{"id":"W1984631885","doi":"10.1002/jmri.21128","title":"Improved dynamic susceptibility contrast (DSC)‐MR perfusion estimates by motion correction","year":2007,"lang":"en","type":"article","venue":"Journal of Magnetic Resonance Imaging","topic":"Advanced MRI Techniques and Applications","field":"Medicine","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"Hotchkiss Brain Institute; Foothills Medical Centre; University of Calgary","funders":"","keywords":"Imaging phantom; Perfusion; Cerebral blood flow; Nuclear medicine; Motion (physics); Contrast (vision); Medicine; Computer science; Perfusion scanning; Artificial intelligence; Computer vision; Biomedical engineering; Radiology; Cardiology","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":[0.0006754652,0.0001443753,0.0002704115,0.0001108396,0.0001067706,0.00002415244,0.00009219678,0.00005379589,0.00008360283],"category_scores_gemma":[0.0002337083,0.0001232506,0.0001076902,0.0001886636,0.0001068569,0.0001844719,0.00002289355,0.0003639311,0.000002579222],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002255626,"about_ca_system_score_gemma":0.00004669549,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002710339,"about_ca_topic_score_gemma":0.00001111397,"domain_scores_codex":[0.9986884,0.00001659886,0.0005896584,0.0001946183,0.0002480885,0.0002626362],"domain_scores_gemma":[0.9988278,0.000110487,0.0003540341,0.0002073677,0.000365545,0.0001347851],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0001760714,0.0001361153,0.01742009,0.00001124189,9.739188e-7,0.00001442666,0.00004106319,0.00001109584,0.3956203,0.000006850772,0.001155776,0.5854059],"study_design_scores_gemma":[0.004677167,0.001762439,0.6077401,0.0008873876,0.0002226756,0.00312566,0.0009171776,0.2148118,0.130747,0.001292718,0.03329651,0.0005192937],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3231331,0.008655241,0.6658124,0.001274149,0.0002732307,0.0003727525,0.000004633,0.00006508951,0.0004094677],"genre_scores_gemma":[0.9554141,0.00059652,0.04323201,0.0001726485,0.0001125092,0.000004864923,0.000007481803,0.00002221378,0.0004377087],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.632281,"threshold_uncertainty_score":0.5026014,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005673060013134229,"score_gpt":0.291375561576232,"score_spread":0.2857025015630977,"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."}}