{"id":"W2040525075","doi":"10.1117/12.766745","title":"Diffuse optical-MRI fusion and applications","year":2008,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Optical Imaging and Spectroscopy Techniques","field":"Medicine","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Functional near-infrared spectroscopy; Image resolution; Computer science; Optical imaging; Hemoglobin; Modality (human–computer interaction); Optode; Absorption (acoustics); Diffuse optical imaging; SIGNAL (programming language); Limiting; Biomedical engineering; Partial volume; Diffusion; Materials science; Nuclear magnetic resonance; Artificial intelligence; Optics; Neuroscience; Physics; Medicine; Iterative reconstruction; Internal medicine; Biology; Fluorescence","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":[0.0003249536,0.0002867542,0.0004726104,0.0001107849,0.0001349508,0.00005602735,0.0003942555,0.0001920543,0.0000106745],"category_scores_gemma":[0.0003646792,0.0002265379,0.0004304463,0.0002897364,0.0005452388,0.0002874809,0.0001651701,0.0003981107,0.000001996178],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001127026,"about_ca_system_score_gemma":0.00003687921,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006254206,"about_ca_topic_score_gemma":3.706176e-8,"domain_scores_codex":[0.9980457,8.347516e-9,0.0005719215,0.0004139483,0.0005985472,0.0003698527],"domain_scores_gemma":[0.9981979,0.0001261504,0.0001909399,0.00008058954,0.001179014,0.0002253767],"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":[0.00009701648,0.0003137447,0.00145488,0.000391399,0.0001885789,3.031079e-7,0.0001001606,0.000001898942,0.5872982,0.4068117,0.002929627,0.0004124335],"study_design_scores_gemma":[0.005524979,0.002063379,0.01378273,0.001147591,0.0009389783,0.0006032574,0.001385099,0.02762965,0.9063224,0.007029328,0.03240841,0.00116423],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9860274,0.0001695079,0.0003755382,0.005057625,0.00005777136,0.0008069776,0.00001854118,0.0001988925,0.007287744],"genre_scores_gemma":[0.5441377,0.001156551,0.4523928,0.0003768681,0.0006372268,0.0004249866,0.00001452416,0.00009455687,0.0007647714],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4520173,"threshold_uncertainty_score":0.9237948,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01129864350086506,"score_gpt":0.2549469858522054,"score_spread":0.2436483423513403,"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."}}