{"id":"W2042439044","doi":"10.1118/1.3574885","title":"Fluence field optimization for noise and dose objectives in CT","year":2011,"lang":"en","type":"article","venue":"Medical Physics","topic":"Radiation Dose and Imaging","field":"Medicine","cited_by":85,"is_retracted":false,"has_abstract":true,"ca_institutions":"Princess Margaret Cancer Centre; Ontario Institute for Cancer Research; University of Toronto","funders":"","keywords":"Image quality; Imaging phantom; Fluence; Dosimetry; Medical imaging; Signal-to-noise ratio (imaging); Simulated annealing; Nuclear medicine; Optics; Computer science; Medical physics; Physics; Computer vision; Algorithm; Artificial intelligence; Image (mathematics); 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":[0.00009315839,0.00004622919,0.00009728444,0.00001739112,0.0000194175,0.00000403936,0.0000301805,0.00002473833,0.00009392726],"category_scores_gemma":[0.0004465103,0.00003919987,0.00001860383,0.00007938666,0.00003659411,0.00009531021,0.00001133006,0.00008953333,0.000002525049],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001039595,"about_ca_system_score_gemma":0.00006417814,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003966214,"about_ca_topic_score_gemma":0.000002555783,"domain_scores_codex":[0.9995759,0.000008515608,0.00009424301,0.0001054953,0.0001278465,0.00008800261],"domain_scores_gemma":[0.9997227,0.00008649717,0.00002070247,0.00006962059,0.00002064825,0.00007985729],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003407339,0.0006691084,0.1817285,0.0002988507,0.00005209133,0.00007286268,0.005527027,0.0001945599,0.0004244825,0.001245625,0.001958456,0.8074877],"study_design_scores_gemma":[0.01521335,0.001467983,0.3843113,0.001194689,0.0001960736,0.00008410079,0.0006554271,0.4599055,0.11591,0.01870975,0.001598107,0.000753752],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7970083,0.0002443991,0.1934139,0.001984757,0.000167288,0.0004586568,0.00000207788,0.00004703744,0.006673601],"genre_scores_gemma":[0.9966514,0.00007091661,0.00152161,0.001548159,0.0001334295,0.00001603842,0.000005403861,0.000006031597,0.00004702388],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.806734,"threshold_uncertainty_score":0.1598524,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02094084638574974,"score_gpt":0.2862061286543341,"score_spread":0.2652652822685844,"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."}}