{"id":"W2164091078","doi":"10.1109/tmi.2009.2024684","title":"Rapid Dynamic Image Registration of the Beating Heart for Diagnosis and Surgical Navigation","year":2009,"lang":"en","type":"article","venue":"IEEE Transactions on Medical Imaging","topic":"Advanced MRI Techniques and Applications","field":"Medicine","cited_by":47,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University; London Health Sciences Centre; Ontario Tech University; Robarts Clinical Trials; Hospital for Sick Children","funders":"Canadian Institutes of Health Research; Health Research Board","keywords":"Imaging phantom; Image registration; Metric (unit); Image quality; Context (archaeology); Artificial intelligence; Cardiac imaging; Computer vision; Computer science; Magnetic resonance imaging; Medicine; 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":[],"consensus_categories":[],"category_scores_codex":[0.0002108207,0.00008408828,0.0001465375,0.00004021998,0.0001667245,0.000009492052,0.00005167308,0.00005303718,0.00002879091],"category_scores_gemma":[0.00003937348,0.0000626975,0.00009596004,0.0001497409,0.0001469449,0.00008606993,7.052022e-7,0.0002192827,5.362276e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003478094,"about_ca_system_score_gemma":0.00004144839,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009107445,"about_ca_topic_score_gemma":0.000001839443,"domain_scores_codex":[0.9991724,0.00002029956,0.0002571192,0.0001730165,0.0002550893,0.0001220915],"domain_scores_gemma":[0.9994135,0.0001804243,0.00006816337,0.0001753047,0.00007095749,0.00009161463],"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":[0.0001032193,0.0005201696,0.0002601919,0.0001002163,0.0000106524,0.000008923478,0.0001550401,0.0001199613,0.02150233,0.0005319389,0.000533483,0.9761539],"study_design_scores_gemma":[0.008322712,0.00158315,0.01145499,0.004352766,0.0007369099,0.001985246,0.0008112419,0.4161995,0.5159943,0.01195353,0.02575208,0.0008536577],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05877791,0.0001077964,0.9156824,0.02446128,0.00005226984,0.000616882,0.00001456238,0.00007864508,0.0002082622],"genre_scores_gemma":[0.9770585,0.0001501122,0.0220632,0.0004832367,0.00003481243,0.000133797,0.000008354549,0.000009828677,0.00005821031],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9753002,"threshold_uncertainty_score":0.255673,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0131128734794723,"score_gpt":0.3363242141140371,"score_spread":0.3232113406345647,"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."}}