{"id":"W2144633671","doi":"10.1109/ccece.1996.548111","title":"Objective image quality measures for evaluating advanced MRI reconstruction methods","year":2002,"lang":"en","type":"article","venue":"","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Computer science; Pixel; Artificial intelligence; Image quality; Human visual system model; Computer vision; Quality (philosophy); Image (mathematics); Basis (linear algebra); Visualization; Iterative reconstruction; Pattern recognition (psychology); Mathematics","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":[0.005465931,0.000150933,0.0002524692,0.00009890187,0.0002670347,0.0001932632,0.0004056176,0.00006367796,0.00007110116],"category_scores_gemma":[0.001428233,0.0001323648,0.000144896,0.0003350633,0.00005615719,0.0009997038,0.00008222643,0.0001265664,0.00002011199],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007153537,"about_ca_system_score_gemma":0.00003031244,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002482597,"about_ca_topic_score_gemma":0.00000369726,"domain_scores_codex":[0.9972058,0.001337362,0.0003711744,0.0005101317,0.0002754653,0.0003000635],"domain_scores_gemma":[0.9976271,0.001234139,0.0001600568,0.0004706451,0.0004383844,0.00006968996],"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":[0.00001523393,0.00001733559,0.000007376172,0.000009974237,0.00001365206,4.877198e-7,0.0004171318,0.00005118854,0.1213078,0.003000339,0.0001575818,0.8750018],"study_design_scores_gemma":[0.001825655,0.0003228875,0.0003999915,0.00003170025,0.00002455603,0.00005635417,0.000233024,0.4968652,0.4152592,0.08297551,0.001523616,0.0004823632],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0008631986,0.000257969,0.9767731,0.0003761764,0.0006705998,0.0003405783,0.000001297041,0.0002362071,0.02048091],"genre_scores_gemma":[0.005084598,0.00001408296,0.9932781,0.0002679508,0.0000702635,0.00005346001,4.657694e-7,0.0000105162,0.001220598],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.8745195,"threshold_uncertainty_score":0.539768,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1328270070587388,"score_gpt":0.4492833041995432,"score_spread":0.3164562971408044,"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."}}