{"id":"W2284538908","doi":"10.1002/mrm.26146","title":"High‐resolution dynamic CE‐MRA of the thorax enabled by iterative TWIST reconstruction","year":2016,"lang":"en","type":"article","venue":"Magnetic Resonance in Medicine","topic":"Advanced MRI Techniques and Applications","field":"Medicine","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto General Hospital; University of Toronto; University Health Network","funders":"Erlangen Graduate School of Advanced Optical Technologies; Deutsche Forschungsgemeinschaft","keywords":"Iterative reconstruction; Image quality; Computer science; Scanner; Magnetic resonance angiography; Iterative method; Image resolution; Twist; Nuclear medicine; Algorithm; Magnetic resonance imaging; Computer vision; Artificial intelligence; Radiology; Mathematics; Medicine; Image (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":[0.000226339,0.0001441263,0.0003283723,0.00008063787,0.00005277007,0.000002255054,0.0001384065,0.00008980157,0.0003504555],"category_scores_gemma":[0.0002187095,0.00007477749,0.00004172593,0.0004257982,0.0005560378,0.00005886822,0.0000366697,0.0001661673,0.000004758146],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001505512,"about_ca_system_score_gemma":0.0000452311,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001216194,"about_ca_topic_score_gemma":0.00003925412,"domain_scores_codex":[0.998734,0.00005803345,0.0004553351,0.0002842607,0.0002563583,0.0002119905],"domain_scores_gemma":[0.9990826,0.0001139272,0.000169495,0.0004632915,0.0001157966,0.0000548366],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0002069285,0.0001143733,0.005775725,0.00004948949,0.00000330727,0.000004998395,0.0002229943,0.000005423659,0.2839841,0.003132972,0.007728621,0.6987711],"study_design_scores_gemma":[0.01773296,0.00443081,0.4312006,0.0129908,0.0002690084,0.0004813986,0.00107423,0.004745435,0.1160913,0.05680962,0.3532799,0.0008939418],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8536485,0.0179412,0.06503901,0.05272892,0.0005299733,0.002977931,0.0001028285,0.000176824,0.006854858],"genre_scores_gemma":[0.9801125,0.002503981,0.01043364,0.0003456252,0.00009643713,0.0001855464,0.00001184448,0.00002091253,0.006289521],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6978772,"threshold_uncertainty_score":0.3837242,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008709421216936313,"score_gpt":0.2818152140893231,"score_spread":0.2731057928723868,"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."}}