{"id":"W3094595091","doi":"10.1080/21681163.2020.1835541","title":"The application and optimization of super-resolution reconstruction for isotropic out-of-plane MRI to study the musculoskeletal system","year":2020,"lang":"en","type":"article","venue":"Computer Methods in Biomechanics and Biomedical Engineering Imaging & Visualization","topic":"Advanced MRI Techniques and Applications","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Alberta Bone and Joint Health Institute; University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; Arthritis Society","keywords":"Isotropy; Magnetic resonance imaging; Computer vision; Computer science; Artificial intelligence; Segmentation; Iterative reconstruction; Visualization; Plane (geometry); CAD; Image resolution; Real-time MRI; Biomedical engineering; Medicine; Radiology; Physics; Mathematics; Optics; Geometry; Engineering","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005165046,0.00009863357,0.0001896704,0.0001065492,0.00007628011,0.00001507695,0.00007254717,0.000045489,3.166578e-7],"category_scores_gemma":[0.00008187683,0.00006851185,0.0000277992,0.0004087733,0.00004552533,0.0000376134,0.00006542593,0.00006373513,5.061367e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000283503,"about_ca_system_score_gemma":0.00001220635,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005980403,"about_ca_topic_score_gemma":2.671e-7,"domain_scores_codex":[0.9991335,0.00005408015,0.0003741021,0.0002172721,0.0001151577,0.00010584],"domain_scores_gemma":[0.9994398,0.0001531767,0.000104853,0.0001332931,0.0001005433,0.0000683375],"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":[0.0000775723,0.0001462419,0.0001484944,0.0005919692,0.00004220735,5.118293e-7,0.0009395056,0.01529496,0.1082791,0.01450437,0.00003216853,0.859943],"study_design_scores_gemma":[0.0004277385,0.0003003298,0.0001153723,0.00007160362,0.00004539756,0.000008068239,0.0001888925,0.9951187,0.002072906,0.00006692686,0.001518675,0.0000653225],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0039433,0.0001333277,0.9936216,0.0009405984,0.0001325584,0.001159249,0.000006593333,0.00006222619,4.990008e-7],"genre_scores_gemma":[0.1587796,0.00009685834,0.8408059,0.00004660228,0.00009874774,0.0001238822,0.00003242691,0.00001549568,4.690725e-7],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9798238,"threshold_uncertainty_score":0.2793832,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02123595995463338,"score_gpt":0.3661688388009876,"score_spread":0.3449328788463542,"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."}}