A global strain estimation algorithm for non-invasive vascular ultrasound elastography
Bibliographic record
Abstract
Most strain estimation algorithms are window-based. Within a calculation window, cross-correlation or affine estimations are performed. However, there is always a trade-off between the window size, overlap and computation efficiency. We propose a parameterized affine model to estimate pixel-wise strain globally within the framework of the Horn-Schunck optical flow (OF) estimation, which enables to derive a global strain field efficiently without multiple windowed calculations. In addition, properties of the global pixel-wise estimation provide higher strain imaging resolution. Specifically, global strain fields were parameterized with discrete cosine transform (DCT) descriptions. A cost function including an OF constancy term, a smoothness constrain and a nearly incompressibility term was minimized to derive affine strain components (axial and lateral strains and shears), from which principal strains were determined. For the simulation study, the proposed method provided less estimation errors than the window-based Lagrangian speckle model estimator (LSME). The computation time with the proposed method was also reduced by more than 4 times compared with the LSME. For in vitro experiments, the proposed method was found to be able to detect a 1 mm hard inclusion.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".