Constrained hyperelastic parameters reconstruction of PVA (Polyvinyl Alcohol) phantom undergoing large deformation
Bibliographic record
Abstract
The nonlinear mechanical behavior of tissues that undergo large deformations, e.g. the breast, is characterized by hyperelastic parameters. These parameters take into account both types of nonlinearities: tissue intrinsic nonlinearity and geometric nonlinearity. Elastography technique capable of tissue hyperelastic parameter reconstruction has important clinical applications such as cancer diagnosis and interventional procedure planning. In this study we report our progress on the development of constrained reconstruction technique of breast tissue hyperelastic parameters [1]. The extension of this work is twofold: the inclusion of the popular Veronda-Westmann hyperelastic model and using a novel technique for tissue displacement tracking. This tracking technique is based on the Horn-Schunck optical flow method [2]. The objective of this paper is to validate the numerical analysis performed in [1] by phantom experiment. For this purpose, a PVA (Polyvinyl Alcohol) phantom that consists of three tissue types was constructed and tested. PVA exhibits nonlinear mechanical behavior and has been recently used for tissue mimicking purposes. Reconstruction results showed that it is feasible to find the relative hyperelastic parameters of the tissue with acceptable accuracy. The error reported for the relative parameter reconstruction was less than 20%, which may be sufficient for cancer diagnosis purposes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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 teacher head, 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".