Methods for the estimation of sub-sample motion of digitized ultrasound echo signals in two dimensions
Bibliographic record
Abstract
Motion estimation in sequences of ultrasound echo signals is essential for a wide range of modern ultrasound-based signal processing applications. Pattern matching algorithms are generally used for finding the motion within sampling accuracy. Subsequently, 1D interpolation techniques are employed to find any motion smaller than sampling accuracy. In this paper, we propose several interpolation schemes that are suited for 2D motion estimation. Simulations using Field II ultrasound simulation software and experiments using an Ultrasonix Sonix RP imaging system with a L9-4/38 linear array transducer are used to study these techniques and compare their performance to other methods reported in the literature. The results show that the proposed interpolation methods outperform other common techniques in terms of bias and jitter. The axial and the lateral bias of the proposed methods are measured to be less than 1 microm and 5 microm, respectively, in both the simulated and the experimental data.
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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 | 0.002 |
| 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 | 0.000 | 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".