A Fast Constrained Nonlinear Elastography Technique: Polyvinyl Alcohol (PVA) Phantom Study Using the Veronda-Westman Model
Bibliographic record
Abstract
Breast elastography has been proposed as a novel imaging modality for breast cancer detection and assessment. As pathologies are known to change tissue stiffness significantly, the idea behind elastography is using tissue stiffness as imaging contrast agent. Evidence in the literature suggests that various pathological tissues exhibit different mechanical stiffness characteristics. Therefore, in addition to the ability of detecting the presence of abnormalities, elastography is capable of pathological tissue classification. In this work, we propose a novel nonlinear (hyperelastic) breast elastography system which takes into account tissue large deformations resulting from mechanical stimulation. To idealize breast tissue, we use the well-known Veronda-Westman model as the forward problem solution in the hyperelastic parameter reconstruction process. This process involves tissue mechanical stimulation, displacement data acquisition followed by solving an inverse problem to find the hyperelastic parameters iteratively. These parameters are useful for in vivo tumor classification, image guided surgery and Virtual Reality systems development. Due to the exponential form of the Veronda-Westman function, however, this model cannot be solved using inverse-matrix techniques. Therefore, we have developed a novel technique to solve the corresponding nonlinear inverse problem. To validate the technique, we used an experimental breast tissue mimicking phantom that was made up of PVA-C (Polyvinyl Alcohol), which exhibits nonlinear mechanical behavior. Displacement data was acquired using a combination of Time Domain Cross-Correlation Estimation (TDE) and Horn-Schunck Optical Flow techniques.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".