1K-1 A New Eigen-Based Clutter Filter Using the Hankel-SVD Approach
Bibliographic record
Abstract
In color flow data processing, the eigen-regression filter has shown potential in suppressing slow-time clutter while preserving blood echoes because of its adaptability to the Doppler signal contents. However, this filter is inherently based on the use of multiple slow-time snapshots that are statistically stationary. In this article, we present a new eigen-based clutter filter called the Hankel-SVD filter that does not involve the use of multiple slow-time snapshots in its formulation. The new filter, which is derived using the notion of principal Hankel component analysis, works by exploiting the eigen-space properties of a matrix form known as the Hankel matrix. To assess its efficacy, the Hankel-SVD filter was applied to synthesized slow-time data with arterial flow parameters and low-velocity flow parameters as well as in vivo color flow imaging data obtained from the carotid arteries of a healthy youth. It was found that the new filter generally has better flow detection performance than the clutter-downmixing filter and a fixed-rank multi-snapshot-based eigen-filter
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".