{"id":"W2114448824","doi":"10.1016/j.neuroimage.2013.05.022","title":"Diffusion imaging quality control via entropy of principal direction distribution","year":2013,"lang":"en","type":"article","venue":"NeuroImage","topic":"Advanced Neuroimaging Techniques and Applications","field":"Medicine","cited_by":20,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University","funders":"Eunice Kennedy Shriver National Institute of Child Health and Human Development; National Institute on Drug Abuse; National Institute of Biomedical Imaging and Bioengineering; National Institute of Neurological Disorders and Stroke; National Institute of Mental Health; University of Washington","keywords":"Diffusion MRI; Computer science; Artificial intelligence; Voxel; Image quality; Entropy (arrow of time); Computer vision; Pattern recognition (psychology); Physics; Magnetic resonance imaging; Medicine; 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":[0.006232662,0.0009454137,0.001235953,0.002097215,0.0006577537,0.002521557,0.00104475,0.0009782917,0.001148744],"category_scores_gemma":[0.02401542,0.0006854466,0.000900804,0.001369124,0.001566617,0.003768123,0.002512167,0.001974398,0.0003133522],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001033086,"about_ca_system_score_gemma":0.002361454,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002315137,"about_ca_topic_score_gemma":0.002526823,"domain_scores_codex":[0.9974572,0.0009106585,0.0002177876,0.0005003136,0.0007322771,0.0001817552],"domain_scores_gemma":[0.9880922,0.006499119,0.00121737,0.001514076,0.002295415,0.0003819696],"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":[0.00104823,0.0002120543,0.008910809,0.000445024,0.0003989538,0.0001733385,0.0003100498,0.4160029,0.03961596,0.1029041,0.003066127,0.4269125],"study_design_scores_gemma":[0.00002914035,0.0000966001,0.002140492,0.00002185947,0.00003543161,0.00009986717,0.0000191434,0.9628703,0.008943384,0.0250277,0.0006745401,0.00004154627],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01501507,0.0002599972,0.9839985,0.0001496908,0.00002363854,0.0000226083,0.00006583064,0.0001905376,0.0002740478],"genre_scores_gemma":[0.5258769,0.0009023545,0.4701839,0.0001084338,0.0002189164,0.0001134332,0.0005521819,0.0004476514,0.001596122],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006232662,"threshold_uncertainty_score":0.03296185,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03213628797906214,"score_gpt":0.3387790987969183,"score_spread":0.3066428108178561,"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."}}