{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007763796,0.000177186,0.0001956451,0.0002000463,0.0005027685,0.00004792318,0.00009584775,0.00009189265,0.0001265441],"category_scores_gemma":[0.00009167036,0.0001314437,0.0001953715,0.00031669,0.0002568965,0.0003836944,0.000001336922,0.0005703679,0.00001065366],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001150376,"about_ca_system_score_gemma":0.0001519619,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002067633,"about_ca_topic_score_gemma":0.00001993446,"domain_scores_codex":[0.9983629,0.00007036702,0.0003170535,0.0002366291,0.0005565736,0.0004564973],"domain_scores_gemma":[0.9989833,0.0002961174,0.00009437976,0.0001939227,0.000114827,0.0003173998],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003550595,0.0006942762,0.003669282,0.0003799425,0.0002453593,0.000003090297,0.004108553,0.0020992,0.04331805,0.0001142941,0.0004382945,0.9445746],"study_design_scores_gemma":[0.003826993,0.0001728751,0.0005534806,0.0007184911,0.00136244,0.001954206,0.00863328,0.965009,0.0127199,0.0002314376,0.004283983,0.0005339505],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0868419,0.00008032472,0.908886,0.00203676,0.001428755,0.0004339714,0.000007838555,0.0001419513,0.0001425487],"genre_scores_gemma":[0.9591325,0.00002707035,0.03927735,0.000972285,0.0004312627,0.00006618602,0.000004323442,0.00003414003,0.00005489106],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9629098,"threshold_uncertainty_score":0.5360119,"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."}}