{"id":"W2024391068","doi":"10.1364/boe.5.001664","title":"Hybrid FMT-MRI applied to in vivo atherosclerosis imaging","year":2014,"lang":"en","type":"article","venue":"Biomedical Optics Express","topic":"Optical Imaging and Spectroscopy Techniques","field":"Medicine","cited_by":27,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal; Montreal Heart Institute","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research","keywords":"Molecular imaging; Preclinical imaging; Imaging phantom; In vivo; Biomedical engineering; Magnetic resonance imaging; Fluorescence-lifetime imaging microscopy; Ex vivo; Positron emission tomography; Optical imaging; Materials science; Fluorescence; Radiology; Medicine; Optics; Biology; 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":[0.0006295764,0.0005559156,0.0003486746,0.0004830815,0.0002179763,0.000396302,0.0002871028,0.0008355938,0.001065846],"category_scores_gemma":[0.0004472172,0.0003412123,0.0002667074,0.0002811958,0.0002075704,0.0003816001,0.0004622904,0.0003867406,0.0003633013],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002232056,"about_ca_system_score_gemma":0.0001879395,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005137388,"about_ca_topic_score_gemma":0.0005479099,"domain_scores_codex":[0.999791,0.00006834418,0.00001041672,0.00005522317,0.0000487925,0.00002639256],"domain_scores_gemma":[0.9998604,0.00004689836,0.00002945333,0.00001820769,0.00002538566,0.00001961553],"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":[0.0000690274,0.00001815653,0.000170977,0.00005044689,0.000008529018,0.00009402007,0.00001619586,0.0003836262,0.9934801,0.0001999192,0.00009243874,0.005416594],"study_design_scores_gemma":[0.0000261072,0.000436596,0.002000386,0.00001986464,0.00006589496,0.001187611,0.00001735514,0.02075021,0.9700548,0.0002399187,0.005163146,0.00003808444],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4904544,0.005118805,0.4971457,0.0003816345,0.0001388409,0.0001872908,0.0002363815,0.0009933189,0.005343631],"genre_scores_gemma":[0.7592863,0.001745661,0.234394,0.0002609189,0.0000984885,0.0002301916,0.0001922382,0.0001076776,0.003684389],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001065846,"threshold_uncertainty_score":0.003565609,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009642877453598781,"score_gpt":0.2777413891698859,"score_spread":0.2680985117162871,"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."}}