{"id":"W2038277297","doi":"10.1118/1.3583814","title":"Spin‐history artifact during functional MRI: Potential for adaptive correction","year":2011,"lang":"en","type":"article","venue":"Medical Physics","topic":"Advanced MRI Techniques and Applications","field":"Medicine","cited_by":51,"is_retracted":false,"has_abstract":true,"ca_institutions":"Baycrest Hospital; Health Sciences Centre; Profound Medical (Canada); University of Toronto; Heart and Stroke Foundation; Sunnybrook Health Science Centre","funders":"","keywords":"Artifact (error); Imaging phantom; Computer science; Computer vision; Artificial intelligence; SIGNAL (programming language); Functional magnetic resonance imaging; Amplitude; Magnetic resonance imaging; Physics; Nuclear magnetic resonance; Optics; Medicine","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":[0.000894769,0.0004227369,0.0001923703,0.0002044669,0.0001905855,0.0004544801,0.0007075264,0.0004744595,0.0007243583],"category_scores_gemma":[0.006637474,0.0001952971,0.0002334711,0.0002800884,0.0005552063,0.0006144801,0.0002994621,0.0004152352,0.0001474873],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003974921,"about_ca_system_score_gemma":0.000776751,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002187551,"about_ca_topic_score_gemma":0.003145197,"domain_scores_codex":[0.9998009,0.00006787944,0.00001274442,0.00002870389,0.00007350915,0.0000162473],"domain_scores_gemma":[0.9984079,0.0009421662,0.0002065522,0.000187837,0.0002138058,0.00004182486],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0006309983,0.0001857199,0.01668012,0.0006431007,0.0001739689,0.0007255293,0.0005117236,0.1893358,0.5208603,0.009324293,0.0014883,0.2594402],"study_design_scores_gemma":[0.0001021019,0.0006390397,0.01590482,0.0001062829,0.0001484749,0.001063214,0.00007100942,0.7359672,0.2278053,0.008377851,0.009708032,0.0001067209],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2535825,0.002185949,0.7389044,0.0008493676,0.0001694491,0.0001410114,0.00005108331,0.001628499,0.002487853],"genre_scores_gemma":[0.8482996,0.0009393728,0.1490194,0.000205809,0.00005952053,0.0000787403,0.00007703241,0.0002077243,0.00111279],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002187551,"threshold_uncertainty_score":0.004732072,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04705663853597609,"score_gpt":0.2859670713820066,"score_spread":0.2389104328460305,"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."}}