{"id":"W2049785422","doi":"10.1016/j.jmir.2014.02.002","title":"Model-based Iterative Reconstruction: A Promising Algorithm for Today's Computed Tomography Imaging","year":2014,"lang":"en","type":"article","venue":"Journal of medical imaging and radiation sciences","topic":"Radiation Dose and Imaging","field":"Medicine","cited_by":86,"is_retracted":false,"has_abstract":true,"ca_institutions":"St. Michael's Hospital","funders":"Forskningsrådet om Hälsa, Arbetsliv och Välfärd","keywords":"Computed tomography; Medical physics; Iterative reconstruction; Radiation dose; Image quality; Computer science; Medical imaging; Algorithm; Medicine; Tomography; Radiology; Nuclear medicine; Artificial intelligence; Image (mathematics)","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":[0.001061923,0.0007948395,0.0009596527,0.0006892394,0.0004021194,0.001088289,0.001375013,0.00149908,0.001549665],"category_scores_gemma":[0.002790364,0.0005367555,0.00134192,0.001195965,0.0005828214,0.001292259,0.001176056,0.001678608,0.00120392],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004863291,"about_ca_system_score_gemma":0.001260247,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00388916,"about_ca_topic_score_gemma":0.003233185,"domain_scores_codex":[0.999478,0.0001707924,0.00002884074,0.00008561421,0.0002062375,0.00003047753],"domain_scores_gemma":[0.9994795,0.0002327794,0.00005490134,0.00007142989,0.0001387565,0.0000226392],"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":[0.000183089,0.0001088132,0.001925283,0.0004174715,0.00022846,0.0002239049,0.0002230596,0.3769789,0.01544671,0.04309793,0.008184026,0.5529824],"study_design_scores_gemma":[0.00001633435,0.00004722495,0.0001630214,0.00003446633,0.00002566335,0.0001946584,0.00001645712,0.9799939,0.002265979,0.009891151,0.007331098,0.00002004199],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001006735,0.001047169,0.9966806,0.0002012459,0.00003560205,0.00001536506,0.00001256137,0.0002697636,0.0007309279],"genre_scores_gemma":[0.05120252,0.002709508,0.9430168,0.0002386275,0.0001025365,0.00009384233,0.0001563825,0.000190104,0.002289618],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00388916,"threshold_uncertainty_score":0.007733047,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01665856019440974,"score_gpt":0.3159215524860587,"score_spread":0.2992629922916489,"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."}}