{"id":"W4380853142","doi":"10.1155/2023/5366733","title":"Enhanced Extraction of Blood and Tissue Time-Activity Curves in Cardiac Mouse FDG PET Imaging by Means of Constrained Nonnegative Matrix Factorization","year":2023,"lang":"en","type":"article","venue":"International Journal of Biomedical Imaging","topic":"Advanced MRI Techniques and Applications","field":"Medicine","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Sherbrooke","funders":"Fonds de Recherche du Québec - Santé; Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research","keywords":"Positron emission tomography; Ventricle; Voxel; Non-negative matrix factorization; Nuclear medicine; Myocardial infarction; Matrix (chemical analysis); Medicine; Matrix decomposition; Algorithm; Cardiology; Computer science; Chemistry; Physics; Radiology; Chromatography","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":[0.0003360809,0.00009244589,0.0002994665,0.000346587,0.00001665656,0.000008208298,0.0000952062,0.00002495582,0.00004090288],"category_scores_gemma":[0.0002675869,0.00008472897,0.00006543877,0.0002724651,0.0002113347,0.0002538358,0.00003673823,0.0001857321,0.00000100475],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005375993,"about_ca_system_score_gemma":0.00007463137,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003966641,"about_ca_topic_score_gemma":5.383504e-7,"domain_scores_codex":[0.9987768,0.00003753241,0.0004901014,0.000123241,0.0004655595,0.0001067501],"domain_scores_gemma":[0.9987917,0.0001857263,0.000482517,0.00006798848,0.0003898818,0.00008219318],"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":[0.00005675948,0.0001751156,0.001447285,0.00004776857,0.00005260958,0.00003385657,0.0001282457,0.000009641994,0.9829736,0.0000383176,0.0004522666,0.01458447],"study_design_scores_gemma":[0.001584933,0.000119286,0.00445471,0.001085289,0.0001197229,0.0002536664,0.0003345636,0.00541135,0.9854339,0.0003664692,0.0007139069,0.0001222402],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6194245,0.0004456114,0.3725711,0.006621324,0.0001847129,0.0002894561,0.0002379251,0.00004316356,0.0001822702],"genre_scores_gemma":[0.9917127,0.001027234,0.006984559,0.00003661875,0.0000791753,0.000005535785,0.00006677031,0.00001176971,0.00007566842],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3722882,"threshold_uncertainty_score":0.3455147,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00712315373453154,"score_gpt":0.3472894331048982,"score_spread":0.3401662793703666,"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."}}