{"id":"W957684199","doi":"10.1118/1.4924010","title":"SU‐E‐I‐13: Evaluation of Metal Artifact Reduction (MAR) Software On Computed Tomography (CT) Images","year":2015,"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":"BC Cancer Agency","funders":"","keywords":"Imaging phantom; Scanner; Image quality; Nuclear medicine; Artifact (error); Computed tomography; Hounsfield scale; Software; Tomography; Materials science; Medical imaging; Biomedical engineering; Computer science; Medicine; Artificial intelligence; Radiology; 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":[0.004792705,0.0008136491,0.000538485,0.001822041,0.0001604272,0.000934548,0.0008315321,0.0008670152,0.001491323],"category_scores_gemma":[0.01134913,0.0003519779,0.0005400927,0.0006989154,0.0004036068,0.0006600995,0.0004877037,0.0003788387,0.0004692558],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003165226,"about_ca_system_score_gemma":0.0003383703,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004435921,"about_ca_topic_score_gemma":0.0005038987,"domain_scores_codex":[0.997745,0.0006247745,0.0003105301,0.0002514766,0.0009840892,0.00008401662],"domain_scores_gemma":[0.9922769,0.004017598,0.0007127617,0.0005986989,0.002205174,0.0001887687],"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":[0.008021671,0.0009363829,0.03706668,0.001408886,0.0007071691,0.0006374552,0.0003196287,0.01235569,0.542295,0.001013237,0.002869643,0.3923686],"study_design_scores_gemma":[0.0003774447,0.005817732,0.09492328,0.000109756,0.0006836403,0.004718491,0.000120657,0.2539273,0.6324387,0.0002756481,0.006430238,0.0001771995],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7413238,0.002018125,0.246268,0.0001534923,0.0001134713,0.0003645848,0.0003973095,0.006595362,0.002765771],"genre_scores_gemma":[0.6527117,0.0005030406,0.3415865,0.0000997447,0.00004238931,0.0003515899,0.001128328,0.001431758,0.002144896],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004792705,"threshold_uncertainty_score":0.02534658,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02844007566278101,"score_gpt":0.2800387793199353,"score_spread":0.2515987036571543,"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."}}