{"id":"W2168894636","doi":"10.1109/nssmic.1995.510427","title":"Motion correction of PET images using multiple acquisition frames","year":2002,"lang":"en","type":"article","venue":"1995 IEEE Nuclear Science Symposium and Medical Imaging Conference Record","topic":"Medical Imaging Techniques and Applications","field":"Medicine","cited_by":44,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Computer vision; Computer science; Artificial intelligence; Position (finance); Head (geology); Data acquisition; Frame (networking); Displacement (psychology); Motion (physics); Positron emission tomography; Nuclear medicine; Medicine","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":[0.0005925382,0.0001644181,0.0002979571,0.0002240298,0.0002713363,0.00008323532,0.0002212611,0.00006778135,0.0006489252],"category_scores_gemma":[0.0004028658,0.0001404961,0.00006613605,0.0004484844,0.001607472,0.0003857423,0.0000825661,0.0003377426,0.00001754339],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007305967,"about_ca_system_score_gemma":0.0001125702,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002838447,"about_ca_topic_score_gemma":0.000001765748,"domain_scores_codex":[0.9979658,0.00003822571,0.0003685771,0.0004662538,0.0008070661,0.0003540578],"domain_scores_gemma":[0.9986663,0.00008314283,0.0001547644,0.0003038879,0.0002923313,0.0004995006],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002271748,0.0002860189,0.009079368,0.0001118256,0.000009200107,0.00002902104,0.0003552571,0.000001747703,0.7040188,0.0002700837,0.004174279,0.2816417],"study_design_scores_gemma":[0.0004594911,0.00008537614,0.001365708,0.0005379361,0.00004911987,0.0004431515,0.0001884763,0.985934,0.008828325,0.0001197426,0.001829949,0.0001586926],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9202239,0.00004114163,0.06521344,0.009455929,0.0008616104,0.0003924426,0.000006222944,0.0002710972,0.003534256],"genre_scores_gemma":[0.9768138,0.0007759173,0.02143957,0.0006694387,0.0001429111,0.000008355034,0.000002841958,0.00001776746,0.0001293768],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9859323,"threshold_uncertainty_score":0.7105276,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02304413121210064,"score_gpt":0.2941550921827016,"score_spread":0.2711109609706009,"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."}}