Regularized tracking of shear-wave in ultrasound elastography
Bibliographic record
Abstract
Ultrasound elastography involves imaging tissue while it undergoes deformation and inferring its mechanical properties from the deformation pattern. The initial deformation in the tissue is typically induced through an external mechanical force, for example, by exerting a slight pressure using an ultrasound probe or by applying an acoustic radiation force (ARF) against the tissue. The ARF excites the tissue locally, which leads to the propagation of a shear-wave. The goal of the shear-wave elastography is to estimate the speed of the shear-wave that is explicitly related to the elasticity of tissue. We formulate tissue displacement estimation as an optimization problem and propose a computationally efficient approach to estimate the displacement field. A novel algorithm based on the minimization of a regularized cost function using higher-order analytical minimization (HAM) coupled with the second-order Taylor series approximation is proposed. Our algorithm first computes an integer displacement field based on dynamic programming (DP) that provides the global optima, which is then refined iteratively to obtain the subpixel displacement estimate. We test the proposed algorithm on real experimental data obtained from a tissue-mimicking phantom and illustrate the superiority of our approach over some commonly used elastography techniques using signal to noise ratio (SNR) comparisons.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".