{"id":"W2894333093","doi":"10.1016/j.neuroimage.2018.09.076","title":"Towards microstructure fingerprinting: Estimation of tissue properties from a dictionary of Monte Carlo diffusion MRI simulations","year":2018,"lang":"en","type":"article","venue":"NeuroImage","topic":"Advanced Neuroimaging Techniques and Applications","field":"Medicine","cited_by":44,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"National Institute of Neurological Disorders and Stroke; National Institutes of Health; National Institute of Biomedical Imaging and Bioengineering; Foulkes Foundation; Natural Sciences and Engineering Research Council of Canada; Royal College of Psychiatrists","keywords":"Monte Carlo method; Human Connectome Project; Diffusion MRI; Computer science; Ground truth; Voxel; Algorithm; Diffusion; Statistical physics; Artificial intelligence; Biological system; Magnetic resonance imaging; Physics; Mathematics; Statistics; Radiology; Neuroscience; Biology","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001247465,0.0006809835,0.001121338,0.001108701,0.0003781307,0.001093561,0.001394423,0.001727218,0.001107468],"category_scores_gemma":[0.006587206,0.001047862,0.0009355217,0.0009091924,0.0008451749,0.001378148,0.001268118,0.001837155,0.000784179],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000497214,"about_ca_system_score_gemma":0.001779663,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004286453,"about_ca_topic_score_gemma":0.004554485,"domain_scores_codex":[0.9996847,0.0001114278,0.00002398371,0.00005664816,0.00009333505,0.00002993816],"domain_scores_gemma":[0.9980135,0.0009568185,0.0002331531,0.0003409781,0.0003241888,0.0001312417],"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":[0.0001962525,0.0001132227,0.001992351,0.0002101752,0.00009100132,0.00008970298,0.0001496063,0.8077125,0.02228024,0.02619983,0.002033982,0.1389313],"study_design_scores_gemma":[0.000005859978,0.000009284312,0.00009734149,0.00000661965,0.000004895606,0.00001889033,0.000004153686,0.9944958,0.0009049862,0.004086064,0.0003606673,0.000005333382],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007903244,0.00008009598,0.9914277,0.00008398855,0.0000103194,0.00002097198,0.00004818344,0.0002096515,0.0002157995],"genre_scores_gemma":[0.2160064,0.0006920904,0.7809153,0.0001355491,0.00006713419,0.0001633971,0.0004061824,0.0003014072,0.001312518],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004286453,"threshold_uncertainty_score":0.008523047,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04307607797040159,"score_gpt":0.3293704997543542,"score_spread":0.2862944217839526,"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."}}