MR validation of soft tissue mimicing phantom deformation as modeled by nonlinear finite element analysis
Bibliographic record
Abstract
A study of the applicability of nonlinear finite element analysis (FEA) to predict soft tissue deformation was validated with phase contrast magnetic resonance velocity imaging. A phantom of varying stiffness was placed in a special purpose, computer controlled MR compatible compression apparatus which provided precise, time varying compression with surface deformations on the order of 11%. The resulting motion was measured with MR velocity images acquired throughout the cycle of compression. The phantom geometry was modeled with a finite element mesh and the mechanical properties of the phantom material were measured and incorporated in the FEA model. The motion as calculated by the FEA model was compared to the motion measured with MRI and the results were found to vary with the material's Poisson's ratio and the coefficient of friction. A minimum difference was reached when the Poisson's ratio and coefficient of friction were set to 0.485 and 0.3, respectively. Under these conditions, the root mean square difference was found to be 14.4%.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".