{"id":"W4225947718","doi":"10.1016/j.neuroimage.2022.119410","title":"Instant tissue field and magnetic susceptibility mapping from MR raw phase using Laplacian enabled deep neural networks","year":2021,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced MRI Techniques and Applications","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Australian Research Council; Canadian Institutes of Health Research","keywords":"Quantitative susceptibility mapping; Computer science; Artificial neural network; Artificial intelligence; Pipeline (software); Pattern recognition (psychology); Susceptibility weighted imaging; Magnetic resonance imaging; Radiology; Medicine","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":[0.0004762657,0.0005045384,0.000249732,0.0004345483,0.0001520757,0.00039838,0.0006644777,0.0005068185,0.00104381],"category_scores_gemma":[0.001717377,0.0002481066,0.0003874187,0.0004308632,0.0002980192,0.0007439255,0.0008036662,0.0008244404,0.0002907862],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004696453,"about_ca_system_score_gemma":0.0006926298,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003400817,"about_ca_topic_score_gemma":0.00537027,"domain_scores_codex":[0.9998859,0.00002655365,0.000005280927,0.00002542183,0.00004322345,0.00001362709],"domain_scores_gemma":[0.9997661,0.0001000514,0.0000304411,0.00003261391,0.00005603768,0.00001486289],"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":[0.0002174692,0.00008806516,0.001146634,0.0001447967,0.00008016353,0.0001691589,0.0001151707,0.5392792,0.04610907,0.007745819,0.003122342,0.4017821],"study_design_scores_gemma":[0.000004223268,0.00001754417,0.0001406035,0.000003978536,0.000005271388,0.00002878021,0.000005231827,0.9927371,0.004366039,0.002275704,0.0004100501,0.000005493391],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0276328,0.0002155938,0.969804,0.0001932089,0.0000260564,0.00003176114,0.00009811308,0.0009079382,0.001090453],"genre_scores_gemma":[0.5093158,0.0003564191,0.4854005,0.0001968136,0.00003923279,0.00009338974,0.0004753737,0.0001828547,0.003939574],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003400817,"threshold_uncertainty_score":0.006762028,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06811692217574883,"score_gpt":0.2508040981851666,"score_spread":0.1826871760094178,"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."}}