Fast Thresholding of SVD Clutter Filter Using the Spatial Similarity Matrix and a Sum-Table Algorithm
Bibliographic record
Abstract
Singular value decomposition (SVD) has become a standard for clutter filtering of ultrafast ultrasound datasets. Its implementation requires the choice of appropriate thresholds to discriminate the singular value subspaces associated with tissue, blood, and noise signals. Comparing the similarity of the spatial singular vectors was shown to be a robust and efficient method to estimate the SVD thresholds. The correlation of the spatial singular vector envelopes gives the spatial similarity matrix (SSM), which usually exhibits two square-like domains juxtaposed along the diagonal of the SSM, representing the tissue and the blood subspaces. Up to now, the proposed methods to automatically segment these two subspaces on the SSM were of high computational complexity and had a long processing time. Here, we propose an optimized algorithm using a sum-table approach that decreases the complexity by two orders of magnitude:$O(n^{4}) to O(n^{2})$. The proposed method resulted in processing times lower than 0.08 s for datasets of 2000 frames, whereas previous algorithms took more than 26 h, so an improvement by a factor of 106. We illustrated this adaptive square-fitting on the SSM in the in vivo case of human neonate brain imaging and carotid imaging with various conditions of clutter. This optimization of SVD thresholding is essential to develop the use of adaptive clutter filtering, especially for real-time applications or block-wise processing.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".