{"id":"W2027931057","doi":"10.1118/1.4894991","title":"Poster — Thur Eve — 05: Objective phantom‐based and porcine model comparison of filtered back projection, adaptive statistical iterative reconstruction and model based iterative reconstruction algorithms","year":2014,"lang":"en","type":"article","venue":"Medical Physics","topic":"Advanced X-ray and CT Imaging","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba; CancerCare Manitoba","funders":"","keywords":"Imaging phantom; Iterative reconstruction; Image quality; Algorithm; Noise (video); Noise reduction; Image noise; Projection (relational algebra); Radon transform; Contrast-to-noise ratio; Iterative method; Computer science; Scanner; Mathematics; Computer vision; Nuclear medicine; 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":[0.002436492,0.0006448092,0.0003783361,0.0008382305,0.0002648803,0.001036794,0.0004800448,0.0007990603,0.003605077],"category_scores_gemma":[0.002892765,0.0004377306,0.0006999386,0.0003992862,0.0004336557,0.0005732292,0.0005810767,0.0003792263,0.0005811024],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004780907,"about_ca_system_score_gemma":0.0004835302,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001815262,"about_ca_topic_score_gemma":0.001863722,"domain_scores_codex":[0.9992093,0.000238127,0.00004923899,0.0001371658,0.0003120661,0.00005414296],"domain_scores_gemma":[0.9984926,0.0005533219,0.0002207906,0.0002415657,0.0004310343,0.00006063762],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.003042623,0.000642477,0.006042891,0.0006307226,0.0002261147,0.0004445051,0.0001856246,0.0597199,0.8690349,0.001297799,0.002485586,0.05624679],"study_design_scores_gemma":[0.0001980912,0.005614965,0.0224752,0.0001198922,0.0003460654,0.002730982,0.000123767,0.1463279,0.8122486,0.0008381578,0.008721022,0.0002554451],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7875195,0.001929958,0.1974943,0.0004234141,0.0002097285,0.0004546495,0.001716693,0.001097758,0.009154026],"genre_scores_gemma":[0.8193193,0.001258039,0.1640237,0.0002325513,0.00006324063,0.0002491419,0.004065757,0.001035022,0.009753239],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003605077,"threshold_uncertainty_score":0.01288551,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01993062112721017,"score_gpt":0.2706390822065416,"score_spread":0.2507084610793314,"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."}}