{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001135852,0.000580819,0.0003308447,0.0007039084,0.0001865976,0.0008584024,0.000962826,0.0006004834,0.001044231],"category_scores_gemma":[0.003881894,0.0003311501,0.0003699815,0.0007982141,0.0004275728,0.0006633179,0.0006026978,0.0005134761,0.0002663232],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003334803,"about_ca_system_score_gemma":0.0006721286,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001198052,"about_ca_topic_score_gemma":0.001553808,"domain_scores_codex":[0.9996283,0.000120594,0.00002141038,0.00003966823,0.0001580588,0.00003200493],"domain_scores_gemma":[0.9985471,0.0006170772,0.0002500598,0.0002048004,0.0003321498,0.0000486976],"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":[0.0015179,0.0002708399,0.01499815,0.0006022408,0.0001975775,0.0005934652,0.000329132,0.2426653,0.4466246,0.004824414,0.001209192,0.2861673],"study_design_scores_gemma":[0.00004659611,0.0002701843,0.004558093,0.00003251587,0.00006176408,0.0005733428,0.00007364259,0.7149335,0.2758866,0.001184797,0.002340954,0.00003798471],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2851318,0.0003528354,0.7117946,0.0001831263,0.00002761255,0.00006359932,0.00008784661,0.0013382,0.001020426],"genre_scores_gemma":[0.5271785,0.0002738503,0.4713067,0.0000504084,0.000007686403,0.00003205675,0.0001890347,0.0001945637,0.000767261],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001198052,"threshold_uncertainty_score":0.006007075,"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."}}