{"id":"W4253029326","doi":"10.1109/nssmic.1996.587982","title":"Analytical modeling of PET imaging with correlated functional and structural images","year":2002,"lang":"en","type":"article","venue":"1996 IEEE Nuclear Science Symposium. Conference Record","topic":"Medical Imaging Techniques and Applications","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; Montreal Neurological Institute and Hospital","funders":"Medical Research Council","keywords":"Imaging phantom; Correction for attenuation; Attenuation; Computer science; Projection (relational algebra); Iterative reconstruction; Partial volume; Artificial intelligence; Computer vision; Detector; Image processing; Image (mathematics); Physics; Algorithm; Optics","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":[0.0008264498,0.0007669064,0.0005377836,0.0006082652,0.0002595507,0.001159292,0.001708034,0.001593939,0.001785397],"category_scores_gemma":[0.0044929,0.0008394867,0.0007606061,0.0007566272,0.0008373584,0.001238866,0.0005175605,0.0006842473,0.0009282455],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001507674,"about_ca_system_score_gemma":0.000745225,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00443733,"about_ca_topic_score_gemma":0.001639918,"domain_scores_codex":[0.9996008,0.0001152953,0.00002037172,0.00006121821,0.000162198,0.00004017301],"domain_scores_gemma":[0.9991744,0.0004769081,0.00009268025,0.0000589356,0.0001721153,0.00002488218],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002791336,0.00001689168,0.0003000938,0.00006766979,0.00002166748,0.0002829214,0.00006828435,0.9687678,0.004124985,0.02026965,0.0005507465,0.005501339],"study_design_scores_gemma":[0.000003367955,0.000008875792,0.00008658414,0.000005564359,0.000005617149,0.00009495488,0.000005294567,0.9947184,0.0006504105,0.003538496,0.0008766189,0.000005975775],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01850388,0.0007907444,0.9690276,0.0005122323,0.00005861467,0.00008321613,0.0001729483,0.0006279941,0.01022281],"genre_scores_gemma":[0.736957,0.003663158,0.23465,0.000606673,0.0001752862,0.0006874512,0.0003909485,0.0004999228,0.02236949],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00443733,"threshold_uncertainty_score":0.01093894,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03011326739160681,"score_gpt":0.2723021795913049,"score_spread":0.2421889121996981,"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."}}