{"id":"W2803916235","doi":"10.1016/j.neuroimage.2018.05.047","title":"Microstructural imaging of the human brain with a ‘super-scanner’: 10 key advantages of ultra-strong gradients for diffusion MRI","year":2018,"lang":"en","type":"review","venue":"NeuroImage","topic":"Advanced Neuroimaging Techniques and Applications","field":"Medicine","cited_by":206,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"Engineering and Physical Sciences Research Council; Medical Research Council; Medical Research Council Canada; Wellcome Trust; Nederlandse Organisatie voor Wetenschappelijk Onderzoek; Cancer Research UK","keywords":"Diffusion MRI; Scanner; Diffusion; Tractography; Materials science; Anisotropic diffusion; Computer science; Biomedical engineering; Nuclear magnetic resonance; Anisotropy; Magnetic resonance imaging; Artificial intelligence; Optics; Physics; Radiology; Medicine","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":[0.001341838,0.001222027,0.001368257,0.002947229,0.0002523984,0.001570798,0.001378322,0.001919174,0.001877419],"category_scores_gemma":[0.001580585,0.0005079272,0.0005597496,0.002422213,0.0013665,0.003010193,0.00116898,0.002741746,0.001410003],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005852551,"about_ca_system_score_gemma":0.001537419,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001371352,"about_ca_topic_score_gemma":0.002482552,"domain_scores_codex":[0.9997025,0.00005510938,0.00003926384,0.00005681408,0.0001187731,0.00002746003],"domain_scores_gemma":[0.9988812,0.0006903923,0.0001048577,0.0000369108,0.0002347996,0.00005178638],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00007569559,0.00005165389,0.0002179913,0.0179947,0.00009998758,0.0002179011,0.0000825859,0.0003520144,0.004505189,0.007713877,0.01971805,0.9489703],"study_design_scores_gemma":[0.00002053255,0.0001145247,0.001540856,0.004109362,0.0001730598,0.003252079,0.00008953327,0.0003216996,0.003299352,0.00773136,0.979273,0.00007470532],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.000122858,0.9981781,0.0005240329,0.0003805036,0.0001397279,0.00000370821,0.00001462155,0.00001040531,0.0006261062],"genre_scores_gemma":[0.0009749751,0.9970349,0.0009791019,0.0002768955,0.0003627045,0.000006977381,0.00002963548,0.000006158445,0.0003287011],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.002947229,"threshold_uncertainty_score":0.00709641,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05194362109467095,"score_gpt":0.3765746836947506,"score_spread":0.3246310626000797,"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."}}