{"id":"W3152578832","doi":"10.22215/etd/2016-11417","title":"Motion Detection in Dynamic Cardiac Positron Emission Tomography Images","year":2016,"lang":"en","type":"dissertation","venue":"","topic":"Medical Imaging Techniques and Applications","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Fiducial marker; Positron emission tomography; Cardiac PET; Computer vision; Artificial intelligence; Computer science; Dynamic imaging; Motion compensation; Nuclear medicine; Biomedical engineering; Image processing; Medicine; Image (mathematics); Digital image processing","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":[0.0005917639,0.0002999871,0.0003453254,0.0008283956,0.0001431575,0.000643616,0.0003218987,0.0003460429,0.001204134],"category_scores_gemma":[0.002258031,0.0003190868,0.0002376919,0.0006762272,0.0003164324,0.0005040956,0.0003284549,0.0004120244,0.0007588841],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002431964,"about_ca_system_score_gemma":0.0002775172,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000366209,"about_ca_topic_score_gemma":0.0004643107,"domain_scores_codex":[0.9997659,0.00004903795,0.00001264086,0.00005535962,0.00009235992,0.00002465257],"domain_scores_gemma":[0.9995104,0.0002199377,0.00006732675,0.00005224944,0.0001329973,0.00001707741],"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":[0.0002002636,0.00003915955,0.00205154,0.0004011579,0.00003229882,0.0002524054,0.0002625238,0.01390315,0.4501407,0.006534533,0.001391452,0.5247909],"study_design_scores_gemma":[0.00005714967,0.0008704057,0.04306938,0.0003168819,0.0001127455,0.003222497,0.0005011933,0.3221197,0.5672956,0.01436409,0.04793991,0.0001305653],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1571366,0.005191535,0.8312226,0.0003846416,0.000099861,0.0001074352,0.0001743576,0.000648455,0.005034405],"genre_scores_gemma":[0.398926,0.009010696,0.5800115,0.0001564547,0.0001007331,0.0001322364,0.0004156007,0.000265668,0.01098101],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001204134,"threshold_uncertainty_score":0.004028201,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004735113004586539,"score_gpt":0.2995408701295953,"score_spread":0.2948057571250088,"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."}}