Estimation of a dense velocity field based on the statistics of dynamic speckle
Bibliographic record
Abstract
Abstract This article presents a new technique for flow velocity estimation from ultrasound image sequences. The method is based on the analysis of the temporal statistics of the speckle pattern in motion. We demonstrate that the biased local temporal variance (LTV) of a single pixel within an image of dynamic speckle is related to velocity. This allows us to estimate the total velocity magnitude without the requirement of neither block matching nor autocovariance estimation. A new estimator, asymptotically without bias, called LTV is presented. Results conducted on experimental B mode sequences (40 MHz) of blood mimicking fluid with calibrated velocities are presented. Performances of the estimator are studied and results show good agreement with the statistical model. Magnitude of the 3D velocity vector in the range of 0.1–2 mm/s have been estimated with a standard deviation error of less than 12%. The validity of the method and its limitations are then discussed. © 2010 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 20, 268‐276, 2010
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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 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".