{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001711943,0.0002952161,0.0004933319,0.00008480146,0.0001102885,0.0000317905,0.00006252867,0.0001353293,0.00002726111],"category_scores_gemma":[0.0001006563,0.0002755554,0.00004490351,0.0001718449,0.000381626,0.0004663814,0.00002615493,0.0004314095,0.000001906589],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000818323,"about_ca_system_score_gemma":0.00008530041,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008926513,"about_ca_topic_score_gemma":0.00001061165,"domain_scores_codex":[0.9984833,0.0001018134,0.0004420298,0.0003837301,0.0003322984,0.0002568152],"domain_scores_gemma":[0.9990545,0.0002648216,0.0001357812,0.0001435417,0.0002269937,0.0001743704],"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":[0.001093466,0.0004987852,0.007031259,0.000741441,0.0003774979,0.000007538831,0.008658616,0.3306183,0.006498661,0.002528415,0.0005067096,0.6414394],"study_design_scores_gemma":[0.001564809,0.0002073035,0.0002809884,0.0002621193,0.0000518899,0.00001942128,0.000179036,0.9731689,0.0163264,0.00766007,0.000002694071,0.0002763646],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06379769,0.00005036528,0.934768,0.00005625066,0.0001634349,0.0002974106,0.0001170506,0.00007918989,0.0006706453],"genre_scores_gemma":[0.9619665,0.000009499293,0.03763076,0.0001140299,0.0001343511,0.00003303865,0.00006055072,0.00003413115,0.00001718503],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8981687,"threshold_uncertainty_score":0.9999697,"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."}}