{"id":"W2033475037","doi":"10.1118/1.3181164","title":"SU‐FF‐I‐45: An Automatic Method for Reduction of Metal Artifacts Caused by Metallic Implants","year":2009,"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":"Centre hospitalier universitaire de Québec","funders":"","keywords":"Imaging phantom; Scanner; Interpolation (computer graphics); Artificial intelligence; Projection (relational algebra); Computer vision; Artifact (error); Computer science; Iterative reconstruction; Nuclear medicine; Reduction (mathematics); Medical imaging; Brachytherapy; Biomedical engineering; Mathematics; Image (mathematics); Medicine; Algorithm; Radiology; Radiation therapy","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":[],"consensus_categories":[],"category_scores_codex":[0.0002491602,0.0001526966,0.0003140804,0.00002719124,0.00004356333,0.00001076723,0.0001343157,0.00007562379,0.00003794719],"category_scores_gemma":[0.00007139591,0.0001416977,0.00008375944,0.0001459453,0.00003520969,0.0002769222,0.000008413605,0.0001739127,0.000006749162],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003068241,"about_ca_system_score_gemma":0.00002205867,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003816786,"about_ca_topic_score_gemma":6.832535e-7,"domain_scores_codex":[0.9989479,0.00003297073,0.0002742299,0.0001590313,0.0003167316,0.0002691795],"domain_scores_gemma":[0.9994807,0.00007730022,0.00005367743,0.0001852788,0.00003106803,0.0001719303],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00001109117,0.0002406024,0.000003287745,0.0001367609,0.0001315854,0.000003908121,0.0005005946,0.002245466,0.2656006,0.000861698,0.0008582462,0.7294062],"study_design_scores_gemma":[0.001010586,0.0002204777,0.00018844,0.00008458471,0.0002070216,0.00002454608,0.0001197785,0.2579696,0.6999167,0.0385011,0.001350238,0.0004068933],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2548676,0.0001589581,0.7439342,0.0001241963,0.0002527953,0.0001918409,0.00002144512,0.0002655818,0.0001833601],"genre_scores_gemma":[0.9881634,0.00001536749,0.0113989,0.00007994797,0.0002079488,0.00001603869,0.0000636234,0.00002676134,0.00002802308],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7332957,"threshold_uncertainty_score":0.5778263,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01678078861867684,"score_gpt":0.3005331470107028,"score_spread":0.283752358392026,"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."}}