{"id":"W1976981161","doi":"10.1118/1.3658728","title":"A theoretical comparison of x-ray angiographic image quality using energy-dependent and conventional subtraction methods","year":2011,"lang":"en","type":"article","venue":"Medical Physics","topic":"Advanced X-ray and CT Imaging","field":"Engineering","cited_by":29,"is_retracted":false,"has_abstract":true,"ca_institutions":"Lawson Health Research Institute; London Health Sciences Centre; Robarts Clinical Trials; Western University","funders":"Canadian Institutes of Health Research; Ministry of Education, Science and Technology","keywords":"Imaging phantom; Monte Carlo method; Image quality; Detector; Subtraction; Energy (signal processing); Flat panel detector; Digital subtraction angiography; Noise (video); Physics; Photon; Signal-to-noise ratio (imaging); Optics; Angiography; Computer science; Radiology; Mathematics; Artificial intelligence; Medicine; Image (mathematics); Statistics","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":[0.002027121,0.0008304216,0.0002328815,0.001284314,0.0002158533,0.001253103,0.001181826,0.0009062708,0.002884008],"category_scores_gemma":[0.007417071,0.0003052121,0.0007935302,0.0005156212,0.0006762701,0.001115702,0.0006389734,0.0004356034,0.0009276629],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0014315,"about_ca_system_score_gemma":0.0005104333,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009960908,"about_ca_topic_score_gemma":0.0007283408,"domain_scores_codex":[0.9988571,0.0002884594,0.00004457734,0.0001633675,0.00058895,0.00005756267],"domain_scores_gemma":[0.9970092,0.001839211,0.0002209503,0.0002261382,0.0006534378,0.00005105265],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.002804562,0.0004477011,0.0250522,0.0011727,0.0006089713,0.0005709722,0.0003725419,0.4400804,0.2206879,0.02924673,0.002575592,0.2763796],"study_design_scores_gemma":[0.00008120936,0.0008275238,0.01908084,0.000107813,0.0003507124,0.001353068,0.00007357099,0.8213758,0.1478837,0.005665925,0.003082949,0.0001168639],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1591446,0.001782825,0.8292232,0.0003174353,0.00005470062,0.00007474763,0.0002256169,0.00173932,0.007437582],"genre_scores_gemma":[0.8249458,0.000830832,0.1710465,0.0001323588,0.00003137346,0.00007847019,0.0004230502,0.0002588091,0.002252861],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002884008,"threshold_uncertainty_score":0.01072061,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0473046949330875,"score_gpt":0.3652672886666867,"score_spread":0.3179625937335991,"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."}}