{"id":"W2807014429","doi":"10.1364/boe.9.002994","title":"Comparison of source localization techniques in diffuse optical tomography for fNIRS application using a realistic head model","year":2018,"lang":"en","type":"article","venue":"Biomedical Optics Express","topic":"Optical Imaging and Spectroscopy Techniques","field":"Medicine","cited_by":33,"is_retracted":false,"has_abstract":true,"ca_institutions":"Hôpital Notre-Dame; Université de Montréal; Polytechnique Montréal; Centre Hospitalier Universitaire Sainte-Justine","funders":"Fonds de Recherche du Québec - Santé; Natural Sciences and Engineering Research Council of Canada; Fonds de recherche du Québec – Nature et technologies; Canadian Institutes of Health Research","keywords":"Diffuse optical imaging; Tikhonov regularization; Inverse problem; Computer science; Iterative reconstruction; Singular value decomposition; Tomography; Bayesian probability; Functional near-infrared spectroscopy; Algorithm; Optical tomography; Regularization (linguistics); Monte Carlo method; Artificial intelligence; Computer vision; Optics; Mathematics; Physics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0008224886,0.0006353717,0.0004355216,0.0006110735,0.0002190712,0.0007018205,0.0004890565,0.0008466613,0.0005147787],"category_scores_gemma":[0.003642856,0.0002374779,0.0005702187,0.000445214,0.0003009611,0.0006074623,0.0004618464,0.0003730251,0.0001631158],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004032322,"about_ca_system_score_gemma":0.0006075563,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005900716,"about_ca_topic_score_gemma":0.003644589,"domain_scores_codex":[0.9996406,0.0001351343,0.00002000158,0.00004607896,0.0001271475,0.00003097211],"domain_scores_gemma":[0.9989539,0.0006334673,0.0000747808,0.00009149569,0.0002095312,0.00003672847],"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":[0.001037411,0.0002079088,0.003371314,0.0005078555,0.0001772845,0.0003757615,0.000314006,0.7918333,0.0658205,0.003450321,0.0007583207,0.1321459],"study_design_scores_gemma":[0.00002052463,0.0001724364,0.001629323,0.00002097969,0.00003689392,0.0001621682,0.00005438365,0.9852532,0.01146186,0.0005097807,0.0006468158,0.00003170301],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3965494,0.001416453,0.5970265,0.0003938304,0.0000630155,0.0001038485,0.0002215114,0.0009369274,0.003288578],"genre_scores_gemma":[0.8426562,0.001321344,0.1546205,0.00005970165,0.00001529772,0.00008083274,0.0002891716,0.0001089342,0.0008481194],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005900716,"threshold_uncertainty_score":0.01173276,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04266849999153628,"score_gpt":0.4035846371876157,"score_spread":0.3609161371960794,"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."}}