{"id":"W2041813686","doi":"10.1118/1.4905048","title":"Suitability of poroelastic and viscoelastic mechanical models for high and low frequency MR elastography","year":2015,"lang":"en","type":"article","venue":"Medical Physics","topic":"Ultrasound Imaging and Elastography","field":"Medicine","cited_by":84,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Sherbrooke","funders":"National Institute of Biomedical Imaging and Bioengineering; National Institutes of Health","keywords":"Magnetic resonance elastography; Poromechanics; Viscoelasticity; Elastography; Acoustics; Biomedical engineering; Finite element method; Estimator; Materials science; Inverse problem; Repeatability; Nonlinear system; Physics; Nuclear magnetic resonance; Mechanics; Porosity; Mathematical analysis; Mathematics; Ultrasound; Porous medium; Statistics; Engineering; Thermodynamics","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":[0.0008920944,0.0005122747,0.000264122,0.0003504015,0.0001812295,0.0005869634,0.0005450683,0.0009587831,0.000656819],"category_scores_gemma":[0.005079278,0.0002856342,0.0004856523,0.0002043516,0.0006271444,0.0006198505,0.0004517951,0.0005776858,0.0002972823],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002786004,"about_ca_system_score_gemma":0.0004461205,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001139513,"about_ca_topic_score_gemma":0.0009477676,"domain_scores_codex":[0.9995765,0.0001483006,0.00002221165,0.00007856156,0.0001463386,0.00002818727],"domain_scores_gemma":[0.9982237,0.001219703,0.0002026706,0.0001764819,0.0001368418,0.00004061092],"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":[0.0002568844,0.0002623883,0.004014637,0.0002024349,0.00003251037,0.0002791184,0.0001368952,0.8391365,0.1198745,0.005307261,0.0002589197,0.03023805],"study_design_scores_gemma":[0.00001590232,0.0001366202,0.0008681766,0.00001598514,0.000008437658,0.0001053372,0.00001245161,0.9849043,0.01236911,0.0009104693,0.0006385986,0.00001460971],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4512534,0.001086352,0.5412415,0.0004562463,0.00005124067,0.0001879137,0.000211989,0.000322107,0.00518919],"genre_scores_gemma":[0.9465107,0.0005378608,0.05132074,0.00004403634,0.00001793948,0.0001733193,0.0001159935,0.00004784155,0.001231569],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001139513,"threshold_uncertainty_score":0.004717886,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01769356638747585,"score_gpt":0.2593364865285344,"score_spread":0.2416429201410585,"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."}}