{"id":"W2082320904","doi":"10.1109/mmbia.2012.6164765","title":"Reconstruction of HARDI using compressed sensing and its application to contrast HARDI","year":2012,"lang":"en","type":"article","venue":"","topic":"Advanced Neuroimaging Techniques and Applications","field":"Medicine","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Diffusion imaging; Compressed sensing; Contrast (vision); Artificial intelligence; Diffusion MRI; Pattern recognition (psychology); Computer vision; 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":[0.0008489419,0.0007426278,0.0004410305,0.0007500334,0.0002453482,0.0008552024,0.0005763524,0.0007667004,0.001094307],"category_scores_gemma":[0.004619371,0.0002431197,0.0004317941,0.0005965724,0.0008353788,0.0008375116,0.001311746,0.00130038,0.0002686839],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002921952,"about_ca_system_score_gemma":0.0005135454,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009448504,"about_ca_topic_score_gemma":0.0007410856,"domain_scores_codex":[0.999615,0.00009256288,0.00001935407,0.00004995584,0.0001922269,0.00003094404],"domain_scores_gemma":[0.9987979,0.0006621755,0.0001645726,0.0001577073,0.0001570371,0.00006055114],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005838777,0.0001571339,0.002198722,0.0005841848,0.00007459288,0.0009319707,0.0004117482,0.4388036,0.1679761,0.08788119,0.002847536,0.2975493],"study_design_scores_gemma":[0.00001387447,0.00008018123,0.0003360542,0.0000140456,0.000007051539,0.0002841598,0.00002266506,0.9729906,0.01824411,0.00682956,0.001158767,0.00001903576],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03414187,0.0003800204,0.9625729,0.0005369951,0.00005967352,0.00006480101,0.00007819006,0.0001739478,0.001991704],"genre_scores_gemma":[0.3670377,0.0009422989,0.6289459,0.0002154049,0.0001380784,0.0001092061,0.0003341887,0.000110177,0.002167015],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001094307,"threshold_uncertainty_score":0.00448972,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08458415834292106,"score_gpt":0.3608040721497761,"score_spread":0.276219913806855,"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."}}