Effect of heterogeneous material of the lung on deformable image registration
Bibliographic record
Abstract
Patient specific 3D finite element models have been developed to investigate the effect of heterogeneous material properties on modeling of the deformation of the lungs by including the bronchial trees of each lung. Each model consists of both lungs, body, tumor, and bronchial trees. Triangular shell elements with 0.1 cm wall thickness are used to model the bronchial trees. Body, lungs and tumor are modeled using 4-node tetrahedral elements. Experimental test data are used for the nonlinear material properties of the lungs. Three elastic modulii of 0.5, 10 and 18 MPa are used for the bronchial tree. Frictionless contact surfaces are applied to lung surfaces and cavities. The accuracy of the results is examined using an average of 40 bifurcation points. Preliminary results have shown an insignificant effect of modeling the bronchial trees explicitly on the overall accuracy of the model. However, local changes in the predicted motion of the bronchial tree of up to 5.2 mm were observed, indicating that modeling the bronchial tree explicitly, with unique material properties, may ensure a more accurately detailed model of the lung as well as reduced maximum residual errors.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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".