{"id":"W2541877621","doi":"10.1109/nssmic.2014.7430809","title":"List-mode motion tracking for positron emission tomography imaging using low-activity fiducial markers","year":2014,"lang":"en","type":"article","venue":"","topic":"Medical Imaging Techniques and Applications","field":"Medicine","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Fiducial marker; Positron emission tomography; Imaging phantom; Physics; Torso; Tracking (education); Artificial intelligence; Match moving; Nuclear medicine; Computer vision; Motion (physics); Algorithm; Computer science; Medicine; Optics; Anatomy","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":[0.001351219,0.000484017,0.0002902527,0.0004121883,0.0003089628,0.000597135,0.001084815,0.0006531227,0.0009468117],"category_scores_gemma":[0.004551894,0.0003918145,0.000401121,0.0004722097,0.000314015,0.0007314009,0.0005453003,0.0004204968,0.0005746854],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008452567,"about_ca_system_score_gemma":0.000745944,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003200943,"about_ca_topic_score_gemma":0.004660519,"domain_scores_codex":[0.9995553,0.0001773293,0.00002841154,0.00006666014,0.000147374,0.00002495351],"domain_scores_gemma":[0.9986947,0.0006803397,0.000192801,0.0001321182,0.0002626439,0.00003747741],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0009039987,0.0001688802,0.009029002,0.000367761,0.00009429824,0.0003107733,0.0005410242,0.5300044,0.1743599,0.01038079,0.002174933,0.2716642],"study_design_scores_gemma":[0.00004076586,0.0002054182,0.001430965,0.00002207592,0.00003109151,0.0001722093,0.00001724427,0.9143342,0.07855432,0.001253373,0.003873661,0.00006459141],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0253141,0.00018695,0.9728503,0.00004620396,0.00002225287,0.00005313785,0.00003604668,0.001111873,0.000379138],"genre_scores_gemma":[0.2471567,0.0002193388,0.7508861,0.00007719824,0.00001058339,0.0001430912,0.0002103304,0.000232833,0.00106383],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003200943,"threshold_uncertainty_score":0.00714606,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02151414732169484,"score_gpt":0.3430701753041107,"score_spread":0.3215560279824158,"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."}}