A new algorithm for time-delay estimation in ultrasonic echo signals and a comparative study of its performance evaluation in ultrasound elastography imaging
Bibliographic record
Abstract
Time-delay estimation is a widely used signal processing task in many areas of ultrasound medical imaging and the performance of many applications is highly dependent on the accuracy and efficiency of the time-delay estimates. Time-delay estimation determines the displacement between two ultrasound echo signals. In this thesis, we propose a new time-delay estimation algorithm, which generates a zero-crossing curve to compute the time-delay estimate between two ultrasound echo signals. A comparative study, using statistical analysis and quantitative measurement of image quality in simulated and experimental ultrasound elastography, was done to compare the performance of the proposed algorithm with other established algorithms, such as normalized cross-correlation (NCC) and sum squared differences (SSD). The results of comparison of various algorithms using signal-to-noise and contrast-to-noise ratios indicated that the new algorithm only marginally improved the quality of the images in ultrasound elastography. In addition, a novel strain map normalization method was implemented to enhance target visualization in ultrasound elastography by compensating for strain decay with depth.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".