{"id":"W2044233509","doi":"10.1016/j.neuroimage.2009.08.008","title":"Gradient distortions in MRI: Characterizing and correcting for their effects on SIENA-generated measures of brain volume change","year":2009,"lang":"en","type":"article","venue":"NeuroImage","topic":"Advanced MRI Techniques and Applications","field":"Medicine","cited_by":74,"is_retracted":false,"has_abstract":false,"ca_institutions":"Montreal Neurological Institute and Hospital; McGill University","funders":"Natural Sciences and Engineering Research Council of Canada; Genentech; Canadian Institutes of Health Research; IC Design Education Center; Fonds Québécois de la Recherche sur la Nature et les Technologies; Multiple Sclerosis Society; Biogen","keywords":"Isocenter; Distortion (music); Imaging phantom; Magnetic resonance imaging; Atrophy; Physics; Nuclear magnetic resonance; Nuclear medicine; Medicine; Radiology; Pathology","routes":{"ca_aff":true,"ca_fund":true,"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":[0.002983434,0.001021657,0.0006229072,0.002293115,0.0008030843,0.00239017,0.001142962,0.001258786,0.001508729],"category_scores_gemma":[0.02335506,0.0006934709,0.0006661364,0.002433479,0.0007745672,0.001684436,0.0008948015,0.001515778,0.0007033334],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005644885,"about_ca_system_score_gemma":0.001427014,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004403481,"about_ca_topic_score_gemma":0.009412905,"domain_scores_codex":[0.9990358,0.0002874627,0.00008149305,0.0001822537,0.0003415013,0.00007158869],"domain_scores_gemma":[0.9964578,0.001620188,0.0004539541,0.0006686149,0.0006752166,0.0001242611],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001684504,0.0002514941,0.03670359,0.001485423,0.001159766,0.0006740415,0.001883809,0.05993272,0.2303804,0.01748734,0.005404925,0.6429521],"study_design_scores_gemma":[0.0001483012,0.0006574772,0.1592285,0.0003386155,0.001409938,0.007861636,0.0005782412,0.3807275,0.4045988,0.01846735,0.02551862,0.0004650811],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.266452,0.003515361,0.7204494,0.0005118457,0.0004562203,0.0003823868,0.000785283,0.004222395,0.003225141],"genre_scores_gemma":[0.5262558,0.002343273,0.4647862,0.0002464234,0.0001248456,0.0002921409,0.0009559849,0.002359949,0.002635361],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004403481,"threshold_uncertainty_score":0.01577812,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05829886035678019,"score_gpt":0.312744808865193,"score_spread":0.2544459485084128,"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."}}