{"id":"W1542507286","doi":"10.1088/0031-9155/60/15/6039","title":"Development of virtual patient models for permanent implant brachytherapy Monte Carlo dose calculations: interdependence of CT image artifact mitigation and tissue assignment","year":2015,"lang":"en","type":"article","venue":"Physics in Medicine and Biology","topic":"Advanced X-ray and CT Imaging","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"Centre hospitalier universitaire de Québec; Université Laval; Carleton University","funders":"Canadian Cancer Society Research Institute; Canadian Institutes of Health Research; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; Cancer Research Institute","keywords":"Imaging phantom; Voxel; Computer science; Brachytherapy; Artifact (error); Monte Carlo method; Scanner; Reduction (mathematics); Filter (signal processing); Hounsfield scale; Nuclear medicine; Artificial intelligence; Computer vision; Computed tomography; Medicine; Mathematics; Radiology; Radiation therapy; 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":[0.001021468,0.0007058881,0.0003865665,0.0004268887,0.0002221525,0.001318518,0.000971063,0.0006504089,0.002054511],"category_scores_gemma":[0.003530526,0.0006993869,0.0008770716,0.0003016004,0.0003229349,0.0004498803,0.000820331,0.0005670762,0.0005909072],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000705494,"about_ca_system_score_gemma":0.001133381,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00168039,"about_ca_topic_score_gemma":0.002515185,"domain_scores_codex":[0.9995197,0.0001797511,0.00003021272,0.00004618248,0.0001987528,0.00002540423],"domain_scores_gemma":[0.9985763,0.0008577799,0.0001347454,0.0002385188,0.0001403927,0.00005231384],"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":[0.0002003121,0.00004900879,0.002410352,0.0001296965,0.00005685151,0.0001494974,0.0002339394,0.9384573,0.01765404,0.003062924,0.000789106,0.03680705],"study_design_scores_gemma":[0.00003137573,0.0001771812,0.001071436,0.00002687105,0.00004321572,0.0004183292,0.00006134337,0.9657279,0.02480872,0.001310294,0.006269752,0.00005360254],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.08108032,0.0001917112,0.9130793,0.000146932,0.00003911831,0.0002684213,0.0004655669,0.001644975,0.003083674],"genre_scores_gemma":[0.6617957,0.0002889201,0.3336322,0.00009572219,0.00001080887,0.0004084829,0.0005534085,0.001001982,0.002212776],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002054511,"threshold_uncertainty_score":0.006873012,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.115132288041055,"score_gpt":0.3557109616511276,"score_spread":0.2405786736100726,"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."}}