{"id":"W2023957719","doi":"10.1002/jmri.20144","title":"Quantitative evaluation of metal artifact reduction techniques","year":2004,"lang":"en","type":"article","venue":"Journal of Magnetic Resonance Imaging","topic":"Advanced MRI Techniques and Applications","field":"Medicine","cited_by":89,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Artifact (error); Distortion (music); Noise (video); Reduction (mathematics); Materials science; Noise reduction; Image quality; Signal-to-noise ratio (imaging); Artificial intelligence; Computer vision; Computer science; Image (mathematics); Optics; Mathematics; Physics","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":[0.005373129,0.0009494192,0.0003908639,0.002348212,0.0002696048,0.0009410788,0.0007002398,0.0006393291,0.002042901],"category_scores_gemma":[0.01436985,0.0002655058,0.0003443389,0.0008914181,0.0006548292,0.0008324094,0.0005696836,0.0004113225,0.0005886553],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004551703,"about_ca_system_score_gemma":0.000345441,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004562624,"about_ca_topic_score_gemma":0.0004666481,"domain_scores_codex":[0.9962432,0.001108904,0.0002883013,0.0003413877,0.001913591,0.0001045056],"domain_scores_gemma":[0.9895957,0.004122898,0.001193044,0.0008959314,0.004038786,0.0001536802],"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":[0.001037278,0.0002163051,0.01076339,0.001361171,0.0001894735,0.0001119762,0.0003446187,0.004051349,0.7179162,0.001133602,0.0004760394,0.2623986],"study_design_scores_gemma":[0.0001298606,0.003122994,0.06562816,0.0001841366,0.0003297531,0.002412064,0.0003127746,0.0518791,0.8661989,0.001410138,0.008226774,0.0001653129],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3262087,0.005126166,0.6616914,0.0001565183,0.0001149353,0.0006235373,0.0003962152,0.001657663,0.004024912],"genre_scores_gemma":[0.5492237,0.001156366,0.4462562,0.00008614417,0.00007068936,0.0006360562,0.0004238253,0.000307361,0.001839679],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005373129,"threshold_uncertainty_score":0.02841622,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04054462545377114,"score_gpt":0.3845878771140973,"score_spread":0.3440432516603261,"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."}}