{"id":"W2046933782","doi":"10.1016/j.neuroimage.2013.06.030","title":"Collaborative patch-based super-resolution for diffusion-weighted images","year":2013,"lang":"en","type":"article","venue":"NeuroImage","topic":"Advanced Neuroimaging Techniques and Applications","field":"Medicine","cited_by":96,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université de Sherbrooke","funders":"Ministerio de Ciencia e Innovación; CHIST-ERA; Agence Nationale de la Recherche","keywords":"Diffusion MRI; Image resolution; Fractional anisotropy; Computer vision; Computer science; Angular resolution (graph drawing); Artificial intelligence; Interpolation (computer graphics); Anisotropic diffusion; Resolution (logic); Diffusion; Image (mathematics); Superresolution; Anisotropy; Tracking (education); Mathematics; Physics; Optics; Magnetic resonance imaging","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00128677,0.0009845463,0.001492948,0.001257958,0.0004884453,0.001038018,0.001606235,0.001507298,0.002611275],"category_scores_gemma":[0.005264841,0.0009659785,0.001336529,0.001739839,0.0006946843,0.001822801,0.00190747,0.001603874,0.001158493],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004083943,"about_ca_system_score_gemma":0.0008701717,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004336809,"about_ca_topic_score_gemma":0.007892474,"domain_scores_codex":[0.9993861,0.0001821501,0.00003459759,0.0001371791,0.0002069062,0.00005308858],"domain_scores_gemma":[0.9981066,0.0009752006,0.0001545807,0.0003764603,0.0002958851,0.00009125641],"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":[0.0004758331,0.0001751406,0.0009828843,0.000599645,0.0004800703,0.0003617631,0.0003784364,0.2883635,0.1036081,0.016961,0.01135641,0.5762572],"study_design_scores_gemma":[0.00001747794,0.00003959241,0.0002997747,0.00001323103,0.00004781694,0.0002357258,0.00001985098,0.9749049,0.01300523,0.008810327,0.002584861,0.00002120838],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003784976,0.0003833862,0.994877,0.00009505982,0.00002690463,0.00002495553,0.0000606967,0.0003373545,0.0004096157],"genre_scores_gemma":[0.08707754,0.0008487873,0.9094735,0.0001171177,0.00009744094,0.00009265235,0.0003159565,0.0002447934,0.001732147],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004336809,"threshold_uncertainty_score":0.008735597,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03252906969361902,"score_gpt":0.3280127986186342,"score_spread":0.2954837289250152,"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."}}