{"id":"W1972951903","doi":"10.1118/1.3615058","title":"Development of a dynamic flow imaging phantom for dynamic contrast‐enhanced CT","year":2011,"lang":"en","type":"article","venue":"Medical Physics","topic":"Advanced MRI Techniques and Applications","field":"Medicine","cited_by":48,"is_retracted":false,"has_abstract":true,"ca_institutions":"Princess Margaret Cancer Centre","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Imaging phantom; Reproducibility; Computer science; Medical imaging; Dynamic imaging; Biomedical engineering; Perfusion scanning; Nuclear medicine; Image processing; Perfusion; Computer vision; Artificial intelligence; Mathematics; Medicine; Radiology; Digital image processing; Image (mathematics)","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":[0.001775221,0.0006473014,0.0003484905,0.0005568648,0.0002515573,0.0005954409,0.0009256195,0.0007294731,0.001529764],"category_scores_gemma":[0.002067688,0.0005142145,0.0004614756,0.0003332219,0.0004734588,0.0004984803,0.0004775422,0.0005926814,0.0006595508],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000520206,"about_ca_system_score_gemma":0.0009530263,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003959706,"about_ca_topic_score_gemma":0.0003415634,"domain_scores_codex":[0.999481,0.0001017073,0.00005019804,0.00009772454,0.0002318424,0.00003751572],"domain_scores_gemma":[0.9990425,0.0004437861,0.0001165033,0.0001197555,0.0002406508,0.0000368095],"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":[0.0001632522,0.0001806656,0.0007867646,0.0003718522,0.00001503976,0.0002229972,0.0001015876,0.02264762,0.9308977,0.003963892,0.001018494,0.03963021],"study_design_scores_gemma":[0.00006140848,0.0006385143,0.001670214,0.00008068344,0.00004415282,0.0009554747,0.00001626399,0.0977018,0.8698292,0.000824466,0.02810239,0.00007546128],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02875899,0.0004730934,0.9657491,0.0002017464,0.0001035694,0.0006179314,0.0002600648,0.001697891,0.002137605],"genre_scores_gemma":[0.122638,0.0004816685,0.8733973,0.0001186225,0.00002069248,0.001027021,0.0004770674,0.0001956538,0.001643987],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001775221,"threshold_uncertainty_score":0.009388387,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01923426424358759,"score_gpt":0.3263799144510978,"score_spread":0.3071456502075102,"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."}}