{"id":"W4282939263","doi":"10.1109/tmi.2022.3178072","title":"Model-Based Quantitative Elasticity Reconstruction Using ADMM","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Medical Imaging","topic":"Ultrasound Imaging and Elastography","field":"Medicine","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Canadian Institutes of Health Research; Natural Sciences and Engineering Research Council of Canada; University of British Columbia","keywords":"Elasticity (physics); Imaging phantom; Iterative method; Finite element method; Mathematics; Mathematical analysis; Algorithm; Mathematical optimization; Computer science; Physics; Optics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001495388,0.001112676,0.0009553563,0.0007901645,0.0002619367,0.001133257,0.00136389,0.00146977,0.002337367],"category_scores_gemma":[0.002389869,0.0006989171,0.001070525,0.0009164276,0.0005541285,0.001156081,0.001526493,0.001808068,0.0008559117],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005745222,"about_ca_system_score_gemma":0.001112117,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001574073,"about_ca_topic_score_gemma":0.001696004,"domain_scores_codex":[0.9995958,0.0001250082,0.00002369068,0.00007889555,0.0001543881,0.00002213152],"domain_scores_gemma":[0.9992468,0.0003407499,0.00009414092,0.0001245763,0.0001599227,0.00003379942],"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":[0.0000481935,0.00004065737,0.0002519295,0.0001664,0.00007972757,0.0001043561,0.00006662161,0.8550194,0.00759857,0.02161925,0.002730164,0.1122748],"study_design_scores_gemma":[0.00000621869,0.00001134334,0.00002428867,0.000005417364,0.000004749809,0.00003712526,0.000002777814,0.993809,0.0009079648,0.003943279,0.001242237,0.000005515953],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0006201304,0.00006080919,0.9986771,0.00006118428,0.00001342861,0.000009252009,0.00002486176,0.0001542993,0.0003789793],"genre_scores_gemma":[0.06984194,0.0003075719,0.926389,0.0001517924,0.00004423785,0.0001386863,0.0003091095,0.0001738857,0.002643824],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002337367,"threshold_uncertainty_score":0.007908463,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02812488656817153,"score_gpt":0.3025785523287972,"score_spread":0.2744536657606257,"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."}}