{"id":"W2058753079","doi":"10.1118/1.4800806","title":"Resolution modeling in PET imaging: Theory, practice, benefits, and pitfalls","year":2013,"lang":"en","type":"review","venue":"Medical Physics","topic":"Medical Imaging Techniques and Applications","field":"Medicine","cited_by":353,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"National Institute of Biomedical Imaging and Bioengineering; National Institutes of Health","keywords":"Computer science; Context (archaeology); Positron emission tomography; Image resolution; Point spread function; Observer (physics); Resolution (logic); Medical imaging; Medical physics; Artificial intelligence; Nuclear medicine; Medicine; Physics","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001117746,0.0003403237,0.001197839,0.0001023262,0.00006020656,0.00003605938,0.0002327935,0.0002118174,0.0001108977],"category_scores_gemma":[0.001535,0.000252601,0.0001778372,0.0003272593,0.0002177528,0.0001295561,0.0002068174,0.001422999,0.00007552913],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001097267,"about_ca_system_score_gemma":0.0004270361,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008125707,"about_ca_topic_score_gemma":8.508208e-7,"domain_scores_codex":[0.9975281,0.0001620485,0.0006902865,0.000519316,0.0007369362,0.0003633572],"domain_scores_gemma":[0.9981765,0.0005150724,0.0002068704,0.0005255108,0.0001077619,0.0004682639],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000003647265,0.0002051734,0.000002172754,0.002604658,0.00002909832,0.00004978041,0.00001778379,3.669016e-7,1.374926e-7,0.00958076,0.004400757,0.9831057],"study_design_scores_gemma":[0.0003406985,0.00002861783,4.590388e-7,0.019996,0.0006543699,0.0004328145,0.00001436567,0.02043584,5.395466e-7,0.00608835,0.9517225,0.0002854426],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.000004986041,0.9631405,0.02988228,0.00387956,0.00006233837,0.001090763,0.00001030299,0.0001457325,0.001783532],"genre_scores_gemma":[0.00008204279,0.9919312,0.005509016,0.001192913,0.0005651264,0.0003605417,0.0001927416,0.00006604208,0.0001003643],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9828202,"threshold_uncertainty_score":0.9999926,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07700151388050115,"score_gpt":0.3870551149579711,"score_spread":0.3100536010774699,"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."}}