Tensor-based off-axis noise suppression in pre-beamformed data to enhance the visualization of hyper echoic structures
Bibliographic record
Abstract
Visualizing hyper-echoic structures, such as bones, in ultrasound imaging is challenging due to strong off-axis energy and many other factors arising from complex interactions between the ultrasound beam and the structures. Most previous research has focused on suppressing off-axis artifacts but is generally limited by factors such as high computation requirements, limited improvements, or ultrasound hardware modifications. This paper describes a new tensor based directional noise suppression method to suppress the off-axis energy. The pre-beamformed channel data are first delay compensated so signals of interest are aligned horizontally while off-axis distractions are aligned in different angles. Then we perform tensor analysis on the aligned channel data to identify regions with strong gradients vertically. Directional high pass filtering is then applied based on tensor analysed results so both weak horizontal lines and strong off-axis non-horizontal structures are suppressed. In a point phantom study, the contrast ratio (CR) is improved from 0.71 to 0.85. In a bone phantom study, the CR is improved from 0.60 to 0.87.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".