{"id":"W1990157619","doi":"10.1118/1.4903260","title":"Efficient scatter distribution estimation and correction in CBCT using concurrent Monte Carlo fitting","year":2014,"lang":"en","type":"article","venue":"Medical Physics","topic":"Digital Radiography and Breast Imaging","field":"Medicine","cited_by":54,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; Ontario Institute for Cancer Research; McGill University; Princess Margaret Cancer Centre","funders":"","keywords":"Monte Carlo method; Imaging phantom; Data set; Projection (relational algebra); Cone beam computed tomography; Computer science; Detector; Algorithm; Pixel; Medical imaging; Calibration; Image quality; Mathematics; Optics; Artificial intelligence; Physics; Statistics; Image (mathematics); Computed tomography","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":[0.0002451877,0.0000886352,0.0001591412,0.00003605676,0.00004639989,0.00002242814,0.00002520889,0.00004365015,0.000003991412],"category_scores_gemma":[0.0002344401,0.0000770856,0.00004456632,0.0002085513,0.000101169,0.00006012019,0.00002276394,0.0001835685,0.000002773182],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006093148,"about_ca_system_score_gemma":0.00002880872,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006273873,"about_ca_topic_score_gemma":0.000001937224,"domain_scores_codex":[0.9991599,0.00002663444,0.0001751304,0.0001624264,0.0003145217,0.0001613365],"domain_scores_gemma":[0.9996417,0.00007383685,0.00005025369,0.00007409172,0.0000348781,0.0001252894],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001925706,0.0001871251,0.04015581,0.00008012396,0.00001039778,0.000005923764,0.0001476896,0.003204596,0.0000795707,0.00007582633,0.0001562245,0.9558775],"study_design_scores_gemma":[0.0007107307,0.00005150257,0.03608403,0.0005240046,0.00003082929,0.00005042148,0.00002329773,0.9618971,0.0003897861,0.00008127531,0.00007928862,0.00007774861],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8636322,0.00004231168,0.1353191,0.0003145738,0.0003393232,0.0001033513,0.000003093464,0.00002999381,0.0002160205],"genre_scores_gemma":[0.9995211,0.000001907858,0.00005943528,0.0001654065,0.0002025337,0.000003579089,0.00003288747,0.000007086897,0.000006047403],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9586925,"threshold_uncertainty_score":0.3143459,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01088595505094725,"score_gpt":0.2687197685777535,"score_spread":0.2578338135268062,"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."}}