{"id":"W4410133259","doi":"10.3389/fnimg.2025.1501801","title":"Denoising very low-field magnetic resonance images using native noise modeling","year":2025,"lang":"en","type":"article","venue":"Frontiers in Neuroimaging","topic":"Advanced MRI Techniques and Applications","field":"Medicine","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Noise (video); Computer science; Noise reduction; Imaging phantom; Artificial intelligence; Field (mathematics); Signal-to-noise ratio (imaging); Software portability; Image quality; Computer vision; Pattern recognition (psychology); Mathematics; Image (mathematics); Physics; Optics; Telecommunications","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":[0.0001051581,0.0001662623,0.000270911,0.0003461126,0.0001262716,0.00003095034,0.0001296515,0.000051786,0.000004080914],"category_scores_gemma":[0.0001537074,0.00017737,0.00006451322,0.0005658751,0.00006108826,0.0001767205,0.00009309472,0.0003854611,7.754433e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000171036,"about_ca_system_score_gemma":0.00008795755,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004660261,"about_ca_topic_score_gemma":0.000001056212,"domain_scores_codex":[0.998858,0.00002534006,0.000285374,0.0003945876,0.0001316965,0.0003050503],"domain_scores_gemma":[0.9994528,0.00005145287,0.00004698543,0.0003261992,0.00007247776,0.00005009568],"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":[0.0006778166,0.0005421762,0.1516981,0.0005916763,0.00002756812,0.0006063311,0.0009094038,0.04969351,0.1743038,0.001950419,0.01794896,0.6010502],"study_design_scores_gemma":[0.0009369276,0.00004959389,0.002396626,0.001252147,0.00006635545,0.00003033509,0.0003475297,0.9597157,0.02313933,0.00935977,0.00243031,0.0002753896],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1015366,0.005486384,0.8887118,0.001168281,0.000243537,0.0003955099,0.00000307618,0.0001356908,0.002319158],"genre_scores_gemma":[0.6381971,0.0005434096,0.3586803,0.001985211,0.00006379862,0.00003879901,0.000003402342,0.00003692525,0.0004510274],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9100222,"threshold_uncertainty_score":0.7232939,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01419239929707706,"score_gpt":0.3067871623302574,"score_spread":0.2925947630331803,"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."}}