Despeckling Ultrasound Images with Decorrelated Compounding in the Spatial Frequency Domain
Bibliographic record
Abstract
Speckle variance in ultrasound images limits the detection of low-contrast targets. Recently, a decorrelated compounding method in synthetic transmit aperture ultrasound imaging was proposed to reduce speckle variance. This paper extended the decorrelated compounding method beyond synthetic transmit aperture ultrasound imaging to any radio-frequency ultrasound images. A spatial-frequency-domain compounding method is proposed here by dividing the spectrum of ultrasound images into overlapped sub-domains to produce multiple sub-images for decorrelation and compounding. The image quality metrics, such as speckle signal-noise-ratio, contrast, contrast-noise-ratio, spatial resolutions, and lesion signal-noise-ratio, were calculated to compare the proposed method and the delay-and-sum method. The proposed method improved the contrast-noise ratio over the delay-and-sum method at the cost of spatial resolution loss. Overall, the proposed method improved lesion detectability in terms of lesion signal-noise-ratio by more than 57\% in numerical simulations and experimental studies. The proportion correct of detecting simulated low-contrast (-1 dB) lesions increased from the 67\% in the the delay-and-sum method to 95\% in the proposed method in the two-alternative forced-choice studies on human observers. Structures in the {\it in vivo} clinical image were identified more easily in the proposed method than in the delay-and-sum method.
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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 | 0.000 | 0.002 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".