{"id":"W1965997320","doi":"10.1002/jmri.20003","title":"Automated image registration of gated cardiac single‐photon emission computed tomography and magnetic resonance imaging","year":2004,"lang":"en","type":"article","venue":"Journal of Magnetic Resonance Imaging","topic":"Cardiac Imaging and Diagnostics","field":"Medicine","cited_by":32,"is_retracted":false,"has_abstract":true,"ca_institutions":"Robarts Clinical Trials; Western University","funders":"Medical Research Council; Canadian Institutes of Health Research","keywords":"Magnetic resonance imaging; Segmentation; Artificial intelligence; Single-photon emission computed tomography; Thresholding; Image registration; Nuclear medicine; Computer science; Preprocessor; Emission computed tomography; Mutual information; Computer vision; Medicine; Positron emission tomography; Radiology; Image (mathematics)","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0008340526,0.000376311,0.0009056462,0.0005278395,0.0001270899,0.0001329792,0.0001819848,0.00008136132,0.00001973945],"category_scores_gemma":[0.0007891198,0.0003520394,0.0003538567,0.000778699,0.0004841628,0.0003869529,0.00007793503,0.0005037003,0.000002592323],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000164969,"about_ca_system_score_gemma":0.0003088039,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001327482,"about_ca_topic_score_gemma":0.000001322121,"domain_scores_codex":[0.9967783,0.0001379304,0.00128733,0.0004131719,0.0008803592,0.0005028696],"domain_scores_gemma":[0.9972352,0.0002638887,0.0007466986,0.0004512484,0.0009808984,0.0003220487],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0007826008,0.000615615,0.3379692,0.0004565476,0.00003243174,0.001632784,0.0009765026,0.00007334149,0.3323795,0.00005666078,0.01112947,0.3138954],"study_design_scores_gemma":[0.007606087,0.0009280719,0.916126,0.005179036,0.0005124697,0.002817155,0.000435886,0.006890316,0.04056137,0.0004031621,0.01801581,0.0005246783],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7212015,0.2720321,0.000739492,0.002774066,0.0006469696,0.0005008091,0.00003209941,0.0001982525,0.001874679],"genre_scores_gemma":[0.9773496,0.001349846,0.02055181,0.0003000732,0.0002683037,0.000004023751,0.00001442204,0.00006570147,0.00009615183],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5781568,"threshold_uncertainty_score":0.9998932,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007021108116391947,"score_gpt":0.2477694975710149,"score_spread":0.240748389454623,"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."}}