{"id":"W2045598508","doi":"10.1016/j.mri.2006.03.015","title":"Synthetic T1-weighted brain image generation with incorporated coil intensity correction using DESPOT1","year":2006,"lang":"en","type":"article","venue":"Magnetic Resonance Imaging","topic":"Advanced MRI Techniques and Applications","field":"Medicine","cited_by":63,"is_retracted":false,"has_abstract":false,"ca_institutions":"Robarts Clinical Trials; Western University","funders":"","keywords":"Normalization (sociology); Electromagnetic coil; Magnetic resonance imaging; Intensity (physics); Nuclear magnetic resonance; SIGNAL (programming language); Computer science; Mathematics; Physics; Artificial intelligence; Optics","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":[0.0001425332,0.0002043553,0.0002496818,0.0001252159,0.0002120285,0.00005350015,0.00006565652,0.00004907403,0.00004224983],"category_scores_gemma":[0.00004199027,0.0001808703,0.00004321101,0.0005308545,0.0001986389,0.0001622072,0.00003066581,0.000203896,0.00001099711],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000178052,"about_ca_system_score_gemma":0.00008181019,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005311289,"about_ca_topic_score_gemma":0.00004188826,"domain_scores_codex":[0.9987492,0.00003315271,0.0002950037,0.0004268724,0.0002122363,0.0002835405],"domain_scores_gemma":[0.9990369,0.0000360903,0.0001438488,0.000373818,0.0003423989,0.00006691117],"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":[0.0002919355,0.000254477,0.01952779,0.00002855129,0.000003083842,0.0001917167,0.00005801765,0.0004917557,0.7965178,0.0004168642,0.01005601,0.172162],"study_design_scores_gemma":[0.001299831,0.0002449222,0.0347755,0.0003441443,0.0001067781,0.001627917,0.00007709551,0.8299506,0.115245,0.0007733764,0.01511426,0.0004406435],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4583106,0.001310625,0.53517,0.001684467,0.0001108224,0.0007404692,0.000006969672,0.0005312603,0.002134747],"genre_scores_gemma":[0.8136786,0.00002552065,0.1841058,0.0004816675,0.0001999658,0.00006833002,0.00007005761,0.0000504219,0.001319659],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8294588,"threshold_uncertainty_score":0.7375675,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01256842140812769,"score_gpt":0.2617777965384925,"score_spread":0.2492093751303648,"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."}}