{"id":"W2470264972","doi":"10.1109/tip.2016.2588328","title":"Multi-Tissue Decomposition of Diffusion MRI Signals via L0 Sparse-Group Estimation","year":2016,"lang":"en","type":"article","venue":"IEEE Transactions on Image Processing","topic":"Advanced Neuroimaging Techniques and Applications","field":"Medicine","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Center for Advancing Translational Sciences; National Institute of Biomedical Imaging and Bioengineering; National Institute of Neurological Disorders and Stroke; National Institute of Mental Health; National Institute on Alcohol Abuse and Alcoholism; National Institute on Aging; University of North Carolina at Chapel Hill; National Institutes of Health; Simon Fraser University","keywords":"Deconvolution; Algorithm; Voxel; Diffusion MRI; Robustness (evolution); Matrix decomposition; Computer science; Mathematics; Mathematical optimization; Artificial intelligence; Magnetic resonance imaging","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008822884,0.0001657074,0.0002194516,0.0001864895,0.0001983359,0.00001919291,0.00007855981,0.00006834461,0.00006147943],"category_scores_gemma":[0.000007832815,0.0001280161,0.00007409337,0.0002561107,0.0001232001,0.0003677988,0.000001683824,0.0001420633,0.00002417015],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007320286,"about_ca_system_score_gemma":0.00002930571,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007891798,"about_ca_topic_score_gemma":0.000001667829,"domain_scores_codex":[0.9989297,0.00002457506,0.0003422096,0.0003214473,0.0002011287,0.0001809042],"domain_scores_gemma":[0.9992602,0.00006521957,0.0001693501,0.0002657709,0.0001525244,0.00008692396],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00005133009,0.000325594,0.000007343352,0.00007214722,0.000004257729,0.00000261527,0.00003161866,0.0001316673,0.6578392,0.000002593697,0.0000106033,0.341521],"study_design_scores_gemma":[0.0009684635,0.0002401235,0.0003379059,0.0006951185,0.00008985111,0.00006705611,0.00001017326,0.03808189,0.9587433,0.0004150507,0.000200572,0.0001504584],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0191794,0.00003731844,0.9788823,0.0009772348,0.00004330454,0.0004722619,0.00002015456,0.0003108217,0.0000771702],"genre_scores_gemma":[0.6742855,0.00005378597,0.3253281,0.00008773972,0.00001693886,0.00007882391,0.000004826943,0.00002797631,0.0001163045],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6551061,"threshold_uncertainty_score":0.5220345,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04059078803748986,"score_gpt":0.3646835081929801,"score_spread":0.3240927201554903,"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."}}