{"id":"W2164225814","doi":"10.1016/j.neuroimage.2007.10.026","title":"Intensity non-uniformity correction using N3 on 3-T scanners with multichannel phased array coils","year":2007,"lang":"en","type":"article","venue":"NeuroImage","topic":"Advanced MRI Techniques and Applications","field":"Medicine","cited_by":144,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University; Montreal Neurological Institute and Hospital","funders":"National Institute on Aging; National Institutes of Health; U.S. Food and Drug Administration; Foundation for the National Institutes of Health","keywords":"Standard deviation; Intensity (physics); Kurtosis; Smoothing; Flip angle; Voxel; Nuclear medicine; Normalization (sociology); Neuroimaging; Mathematics; Materials science; Nuclear magnetic resonance; Biomedical engineering; Physics; Magnetic resonance imaging; Computer science; Optics; Artificial intelligence; Medicine; Statistics; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001277803,0.0001463128,0.0001930453,0.0001021246,0.0001442078,0.00001050148,0.00005339897,0.00005717381,0.00001589351],"category_scores_gemma":[0.00005232123,0.00012331,0.00004984677,0.0002601953,0.00009719365,0.00008535686,0.000007902508,0.0003018933,0.00001199995],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001002305,"about_ca_system_score_gemma":0.00003265054,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007773075,"about_ca_topic_score_gemma":0.00001976398,"domain_scores_codex":[0.9991153,0.000006921248,0.0001643543,0.0002901284,0.00017108,0.000252197],"domain_scores_gemma":[0.9992372,0.00004454821,0.00009305149,0.0003280181,0.0001577592,0.0001394688],"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":[0.000921036,0.0003487866,0.001145977,0.00001634255,0.00000673509,0.00006408501,0.00009198471,0.0004249132,0.9903932,0.0000271368,0.0004962172,0.006063628],"study_design_scores_gemma":[0.001832327,0.001073649,0.02118346,0.000175225,0.00008968195,0.0003012031,0.0002758965,0.007262977,0.962813,0.00006457023,0.004641269,0.0002867549],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4172149,0.000002156635,0.5728782,0.000187533,0.0001244661,0.0004440679,0.000004500795,0.0001762076,0.008967996],"genre_scores_gemma":[0.9658168,0.000005786346,0.03256317,0.001028555,0.0001167865,0.000009364584,0.00001336809,0.00003154185,0.0004146086],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5486019,"threshold_uncertainty_score":0.5028434,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03083813653025838,"score_gpt":0.3302914818699821,"score_spread":0.2994533453397237,"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."}}