{"id":"W3138483360","doi":"10.1016/j.neuroimage.2021.118404","title":"Accelerating quantitative susceptibility and R2* mapping using incoherent undersampling and deep neural network reconstruction","year":2021,"lang":"en","type":"article","venue":"NeuroImage","topic":"Advanced MRI Techniques and Applications","field":"Medicine","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; Australian Research Council; Canadian Institutes of Health Research; University of Queensland","keywords":"Quantitative susceptibility mapping; Undersampling; Artificial intelligence; Mean squared error; Iterative reconstruction; Compressed sensing; Computer science; Pattern recognition (psychology); Artificial neural network; Voxel; Mathematics; Algorithm; Magnetic resonance imaging; Statistics; Medicine; Radiology","routes":{"ca_aff":true,"ca_fund":true,"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":[0.00009582775,0.0001065234,0.0001761483,0.00002755915,0.000234134,0.00004819982,0.00001985725,0.00004220257,0.00001496247],"category_scores_gemma":[0.00009489946,0.0001094443,0.00002805999,0.0001786482,0.00009834713,0.0001391021,0.00007989286,0.0002133735,4.550718e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003362383,"about_ca_system_score_gemma":0.00002307617,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001363188,"about_ca_topic_score_gemma":0.000030472,"domain_scores_codex":[0.9991716,0.00004266592,0.0002007891,0.0003464436,0.000069428,0.0001690665],"domain_scores_gemma":[0.9994866,0.000107947,0.00007994721,0.0001674456,0.00008367138,0.00007436564],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00007338197,0.00009888329,0.09239791,0.0002530143,0.00002570178,0.00008892469,0.0006732724,0.003219348,0.7283825,0.004644453,0.00003029499,0.1701123],"study_design_scores_gemma":[0.001917836,0.0003801272,0.1697027,0.0005065204,0.0001958414,0.002755292,0.006166881,0.7885236,0.01265197,0.01578193,0.0007273693,0.000689961],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7916151,0.0003621868,0.2070561,0.000347328,0.00004869043,0.0002329439,0.000002006652,0.00007541316,0.0002602556],"genre_scores_gemma":[0.683452,0.00009719129,0.3161204,0.0002172705,0.00007361847,0.000006851637,0.000008210855,0.00001376376,0.00001069419],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7853042,"threshold_uncertainty_score":0.4463008,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1274052762402658,"score_gpt":0.3611550545495263,"score_spread":0.2337497783092605,"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."}}