{"id":"W2175900216","doi":"10.1016/j.media.2015.10.012","title":"Sparse Reconstruction Challenge for diffusion MRI: Validation on a physical phantom to determine which acquisition scheme and analysis method to use?","year":2015,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Advanced Neuroimaging Techniques and Applications","field":"Medicine","cited_by":90,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université de Sherbrooke","funders":"National Cancer Institute; National Institute of Biomedical Imaging and Bioengineering; National Institute of Neurological Disorders and Stroke; National Institute of Mental Health; National Institutes of Health","keywords":"Computer science; Imaging phantom; Diffusion MRI; Artificial intelligence; Set (abstract data type); Data set; Iterative reconstruction; Neuroimaging; Pattern recognition (psychology); Protocol (science); Data mining; Machine learning; Medical physics; Magnetic resonance imaging; Nuclear medicine; Medicine; Radiology","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":[0.0005739021,0.0002009713,0.0006394332,0.0008936729,0.0000997185,0.00005327125,0.0001046123,0.00009602678,0.00006131171],"category_scores_gemma":[0.0009047346,0.0001718376,0.0002608448,0.002939178,0.0000444214,0.000177768,0.00008710661,0.000167067,0.00001443555],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009225272,"about_ca_system_score_gemma":0.00003751083,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005765948,"about_ca_topic_score_gemma":0.00003429358,"domain_scores_codex":[0.9980268,0.00009539791,0.0003529748,0.0006851916,0.0005919914,0.0002475735],"domain_scores_gemma":[0.9979675,0.0002490978,0.0001114864,0.0004934432,0.0004508287,0.0007276612],"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":[0.002251348,0.003601394,0.04120982,0.0001874231,0.006034451,0.0001018205,0.00202632,0.0009840211,0.1708895,0.001377573,0.004361139,0.7669752],"study_design_scores_gemma":[0.002282139,0.001421889,0.02051784,0.0001411565,0.015104,0.00004908224,0.0001812818,0.9191501,0.03371868,0.001602192,0.005229607,0.0006021073],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3222193,0.000005898444,0.6687893,0.008342382,0.00001303039,0.0004364467,0.00002659336,0.000101679,0.00006535399],"genre_scores_gemma":[0.5740762,0.00003646149,0.4237626,0.001229503,0.0001975622,0.0002689073,0.0002777956,0.00002414452,0.0001268822],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.918166,"threshold_uncertainty_score":0.7007335,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1068028164072613,"score_gpt":0.4220687880391478,"score_spread":0.3152659716318865,"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."}}