{"id":"W1988821523","doi":"10.1002/mrm.21901","title":"Optimal <i>k</i>‐space sampling for dynamic contrast‐enhanced MRI with an application to MR renography","year":2009,"lang":"en","type":"article","venue":"Magnetic Resonance in Medicine","topic":"MRI in cancer diagnosis","field":"Medicine","cited_by":126,"is_retracted":false,"has_abstract":true,"ca_institutions":"Siemens (Canada)","funders":"National Institute of Diabetes and Digestive and Kidney Diseases","keywords":"Undersampling; Imaging phantom; Dynamic contrast-enhanced MRI; Flip angle; Temporal resolution; Magnetic resonance imaging; SIGNAL (programming language); Sampling (signal processing); Nuclear medicine; Mathematics; Computer science; Physics; Nuclear magnetic resonance; Materials science; Medicine; Artificial intelligence; Radiology; Computer vision; Optics","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004959215,0.0003235854,0.0006982631,0.0003594711,0.00006310872,0.00001565522,0.0002485403,0.0001260556,0.00007994615],"category_scores_gemma":[0.0001291414,0.0002631157,0.00005404986,0.001050539,0.0001789904,0.00009001467,0.00001495075,0.0002640006,0.000007017838],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001653165,"about_ca_system_score_gemma":0.00008811958,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001169512,"about_ca_topic_score_gemma":0.0002968655,"domain_scores_codex":[0.9975031,0.00003703989,0.0005352583,0.0007991722,0.000520925,0.0006044958],"domain_scores_gemma":[0.998322,0.000232767,0.0001267112,0.000755185,0.0002275853,0.0003357657],"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":[0.007712232,0.0007960464,0.01085392,0.000312859,0.00002499965,0.00004563297,0.003642976,0.003296242,0.0710091,0.0008299661,0.00808472,0.8933913],"study_design_scores_gemma":[0.02851994,0.05388649,0.5529913,0.005579375,0.0004555135,0.0001188113,0.001733117,0.02940789,0.007536313,0.001077958,0.3173242,0.00136915],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5896097,0.01191671,0.3243878,0.06651884,0.0001990093,0.005969225,0.00002602149,0.0002187998,0.001153868],"genre_scores_gemma":[0.8212993,0.001799288,0.1691496,0.005596424,0.0004049246,0.001271666,0.00007616828,0.00006061601,0.0003420688],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8920221,"threshold_uncertainty_score":0.9999821,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01276906407329472,"score_gpt":0.3140521235577891,"score_spread":0.3012830594844944,"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."}}