{"id":"W4327703319","doi":"10.1016/j.mri.2023.03.003","title":"Optimization in the space domain for density compensation with the nonuniform FFT","year":2023,"lang":"en","type":"article","venue":"Magnetic Resonance Imaging","topic":"Advanced MRI Techniques and Applications","field":"Medicine","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"Western University","funders":"National Heart, Lung, and Blood Institute; National Institutes of Health; National Institute of Biomedical Imaging and Bioengineering; University of California, San Francisco; American Heart Association","keywords":"Fourier transform; Imaging phantom; k-space; Fast Fourier transform; Compensation (psychology); Point (geometry); Discrete Fourier transform (general); Algorithm; Mathematics; Set (abstract data type); Image quality; Fourier analysis; Computer science; Image (mathematics); Mathematical analysis; Computer vision; Optics; Physics; Geometry; Short-time Fourier transform","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003083734,0.0000776966,0.0000899549,0.00004573785,0.0001608937,0.00002599643,0.0001002362,0.00001584523,0.000007574893],"category_scores_gemma":[0.00002606262,0.00004347449,0.00002339187,0.0004623872,0.00008337827,0.00005098743,0.00001952388,0.00009762431,0.00000441957],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003184797,"about_ca_system_score_gemma":0.00002256395,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002818811,"about_ca_topic_score_gemma":0.00003844232,"domain_scores_codex":[0.9994038,0.00001807333,0.0001070079,0.0001546967,0.0001427878,0.000173661],"domain_scores_gemma":[0.9994696,0.0001291905,0.00004463152,0.0002765208,0.00006335181,0.00001673838],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001168672,0.000474646,0.08852759,0.000280316,0.00001015845,0.0001666007,0.01169754,0.05960898,0.01109022,0.137935,0.0682413,0.620799],"study_design_scores_gemma":[0.001982481,0.0002232385,0.2018853,0.0001769207,0.00004527489,0.0001071478,0.003662601,0.5693509,0.0004768003,0.005845241,0.2160141,0.0002301036],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05427745,0.0005343886,0.8663548,0.07377459,0.00002443981,0.002462204,0.000006807094,0.0001792146,0.002386155],"genre_scores_gemma":[0.6796153,0.0002120553,0.3151535,0.002512377,0.0001289575,0.00103492,0.00007810232,0.00003857553,0.001226254],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6253378,"threshold_uncertainty_score":0.1772838,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01226358184022595,"score_gpt":0.2816680891642888,"score_spread":0.2694045073240628,"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."}}