3D Global Time-Delay Estimation for Shear-Wave Absolute Vibro-Elastography of the Placenta
Bibliographic record
Abstract
The placenta is a vital organ for growth and development of the fetus. Shear Wave Absolute Vibro-Elastography (SWAVE) is a new elastography technique proposed to detect placenta disorders. Elastography involves applying a force on the tissue and measuring the resulting tissue deformation. All types of compression cause the tissue to expand in three directions given the biological tissues are nearly incompressible. Hence, 3D displacement estimation should lead to the most accurate elasticity reconstruction compared to the traditional 1D methods. Previous studies estimated 3D displacements over ultrasound volumes mostly for quasi-static compression to generate strain images. However, accurate displacement tracking of dynamic motion continues to be a challenge. In this work, a novel volumetric regularized algorithm, 3D GLobal Ultrasound Elastography (GLUE3D), is presented to estimate the 3D displacement over a volume of ultrasound data, following by a 3D Young's modulus reconstruction. The proposed method outperforms the previous 2D method over a volume and is compared with a 3D technique using phantom data for which the elasticity are provided by the values from magnetic resonance elastography on the same phantom and also the manufacturer reference numbers. We then present Young's modulus reconstruction results obtained from clinical data of placenta which shows more uniform elasticity maps compared to the traditional 1D displacement measurements over a volume of ultrasound data. Furthermore, the dependency of the elasticity values to the frequency is investigated in this study.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".