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Record W2074618508 · doi:10.1118/1.3218761

Sliding characteristic and material compressibility of human lung: Parametric study and verification

2009· article· en· W2074618508 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueMedical Physics · 2009
Typearticle
Languageen
FieldEngineering
TopicSoft Robotics and Applications
Canadian institutionsPrincess Margaret Cancer Centre
FundersNational Cancer Institute
KeywordsCompressibilityDisplacement (psychology)Parametric statisticsImage registrationPoisson distributionFinite element methodMaterials scienceMathematicsBiomedical engineeringMechanicsMathematical analysisPhysicsStructural engineeringMedicineImage (mathematics)StatisticsComputer scienceEngineering

Abstract

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PURPOSE: To find and verify the optimum sliding characteristics and material compressibility that provide the minimum error in deformable image registration of the lungs. METHODS: A deformable image registration study has been conducted on a total of 16 lung cancer patients. Patient specific three dimensional finite element models have been developed to model left and right lungs, chest (body), and tumor based on 4D CT images. Contact surfaces have been applied to lung-chest cavity interfaces. Experimental test data are used to model nonlinear material properties of lungs. A parametric study is carried out on seven patients, 20 conditions for each, to investigate the sliding behavior and the tissue compressibility of lungs. Three values of coefficient of friction of 0, 0.1, and 0.2 are investigated to model lubrication and sliding restriction on the lung-chest cavity interface. The effect of material compressibility of lungs is studied using Poisson's ratios of 0.35, 0.4, 0.45, and 0.499. The model accuracy is examined by calculating the difference between the image-based displacement of bronchial bifurcation points identified in the lung images and the calculated corresponding model-based displacement. Furthermore, additional bifurcation points around the tumor and its center of mass are used to examine the effect of the mentioned parameters on the tumor localization. RESULTS: The frictionless contact model with 0.4 Poisson's ratio provides the smallest residual errors of 1.1 +/- 0.9, 1.5 +/- 1.3, and 2.1 +/- 1.6 mm in the LR, AP, and SI directions, respectively. Similarly, this optimum model provides the most accurate location of the tumor with residual errors of 1.0 +/- 0.6, 0.9 +/- 0.7, and 1.4 +/- 1.0 mm in all three directions. The accuracy of this model is verified on an additional nine patients with average errors of 0.8 +/- 0.7, 1.3 +/- 1.1, and 1.7 +/- 1.6 mm in the LR, AP, and SI directions, respectively. CONCLUSIONS: The optimum biomechanical model with the smallest registration error is when frictionless contact model and 0.4 Poisson's ratio are applied. The overall accuracies of all bifurcation points in all 16 patients including tumor points are 1.0 +/- 0.7, 1.2 +/- 1.0, and 1.7 +/- 1.4 mm in the LR, AP, and SI directions, respectively.

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Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.756
Threshold uncertainty score0.227

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.018
GPT teacher head0.279
Teacher spread0.261 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it