Simulation studies of filtered spatial compounding (FSC) and filtered frequency compounding (FFC) in synthetic transmit aperture (STA) imaging
Bibliographic record
Abstract
Speckle patterns are formed by constructive and destructive interference of backscattered waves from non-resolvable scatterers. Speckles can result in a low speckle signal-to-noise ratio (sSNR) in the ultrasound images of even a uniform sample. Speckles also reduce the contrast-to-noise-ratio (CNR) and the detectability of lesions, especially for low contrast lesions. Moreover, undesired signals arising from off-axis targets can result in sidelobes and clutters which lead to even lower lesion CNR. Typically, the speckle SNR can be increased by compounding, either spatial compounding (SC) or frequency compounding (FC). Here we propose methods to implement a 2-dimentional (2-D) aperture domain filter in the SC and FC processes, which are referred to as filtered spatial compounding (FSC) and filtered frequency compounding (FFC), for synthetic transmit aperture (STA) imaging. Both FSC and FFC can provide more homogeneous speckle patterns with improved speckle SNR and lesion CNR. The aperture domain filter reduces the interference effect of the off-axis signals to further enhance lesion CNR. Consequently, the target detectabilities (lesion-signal-to-noise ratio (lSNR)) in both FSC and FFC are increased significantly, up to around 3 times, compared to that in the standard delay-and-sum (DAS) 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".