{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008821625,0.000801186,0.0006252246,0.001412727,0.0004460026,0.001148291,0.0007280594,0.0008775276,0.003519525],"category_scores_gemma":[0.003838512,0.000528104,0.0005113668,0.001351645,0.0002607748,0.0007394211,0.00061247,0.001062504,0.001301124],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005695311,"about_ca_system_score_gemma":0.0008201177,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002315291,"about_ca_topic_score_gemma":0.00322054,"domain_scores_codex":[0.9995433,0.00006390722,0.00004442126,0.0001264532,0.0001539527,0.00006791556],"domain_scores_gemma":[0.9992685,0.0001698886,0.00009446889,0.0001682416,0.0002668749,0.00003208287],"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":[0.001141478,0.00005456303,0.00201245,0.0007491117,0.0001482259,0.0007650626,0.0003292658,0.01280188,0.4060728,0.002577547,0.004465923,0.5688818],"study_design_scores_gemma":[0.0001659672,0.000712932,0.03253813,0.0002230138,0.0004895881,0.004418035,0.0002413641,0.1894073,0.6744941,0.003845672,0.09322216,0.0002417362],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09722358,0.003571475,0.8899211,0.000358298,0.0008149086,0.0003750657,0.0005535437,0.004371558,0.002810483],"genre_scores_gemma":[0.2395934,0.003012877,0.7467715,0.0001977192,0.0001713949,0.0003773035,0.001212002,0.00147818,0.007185651],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003519525,"threshold_uncertainty_score":0.011774,"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."}}