{"id":"W2605823361","doi":"10.1002/hbm.23622","title":"Contributions of imprecision in<scp>PET</scp>‐<scp>MRI</scp>rigid registration to imprecision in amyloid<scp>PET</scp><scp>SUVR</scp>measurements","year":2017,"lang":"en","type":"article","venue":"Human Brain Mapping","topic":"Advanced Neuroimaging Techniques and Applications","field":"Medicine","cited_by":32,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institute of Biomedical Imaging and Bioengineering; National Institutes of Health; H. Lundbeck A/S; Servier; Eisai; Genentech; IXICO; Elsie and Marvin Dekelboum Family Foundation; Canadian Institutes of Health Research; GHR Foundation; F. Hoffmann-La Roche; Biogen; BioClinica; U.S. Department of Defense; Meso Scale Diagnostics; Alzheimer's Disease Neuroimaging Initiative; Novartis Pharmaceuticals Corporation; Pfizer; Eli Lilly and Company; Bristol-Myers Squibb; National Institute on Aging; Alzheimer's Association; Foundation for the National Institutes of Health","keywords":"Hum; White matter; Orientation (vector space); Artificial intelligence; Positron emission tomography; Image registration; Pattern recognition (psychology); Nuclear medicine; Computer science; Magnetic resonance imaging; Mathematics; Medicine; Radiology; Image (mathematics)","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01012894,0.00116422,0.00124238,0.001032521,0.0007881258,0.001626266,0.001391453,0.001113107,0.0008253149],"category_scores_gemma":[0.04871273,0.001241676,0.001237029,0.001249648,0.00177953,0.001182181,0.001677857,0.00151832,0.000330856],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001367648,"about_ca_system_score_gemma":0.001155559,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00697145,"about_ca_topic_score_gemma":0.005213271,"domain_scores_codex":[0.9932387,0.003173579,0.0006065651,0.001418333,0.001203436,0.0003593871],"domain_scores_gemma":[0.9784243,0.01473404,0.001824951,0.003561109,0.001152039,0.0003036408],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.002766379,0.0002122099,0.06194806,0.0007378132,0.001332564,0.001398925,0.001961578,0.7908154,0.07432259,0.004785877,0.001212106,0.05850652],"study_design_scores_gemma":[0.0002782797,0.001273779,0.1069039,0.0002130986,0.0007320885,0.0030782,0.0009142532,0.732134,0.1288689,0.0184804,0.006580887,0.0005421368],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7639455,0.00142519,0.2303162,0.0005112822,0.0001611856,0.0002057464,0.0005356311,0.001174329,0.001724871],"genre_scores_gemma":[0.97367,0.0001948282,0.02486652,0.0001399784,0.00001362641,0.000108384,0.0003500581,0.0003038851,0.0003528715],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01012894,"threshold_uncertainty_score":0.05356765,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1003363508487368,"score_gpt":0.3802710883922431,"score_spread":0.2799347375435063,"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."}}