{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002177329,0.0009254911,0.0007983868,0.001230941,0.0006498704,0.001028161,0.001681161,0.00089959,0.001166341],"category_scores_gemma":[0.008727895,0.0007676698,0.0008973214,0.001086452,0.0006054533,0.001064281,0.001337861,0.001000893,0.0003723082],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00156112,"about_ca_system_score_gemma":0.002272691,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01389028,"about_ca_topic_score_gemma":0.01659059,"domain_scores_codex":[0.9988507,0.0001988064,0.00007174827,0.000170609,0.0006508704,0.00005719899],"domain_scores_gemma":[0.9968695,0.00135445,0.000337025,0.000476221,0.0008603297,0.0001024772],"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":[0.0003423993,0.0001819512,0.01012339,0.00009247593,0.0001606091,0.0001624834,0.0002535632,0.739505,0.03200461,0.003067025,0.000619898,0.2134867],"study_design_scores_gemma":[0.00001033837,0.00003644598,0.0008109171,0.0000076829,0.00001451628,0.00008413912,0.000008397042,0.9890481,0.008768167,0.000578874,0.0006173711,0.00001523905],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02806453,0.0001001524,0.9701372,0.00004292224,0.00001205619,0.00007396556,0.00001851684,0.00104381,0.0005068195],"genre_scores_gemma":[0.329126,0.00009422655,0.6692991,0.00005218992,0.00001454433,0.0001575123,0.0001125144,0.0003941589,0.0007498084],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01389028,"threshold_uncertainty_score":0.02761889,"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."}}