{"id":"W2016697944","doi":"10.1016/j.carj.2009.02.036","title":"Parallel Imaging Artifacts in Body Magnetic Resonance Imaging","year":2009,"lang":"en","type":"review","venue":"Canadian Association of Radiologists Journal","topic":"Advanced MRI Techniques and Applications","field":"Medicine","cited_by":28,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; University Health Network; Hôtel-Dieu de Québec; McGill University; Montreal General Hospital; Université Laval; Mount Sinai Hospital","funders":"National Institute of Biomedical Imaging and Bioengineering","keywords":"Image quality; Magnetic resonance imaging; Medicine; Computer science; Image resolution; Computer vision; Artificial intelligence; Image (mathematics); Radiology","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":[0.001486227,0.001492548,0.002060779,0.003218767,0.0004329463,0.001235933,0.001553245,0.002240064,0.002619554],"category_scores_gemma":[0.004160075,0.0005033572,0.0007507202,0.003200533,0.002345077,0.002011658,0.0009011251,0.002054297,0.002942056],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007332688,"about_ca_system_score_gemma":0.001106728,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001405021,"about_ca_topic_score_gemma":0.001477164,"domain_scores_codex":[0.9983589,0.0002970871,0.0002093697,0.0001604534,0.0009124097,0.0000618177],"domain_scores_gemma":[0.9969828,0.001715685,0.0004916032,0.00008629185,0.0006479541,0.00007578397],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00007157799,0.00004630612,0.0007030661,0.02205474,0.0001124282,0.00364929,0.0002101324,0.0007069148,0.005643913,0.007021223,0.02215399,0.9376263],"study_design_scores_gemma":[0.00003147617,0.0002621215,0.004196753,0.008758918,0.0002666412,0.08215158,0.0003641239,0.0007943085,0.01259309,0.01396114,0.8765074,0.0001124739],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.0004369841,0.9934763,0.002983345,0.0005364221,0.0006437796,0.00001548608,0.000009976931,0.0000350486,0.001862597],"genre_scores_gemma":[0.004610911,0.9875935,0.003819753,0.0006164957,0.001225136,0.0000280378,0.00003212177,0.00001446089,0.002059723],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.003218767,"threshold_uncertainty_score":0.008763313,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02377042610967777,"score_gpt":0.3340081911519576,"score_spread":0.3102377650422798,"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."}}