Statistical analysis of decorrelation‐based transducer tracking for three‐dimensional ultrasound
Bibliographic record
Abstract
The use of speckle decorrelation techniques to calculate the displacement of a moving transducer has shown promise. We describe a technique to estimate displacement between pairs of parallel planes without assuming that plane separation in the scan is uniform. We perform theoretical and empirical analyses of the bias and uncertainty in plane spacing estimates as a function of speckle size, patch size, and the number of planes used for normalization. Practically, only the central, linear region of the autocovariance curves can be used in this decorrelation method, which implies that distance between acquired image planes should be approximately half the speckle size. In this region, the uncertainty in estimated plane spacing was less than 15% for a 8.1 mm (axial) by 9.1 mm (lateral) patch and increased to 33% for an 8.1 mm (axial) by 1.5 mm (lateral) patch. The number of planes, Nz, used to calculate the normalization factors (averages of brightness and- squared brightness) was a major source of bias. Optimum Nz was found to be five to ten planes, depending on distance between acquired image planes, with a poor choice of Nz resulting in a bias of 10% or greater. A second source of bias is brightness gradients which, although they appear very slight on intensity images, can cause a large bias is the plane spacing estimates made using linearized data.
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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 | 0.007 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".