Decorrelated Compounding in Synthetic Transmit Aperture (STA) Ultrasound Imaging to detect low-contrast lesions
Bibliographic record
Abstract
Speckles are ubiquitous in ultrasound images and have been used to track motion and characterize tissues in biomedical applications successfully. However, low signal-to-noise-ratio in ultrasound images due to speckles still severely limits the detection of low-contrast targets. Conventional Spatial Compounding (SC) or Frequency Compounding (SC) was used to reduce the speckles in ultrasonic imaging with limited success, usually at the cost of frame rate. The goal of this paper is to improve the performance of compounding in detecting low-contrast targets. We first generated a large number of correlated sub-images by using various sub-apertures and sub-bands in Synthetic Transmit Aperture (STA) ultrasound imaging. Then we decorrelated and normalized the sub-images by applying Singular Value Decomposition (SVD) to the sub-images. Lastly, the decorrelated sub-images were incoherently compounded to yield the de-speckled images. The speckle variance and contrast of targets in the reconstructed images were quantified. The Contrast-Noise-Ratio (CNR) improvement in simulation images using the proposed method is 1842% over the Delay-and-Sum (DAS) method. The feasibility of applying the proposed method to image a lesion created by high-intensity focused ultrasound was also demonstrated.
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) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".