{"id":"W3039122454","doi":"10.1007/s11307-020-01512-w","title":"Ultrafast Ultrasound Imaging for Super-Resolution Preclinical Cardiac PET","year":2020,"lang":"en","type":"article","venue":"Molecular Imaging and Biology","topic":"Medical Imaging Techniques and Applications","field":"Medicine","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"Montreal Heart Institute; Polytechnique Montréal","funders":"Institut National Du Cancer; Université Paris Descartes; Agence Nationale de la Recherche","keywords":"Cardiac PET; Ultrasound; Pet imaging; Image quality; Positron emission tomography; Biomedical engineering; Nuclear medicine; Cardiac imaging; Image resolution; Partial volume; Medicine; Computer science; Radiology; Artificial intelligence; Image (mathematics)","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":[0.0002116245,0.0001364244,0.0002779289,0.00003360821,0.0001024947,0.00002314031,0.00008065993,0.00003948434,0.00001708554],"category_scores_gemma":[0.0005082905,0.0001156917,0.0001250514,0.000090376,0.0002601451,0.00002758737,0.00004422185,0.0001983335,0.000006088711],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001379733,"about_ca_system_score_gemma":0.00005053755,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000259299,"about_ca_topic_score_gemma":2.033046e-7,"domain_scores_codex":[0.9989622,0.00005249204,0.000234046,0.0004146176,0.00006121366,0.0002753786],"domain_scores_gemma":[0.9993207,0.0001453986,0.00004279284,0.0001833866,0.0000715274,0.0002361833],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0000563041,0.00005556697,0.04373854,0.00006956959,0.00003941473,0.00001818063,0.00007234242,4.933715e-7,0.9341412,0.002090526,0.01084832,0.008869529],"study_design_scores_gemma":[0.003863757,0.0006053076,0.01100832,0.0001827312,0.0007279557,0.000916643,0.0003579527,0.05208596,0.126535,0.003743203,0.7990837,0.000889556],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5160354,0.00169928,0.2827611,0.1967045,0.0001612707,0.001097688,0.0001149314,0.0004990619,0.0009266712],"genre_scores_gemma":[0.9684926,0.0001333999,0.02099425,0.009808878,0.0002206325,0.00007087224,0.000232551,0.00002156586,0.00002527387],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8076062,"threshold_uncertainty_score":0.4717771,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0358539853024742,"score_gpt":0.351680765637199,"score_spread":0.3158267803347248,"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."}}