{"id":"W3213610673","doi":"10.1016/j.neuroimage.2021.118738","title":"Advances in spiral fMRI: A high-resolution study with single-shot acquisition","year":2021,"lang":"en","type":"article","venue":"NeuroImage","topic":"Advanced MRI Techniques and Applications","field":"Medicine","cited_by":36,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"René und Susanne Braginsky Stiftung; Universität Zürich; McGill University; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung; Eidgenössische Technische Hochschule Zürich; Wellcome Trust","keywords":"Spiral (railway); Computer science; Artificial intelligence; Computer vision; Image quality; Field of view; Ghosting; Image resolution; Temporal resolution; Physics; Optics; Mathematics; 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":[0.001987651,0.0008197942,0.0007530729,0.0007912834,0.0001884703,0.0008229269,0.0007718509,0.00101387,0.001569576],"category_scores_gemma":[0.002450093,0.0006333035,0.0004677829,0.0007010855,0.0005429508,0.001363124,0.0008773617,0.0008440622,0.0006956519],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004170048,"about_ca_system_score_gemma":0.0006254385,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004387531,"about_ca_topic_score_gemma":0.0006146011,"domain_scores_codex":[0.9995735,0.0001527628,0.00003369054,0.0001075426,0.00009824016,0.00003425861],"domain_scores_gemma":[0.9989779,0.0003746286,0.000140979,0.0001751043,0.0002418254,0.0000895863],"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":[0.0003587949,0.00006690332,0.001930141,0.00142031,0.0001602002,0.000515598,0.0002418586,0.002635262,0.8156827,0.01111007,0.001874101,0.164004],"study_design_scores_gemma":[0.0001933945,0.003346877,0.01795734,0.0007645488,0.0006024356,0.01518893,0.0002512288,0.08420675,0.7189952,0.03402531,0.1241405,0.0003274736],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08318833,0.04769359,0.8560675,0.002668422,0.0003812765,0.0001908084,0.0004057418,0.001100998,0.008303344],"genre_scores_gemma":[0.3622604,0.03913426,0.5925158,0.0007259796,0.000854174,0.0003283976,0.0007649427,0.0003288063,0.003087213],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001987651,"threshold_uncertainty_score":0.01051182,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0306278590952605,"score_gpt":0.3191865438145952,"score_spread":0.2885586847193347,"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."}}