{"id":"W2079041948","doi":"10.1109/tmi.2012.2228664","title":"Mesh Adaptation for Improving Elasticity Reconstruction Using the FEM Inverse Problem","year":2012,"lang":"en","type":"article","venue":"IEEE Transactions on Medical Imaging","topic":"Ultrasound Imaging and Elastography","field":"Medicine","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Polygon mesh; Finite element method; Imaging phantom; Inverse problem; Iterative reconstruction; Elasticity (physics); Mesh generation; Computer science; Algorithm; Volume mesh; Mathematics; Mathematical optimization; Geometry; Mathematical analysis; Computer vision; Optics; Materials science","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000742975,0.0005250386,0.0002874693,0.0005471829,0.0001782703,0.0003791958,0.0004874048,0.0007056759,0.001400146],"category_scores_gemma":[0.00214382,0.0002703446,0.0005619022,0.0003560209,0.0002808175,0.0004465492,0.0006742741,0.0005494595,0.0004059298],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002246838,"about_ca_system_score_gemma":0.0003227317,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007001248,"about_ca_topic_score_gemma":0.0007338833,"domain_scores_codex":[0.9997014,0.00008576321,0.00001814206,0.00004559565,0.000130282,0.00001885695],"domain_scores_gemma":[0.999542,0.0002289681,0.00004206742,0.0000879575,0.00008854918,0.00001054255],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001458654,0.00008323168,0.00137018,0.000190493,0.00004191195,0.0002684827,0.0002998034,0.5320041,0.241942,0.008476759,0.001102245,0.214075],"study_design_scores_gemma":[0.000004653341,0.00003321195,0.0003082498,0.000009105309,0.00000762132,0.0001082841,0.00001869197,0.9703082,0.02612927,0.0009827486,0.00207993,0.00001003312],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0184215,0.00006991455,0.9801538,0.00003816211,0.0000227078,0.00002598402,0.00001797204,0.0003204597,0.0009294996],"genre_scores_gemma":[0.1837952,0.000165175,0.8144411,0.00004483723,0.00001212798,0.00007011244,0.0001004335,0.0001616342,0.001209481],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001400146,"threshold_uncertainty_score":0.004683971,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02406318079195828,"score_gpt":0.2769370982287413,"score_spread":0.252873917436783,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}