{"id":"W4304987437","doi":"10.1002/mp.16049","title":"Metal artifact correction in photon‐counting detector computed tomography: metal trace replacement using high‐energy data","year":2022,"lang":"en","type":"article","venue":"Medical Physics","topic":"Advanced X-ray and CT Imaging","field":"Engineering","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"Redlen Technologies (Canada); University of Victoria","funders":"","keywords":"Imaging phantom; Cadmium zinc telluride; Iterative reconstruction; Detector; Image quality; Photon counting; Artifact (error); Energy (signal processing); Tomography; Nuclear medicine; Data set; Optics; Materials science; Physics; Medical physics; Computer science; Computer vision; Artificial intelligence; Medicine; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003846018,0.0001931035,0.0002808253,0.00009136814,0.0001747469,0.00002190518,0.000353095,0.00004087467,0.0001808299],"category_scores_gemma":[0.00004528265,0.0002146473,0.00006652575,0.0006637803,0.00004633958,0.0003379048,0.0002822815,0.0005586259,0.000002601286],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001321759,"about_ca_system_score_gemma":0.00004350694,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002525833,"about_ca_topic_score_gemma":0.0000631064,"domain_scores_codex":[0.9982302,0.00007172178,0.0003322312,0.0003512993,0.000658029,0.0003564715],"domain_scores_gemma":[0.9993114,0.00009921246,0.00006554459,0.0004047175,0.0000170106,0.0001020825],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001485318,0.0006601636,0.002468268,0.0001438301,0.0005001712,0.0002083386,0.0006147888,0.6818123,0.02444766,0.000419263,0.0008988673,0.2876778],"study_design_scores_gemma":[0.0005418095,0.00003544225,0.0002566228,0.00003559635,0.00005286989,0.00002357764,0.0001428585,0.9733893,0.02130737,0.0005582033,0.003372994,0.0002833218],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4977317,0.0004302447,0.4990819,0.00002883444,0.002177934,0.0001139892,0.0000301713,0.0002863634,0.0001189325],"genre_scores_gemma":[0.998365,0.000009269971,0.001030937,0.00009920672,0.0002694253,0.00002201403,0.000149203,0.00004289568,0.0000120026],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5006334,"threshold_uncertainty_score":0.8753062,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01924279249788182,"score_gpt":0.2470373999417339,"score_spread":0.2277946074438521,"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."}}