium. In conventional compounding, multiple correlated sub-images from various imaging apertures and frequency bandwidths are generated and then averaged incoherently to reduce the speckles. In this paper, we present our study on a method called Decorrelated Compounding to reduce speckles. In decorrelated compounding, a decorrelation procedure was applied to the correlated sub-images to further reduce speckle variance in synthetic transmit aperture (STA) ultrasound imaging. Decorrelated compounding was shown to improve the detectability of low-contrast lesions in terms of lesion signal-to-noise ratio (lSNR), visual detection, and statistical tests of the performance in detecting low-contrast lesions. The application of the proposed method to monitoring ultrasound thermal therapy and to measuring the attenuation coefficient will also be discussed.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".