{"id":"W4402354923","doi":"10.1002/mrm.30247","title":"Rational approximation of golden angles: Accelerated reconstructions for radial MRI","year":2024,"lang":"en","type":"article","venue":"Magnetic Resonance in Medicine","topic":"Advanced Neuroimaging Techniques and Applications","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"National Institute of Biomedical Imaging and Bioengineering; Deutsche Forschungsgemeinschaft; National Institute on Aging; National Institutes of Health; National Institute of Neurological Disorders and Stroke; National Institute of General Medical Sciences; Deutsches Zentrum für Herz-Kreislaufforschung","keywords":"Golden ratio; Sampling (signal processing); Precomputation; Equidistant; Computer science; Algorithm; Imaging phantom; Mathematics; Golden hamster; Artificial intelligence; Computer vision; Computation; Optics; Geometry; Physics","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":[0.001198595,0.0006109165,0.000315197,0.0004935862,0.000181446,0.0006991563,0.0005965778,0.0004630112,0.001573247],"category_scores_gemma":[0.003404864,0.0002794389,0.0003809242,0.000405259,0.0006031533,0.0006906301,0.0007237045,0.0006812458,0.0008310747],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004592549,"about_ca_system_score_gemma":0.0005166915,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007371409,"about_ca_topic_score_gemma":0.000899299,"domain_scores_codex":[0.9996256,0.0001626405,0.00002008604,0.00003641182,0.0001302102,0.00002492449],"domain_scores_gemma":[0.9992342,0.0003108379,0.0001297985,0.0001464887,0.0001295771,0.00004905948],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000648221,0.00009085483,0.002260784,0.000312874,0.00005347032,0.0003795181,0.0004206973,0.4200675,0.1037067,0.201457,0.002645569,0.2679568],"study_design_scores_gemma":[0.00001180786,0.00004621458,0.0001893784,0.00001617321,0.000005589823,0.0001650668,0.00001047668,0.9731675,0.01386414,0.009216945,0.003292914,0.00001372459],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006994937,0.00008928731,0.9919768,0.00002752387,0.00001096442,0.00001454601,0.00001447738,0.0001663479,0.0007051561],"genre_scores_gemma":[0.1218324,0.0002207502,0.8766764,0.00003209984,0.00001645317,0.00004778513,0.00007486962,0.0001757398,0.0009235101],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001573247,"threshold_uncertainty_score":0.006338835,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08137913570329844,"score_gpt":0.3705281476296144,"score_spread":0.2891490119263159,"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."}}