{"id":"W2023743679","doi":"10.1016/j.media.2008.10.004","title":"Phase unwrapping of MR images using ΦUN – A fast and robust region growing algorithm","year":2008,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Advanced X-ray Imaging Techniques","field":"Physics and Astronomy","cited_by":92,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"Friedrich-Schiller-Universität Jena","keywords":"Algorithm; Computer science; Phase unwrapping; Artificial intelligence; Signal-to-noise ratio (imaging); Image resolution; Noise (video); Phase (matter); Imaging phantom; Computer vision; Image (mathematics); Physics; Optics; Interferometry","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":[0.0002260436,0.0001891331,0.0005108166,0.0003255847,0.000173468,0.0000246874,0.0001870466,0.00004687122,0.0001760759],"category_scores_gemma":[0.00005689302,0.0001775452,0.0002462385,0.0008923779,0.000435193,0.0004826712,0.0001312121,0.0002333262,0.000001307902],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000021927,"about_ca_system_score_gemma":0.0000522367,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004562566,"about_ca_topic_score_gemma":0.000001093171,"domain_scores_codex":[0.9984986,0.0000720671,0.0003973209,0.0003507638,0.0004028845,0.0002784235],"domain_scores_gemma":[0.9991146,0.00008176843,0.0001954278,0.0003011013,0.000126346,0.0001807889],"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":[0.00003920544,0.001765374,0.06277366,0.00009690531,0.004111464,0.0008747203,0.001355804,0.0008305487,0.05084924,0.0003708751,0.001461548,0.8754706],"study_design_scores_gemma":[0.003540735,0.0001423712,0.00100733,0.000316362,0.003311109,0.00008697992,0.001221104,0.883963,0.1025446,0.002407726,0.0004658895,0.0009927884],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1169693,0.0001315079,0.8822605,0.0001816011,0.0000138176,0.00006676817,0.00001285122,0.00006523538,0.0002984582],"genre_scores_gemma":[0.7141212,0.00004731007,0.285465,0.00007130226,0.0001630832,0.000007579984,0.00003684514,0.00002199102,0.00006564179],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8831325,"threshold_uncertainty_score":0.7240085,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02216323376578455,"score_gpt":0.3097367375434367,"score_spread":0.2875735037776521,"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."}}