{"id":"W4226157186","doi":"","title":"Imaging of the pial arterial vasculature of the human brain in vivo using high-resolution 7T time-of-flight angiography.","year":2022,"lang":"en","type":"article","venue":"DZNE Pub","topic":"Advanced MRI Techniques and Applications","field":"Medicine","cited_by":60,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institute of Biomedical Imaging and Bioengineering; National Institute of Neurological Disorders and Stroke; National Institute of Mental Health; European Commission; Natural Sciences and Engineering Research Council of Canada; National Institutes of Health; National Institute on Aging; Deutsche Forschungsgemeinschaft; Fonds de recherche du Québec – Nature et technologies","keywords":"Magnetic resonance imaging; Voxel; Magnetic resonance angiography; Angiography; Cerebral circulation; Cerebral arteries; Blood flow; Human brain; Biomedical engineering; Medicine; Radiology; Cardiology","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001552634,0.00006426476,0.0001550102,0.00008236484,0.0001080824,0.000002110179,0.0001509536,0.00002633151,0.00009420267],"category_scores_gemma":[0.0000148143,0.00004351969,0.0002006512,0.0005087064,0.0001158401,0.00003019528,0.0001350147,0.0001406054,7.650136e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003652117,"about_ca_system_score_gemma":0.00002827568,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001676471,"about_ca_topic_score_gemma":0.000008174556,"domain_scores_codex":[0.9993277,0.00005036761,0.0002159913,0.0001116767,0.0001912616,0.0001030266],"domain_scores_gemma":[0.9994054,0.00001840229,0.0001715333,0.0003528153,0.00003777322,0.00001407318],"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":[0.00003055949,0.0001433393,0.005056267,0.00002000768,0.00001488157,5.17229e-7,0.0002630321,0.000694062,0.9871932,0.004822168,0.001471747,0.0002902508],"study_design_scores_gemma":[0.005139864,0.000257155,0.3348646,0.0005287487,0.000519006,0.00006844652,0.0006156646,0.007403735,0.5942432,0.02085542,0.03503007,0.0004740833],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9950224,0.0001503562,0.001984853,0.001706814,0.00009583229,0.000561021,0.00007378694,0.00002123889,0.0003837079],"genre_scores_gemma":[0.9977832,0.000001464674,0.001923932,0.0001009308,0.00007038857,0.00002768337,0.000009917521,0.00001060391,0.00007181094],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3929499,"threshold_uncertainty_score":0.1774681,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009780328751603026,"score_gpt":0.2729446315749552,"score_spread":0.2631643028233522,"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."}}