Po‐Poster ‐ 30: A method to estimate an exponential elastic model
Bibliographic record
Abstract
The response of soft tissue to interventional actions is an important subject in surgery simulation and planning. With properly calibrated elastic properties, it is possible to build a computational model to predict the deformation and stress of different organs. This paper studies the biomechanical properties of a pig liver and proposes a parameter evaluation method specifically for an exponential constitutive model urn:x-wiley:00942405:media:mp1009:mp1009-math-0001 which is extensively used in the literature. Under the assumption of the incompressibility, the uniaxial deformation mode allows us to generate a simplified stress expression: urn:x-wiley:00942405:media:mp1009:mp1009-math-0002 Different from a general trial and error procedure employed when constructing a finite element computational model, here we fit the above simplified stress expression with the experimental curve of stress and strain using a least square method. The fitted result shows that for a sample of a pig liver, b=8.6, C=0.000169 (N/mm2), and A=ln(bC)=−6.53. From the value A we also can deduce Young's module which satisfies E=6bC under a small strain. Consequently we determine that E=6eA=0.0088. We then compare the theoretic model with some published data, and find that the exponential model calibrated with this method matches well with both the Ogden model and an experimental result. Therefore the calibrated parameters could serve as a valuable reference in building a computational physics‐based deformable model for surgical simulation and radiation treatment planning.
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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 | 0.002 | 0.005 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.006 |
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".