{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007645597,0.0005244391,0.000218863,0.0003993265,0.0002152659,0.000466887,0.0003703185,0.0004253233,0.0005291394],"category_scores_gemma":[0.003187711,0.0002557056,0.0003426292,0.0002234524,0.0004833236,0.0003184832,0.000435907,0.000305911,0.00009973012],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001122784,"about_ca_system_score_gemma":0.0006442806,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001533077,"about_ca_topic_score_gemma":0.001396829,"domain_scores_codex":[0.9996942,0.0001211275,0.00001091336,0.00003727452,0.0001151662,0.00002132728],"domain_scores_gemma":[0.9992158,0.0005175492,0.0001134034,0.00002908782,0.000107219,0.00001693273],"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":[0.0001821743,0.00008659245,0.001550628,0.000126022,0.00004001946,0.00004967429,0.0001110536,0.9069259,0.061808,0.002668518,0.0002176724,0.02623376],"study_design_scores_gemma":[0.00003936931,0.0002193648,0.00134981,0.0000167604,0.00003276695,0.00008468154,0.00001401609,0.9595522,0.03608811,0.001715112,0.0008734284,0.00001430876],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1978144,0.0003975427,0.7986131,0.0001195387,0.00001209064,0.0000919966,0.0000243196,0.0002779016,0.002649164],"genre_scores_gemma":[0.8667775,0.0001047448,0.1319871,0.00006832113,0.00001001962,0.00009585699,0.00004618177,0.0001099704,0.0008003215],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001533077,"threshold_uncertainty_score":0.008146405,"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."}}