{"id":"W2057100016","doi":"10.1097/rli.0b013e31827f1b68","title":"Free Breathing Real-Time Cardiac Cine Imaging With Improved Spatial Resolution at 3 T","year":2013,"lang":"en","type":"article","venue":"Investigative Radiology","topic":"Cardiac Imaging and Diagnostics","field":"Medicine","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Bayerische Forschungsstiftung; Bundesministerium für Bildung und Forschung; Deutsches Zentrum für Herz-Kreislaufforschung","keywords":"Voxel; Imaging phantom; Nuclear medicine; Image quality; Image resolution; Temporal resolution; Breathing; Cardiac imaging; Iterative reconstruction; Ventricle; Partial volume; Ejection fraction; Biomedical engineering; Computer science; Medicine; Artificial intelligence; Physics; Radiology; Image (mathematics); Cardiology","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":[0.00176804,0.0006292007,0.0004108403,0.0002931358,0.0001377183,0.000755266,0.0008346849,0.001355366,0.002362012],"category_scores_gemma":[0.003029109,0.0003816532,0.0003154892,0.0001902217,0.0003222008,0.001009726,0.0003480711,0.0006251077,0.0005340231],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001521715,"about_ca_system_score_gemma":0.000277694,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002247247,"about_ca_topic_score_gemma":0.0005104788,"domain_scores_codex":[0.9996563,0.0001419974,0.00001937249,0.00006071218,0.00009737688,0.000024204],"domain_scores_gemma":[0.9989576,0.000483132,0.0001772145,0.0001149871,0.000186295,0.00008079796],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0007442264,0.0001444412,0.001309714,0.0004483821,0.0001023786,0.0004922274,0.0001662536,0.00247183,0.9605301,0.0004345582,0.0005886126,0.03256722],"study_design_scores_gemma":[0.000760995,0.005168278,0.03436388,0.0001830243,0.0007617204,0.01810821,0.0001421247,0.07426153,0.8542176,0.001787734,0.01007573,0.0001690917],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6814567,0.005792157,0.3073843,0.0007934215,0.0001316921,0.000145605,0.0002051012,0.001356647,0.002734313],"genre_scores_gemma":[0.6960633,0.001936171,0.2983209,0.0005915685,0.000181438,0.0001336882,0.0004960657,0.0005608218,0.001715936],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002362012,"threshold_uncertainty_score":0.009350359,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009312027644218436,"score_gpt":0.2308191785664458,"score_spread":0.2215071509222274,"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."}}