{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003166306,0.0001824987,0.0005245471,0.0003381218,0.00006264724,0.00002034484,0.0001583631,0.0002659021,0.000002946369],"category_scores_gemma":[0.0002976087,0.000163771,0.0001018956,0.0004482392,0.0008493579,0.00005907612,0.00006499774,0.0001888413,7.075183e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008716997,"about_ca_system_score_gemma":0.00007684351,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000561212,"about_ca_topic_score_gemma":0.000004198417,"domain_scores_codex":[0.9982141,0.0000260212,0.0006536195,0.0003619395,0.000402356,0.0003420255],"domain_scores_gemma":[0.9988732,0.0001224518,0.0001348648,0.0003811104,0.0002684762,0.0002199228],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001105658,0.00701176,0.007745627,0.001093423,0.0000737615,0.000005206193,0.001180146,0.0002280991,0.9407951,0.02318649,0.00118759,0.01638713],"study_design_scores_gemma":[0.0008412282,0.0007080912,0.00007451631,0.0003646153,0.00008843531,0.000005425884,0.00006560696,0.8591642,0.1360621,0.001707432,0.0007645462,0.000153807],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0839841,0.00004623384,0.9142962,0.000272752,0.00003151285,0.0007414588,0.0000178198,0.0002049915,0.0004050035],"genre_scores_gemma":[0.6490524,0.00001060901,0.3505412,0.0001086345,0.0001149308,0.00007594109,0.00005672956,0.00002654311,0.000013045],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8589361,"threshold_uncertainty_score":0.6678387,"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."}}