{"id":"W4360604116","doi":"10.1016/j.mri.2023.03.014","title":"Efficient approximate signal reconstruction for correction of gradient nonlinearities in diffusion-weighted imaging","year":2023,"lang":"en","type":"article","venue":"Magnetic Resonance Imaging","topic":"Advanced Neuroimaging Techniques and Applications","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université de Sherbrooke","funders":"Eunice Kennedy Shriver National Institute of Child Health and Human Development; National Center for Advancing Translational Sciences; National Institute of Biomedical Imaging and Bioengineering; National Center for Research Resources; National Institute of General Medical Sciences; National Institutes of Health","keywords":"Voxel; Diffusion MRI; Orientation (vector space); Computer science; SIGNAL (programming language); Preprocessor; Weighting; Algorithm; Artificial intelligence; Mathematics; Physics; Magnetic resonance imaging; Geometry","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":[0.0009051338,0.0007425196,0.0007456449,0.0005568197,0.0003939936,0.000982474,0.0009436867,0.001109947,0.002005825],"category_scores_gemma":[0.004356703,0.0005528115,0.0004921033,0.0008682837,0.0005394485,0.001346579,0.0012366,0.001261582,0.0009952697],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004254474,"about_ca_system_score_gemma":0.00120742,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003063232,"about_ca_topic_score_gemma":0.004197542,"domain_scores_codex":[0.9996341,0.0001111139,0.00002156284,0.00003840786,0.0001675286,0.00002721876],"domain_scores_gemma":[0.9991801,0.0004091646,0.00007275868,0.0001323176,0.0001658014,0.00003973394],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0005071972,0.0001151478,0.0009380679,0.000485549,0.0001422819,0.0002423075,0.0002962079,0.3863388,0.091249,0.05224459,0.005184689,0.4622562],"study_design_scores_gemma":[0.000009655034,0.00002569458,0.0001165786,0.00001023485,0.00001102438,0.0001073359,0.00001258697,0.9806468,0.01200564,0.005310548,0.00173254,0.00001140085],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004262342,0.0001580283,0.9950606,0.00005315072,0.00001118844,0.00001255581,0.00002496023,0.0001772642,0.0002398563],"genre_scores_gemma":[0.08543102,0.0004166517,0.9118056,0.00004362109,0.00002370882,0.00005819649,0.0001824266,0.000213727,0.001825117],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003063232,"threshold_uncertainty_score":0.006710112,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02409103062778193,"score_gpt":0.2986922381842282,"score_spread":0.2746012075564462,"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."}}