{"id":"W3001554227","doi":"10.1109/msp.2019.2950640","title":"Deep-Learning Methods for Parallel Magnetic Resonance Imaging Reconstruction: A Survey of the Current Approaches, Trends, and Issues","year":2020,"lang":"en","type":"article","venue":"IEEE Signal Processing Magazine","topic":"Advanced MRI Techniques and Applications","field":"Medicine","cited_by":397,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"H2020 European Research Council; National Institute of Biomedical Imaging and Bioengineering; National Heart, Lung, and Blood Institute; Division of Computing and Communication Foundations; Austrian Science Fund","keywords":"Computer science; Artificial intelligence; Deep learning; Artificial neural network; Machine learning; Interpolation (computer graphics); Iterative reconstruction; k-space; Focus (optics); Image processing; Magnetic resonance imaging; Computer vision; Algorithm; Image (mathematics)","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":[0.001502444,0.0009549677,0.001044619,0.001349531,0.0002619013,0.001577511,0.001113095,0.001264164,0.003796998],"category_scores_gemma":[0.00248875,0.0006977594,0.0007190648,0.002010644,0.0007765471,0.002196608,0.001159893,0.002355287,0.001973636],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007219627,"about_ca_system_score_gemma":0.000809798,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001905966,"about_ca_topic_score_gemma":0.001549234,"domain_scores_codex":[0.9994054,0.000133257,0.00006526229,0.00009532881,0.0002703682,0.00003037949],"domain_scores_gemma":[0.9990935,0.0004825825,0.00005928154,0.00007694067,0.0002497415,0.00003794482],"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":[0.00007767095,0.00009128278,0.0006183223,0.002850675,0.00009952387,0.0001076493,0.0001274497,0.04671987,0.003464861,0.07543004,0.01858586,0.8518268],"study_design_scores_gemma":[0.00004106408,0.00022793,0.0009838835,0.001980097,0.00009970147,0.0007338927,0.0001565613,0.4871145,0.008781206,0.1207059,0.3790429,0.0001323542],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"review","genre_scores_codex":[0.002324647,0.2991565,0.6858115,0.002650974,0.000507066,0.00006517059,0.0001855304,0.0006037486,0.008694888],"genre_scores_gemma":[0.04507913,0.5669166,0.3742784,0.0009946346,0.001984252,0.000184206,0.0007830469,0.0004054679,0.009374144],"genre_candidate":"review","genre_consensus":null,"teacher_disagreement_score":0.003796998,"threshold_uncertainty_score":0.01270223,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07842013683926809,"score_gpt":0.3707677508099101,"score_spread":0.2923476139706421,"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."}}