{"id":"W2035350590","doi":"10.1118/1.4894980","title":"Sci—Thur PM: Imaging — 07: Assessment of iterative reconstruction algorithms in four commercial MDCT scanners: a phantom study","year":2014,"lang":"en","type":"article","venue":"Medical Physics","topic":"Radiation Dose and Imaging","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba; CancerCare Manitoba","funders":"","keywords":"Imaging phantom; Iterative reconstruction; Image quality; Radon transform; Image resolution; Algorithm; Noise (video); Optical transfer function; Image noise; Nuclear medicine; Noise reduction; Computer science; Artificial intelligence; Physics; Medicine; Optics; 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":[0.006988382,0.0005834394,0.0003830458,0.001531817,0.0003123558,0.001060907,0.0007884879,0.0007847052,0.002550857],"category_scores_gemma":[0.01518979,0.0005907112,0.0006228829,0.001162673,0.0005027599,0.0005523199,0.0007522806,0.0003403667,0.0008231939],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005286955,"about_ca_system_score_gemma":0.0003148871,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001198645,"about_ca_topic_score_gemma":0.0009675716,"domain_scores_codex":[0.997483,0.001291637,0.0002492139,0.0002860709,0.0006011728,0.0000889209],"domain_scores_gemma":[0.9904273,0.005231283,0.000793073,0.001701319,0.001641488,0.000205565],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.007424414,0.001218476,0.02784958,0.0008583636,0.0006831329,0.0005596575,0.0007723715,0.0338792,0.6782731,0.001820813,0.001871933,0.2447889],"study_design_scores_gemma":[0.0005350987,0.011084,0.1244075,0.0001537712,0.0006211494,0.007433756,0.0002647264,0.1349762,0.7063389,0.0008859215,0.01300906,0.0002899679],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8490106,0.001790851,0.1433285,0.0002792296,0.00005345264,0.0004468545,0.0004705439,0.0009498895,0.003670119],"genre_scores_gemma":[0.7998883,0.0008610609,0.1937005,0.0002103802,0.00005485583,0.000249377,0.001172768,0.001456548,0.002406296],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006988382,"threshold_uncertainty_score":0.03695852,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02296006307478131,"score_gpt":0.346055335534745,"score_spread":0.3230952724599637,"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."}}