{"id":"W1968909094","doi":"10.1016/j.mri.2012.02.025","title":"Calibration-Less Multi-coil MR image reconstruction","year":2012,"lang":"en","type":"article","venue":"Magnetic Resonance Imaging","topic":"Advanced MRI Techniques and Applications","field":"Medicine","cited_by":76,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; Qatar National Research Fund","keywords":"Sensitivity (control systems); Calibration; Computer science; Electromagnetic coil; Interpolation (computer graphics); Iterative reconstruction; Artificial intelligence; Scanner; Image (mathematics); Computer vision; Algorithm; Compressed sensing; Position (finance); Least-squares function approximation; Mathematics; Physics","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":[0.0009741761,0.0009803682,0.00068426,0.0006925341,0.0004238751,0.001214969,0.001147382,0.0012576,0.007894882],"category_scores_gemma":[0.003541425,0.0009522566,0.0006164596,0.001065758,0.000359163,0.001459701,0.001703981,0.001254222,0.004245968],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002567199,"about_ca_system_score_gemma":0.001032005,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007721717,"about_ca_topic_score_gemma":0.001744078,"domain_scores_codex":[0.999314,0.0001832973,0.00004079603,0.0001181215,0.0002796699,0.00006418509],"domain_scores_gemma":[0.9986305,0.0002780534,0.000155224,0.0005567061,0.0003259061,0.00005356586],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0009312663,0.0001558991,0.002569813,0.0004894612,0.0002285699,0.0003636288,0.0003063589,0.06490976,0.2252823,0.01378775,0.01279054,0.6781847],"study_design_scores_gemma":[0.00007322477,0.0002458823,0.004343724,0.00007584646,0.0001734743,0.004446079,0.0001151006,0.6355828,0.2983781,0.01131973,0.04511358,0.0001324808],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005932602,0.0001487389,0.9907952,0.0001503015,0.000038324,0.0000243029,0.00007811969,0.001141311,0.001691126],"genre_scores_gemma":[0.1130394,0.0002999243,0.8800039,0.0003610452,0.00005648407,0.00005629666,0.0005085325,0.000746738,0.004927615],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007894882,"threshold_uncertainty_score":0.026411,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01993698523742603,"score_gpt":0.301560628205605,"score_spread":0.281623642968179,"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."}}