{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001039489,0.0000802812,0.0001843731,0.00003514353,0.00001420366,0.000001277551,0.00002765643,0.00001746312,7.046789e-7],"category_scores_gemma":[0.000009684966,0.00006309173,0.000008174363,0.00003539132,0.00007101674,0.00007555199,0.00001690337,0.00004901123,8.562738e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002541903,"about_ca_system_score_gemma":0.00001289345,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002015971,"about_ca_topic_score_gemma":0.0000129445,"domain_scores_codex":[0.9995049,0.0000135325,0.0002426485,0.00009585077,0.0000503871,0.00009271283],"domain_scores_gemma":[0.9997722,0.00004968037,0.00005247934,0.00005426194,0.00003818645,0.00003322385],"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":[0.00009464212,0.00007865627,0.0007820706,0.0001645,0.00006612336,0.00000336467,0.02047773,0.07082648,0.3484788,0.003416662,0.00004932745,0.5555617],"study_design_scores_gemma":[0.004897825,0.001589024,0.001561326,0.0006306204,0.00005745259,0.00003856335,0.00985029,0.6583806,0.2818402,0.03932566,0.001304739,0.0005237136],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8210672,0.0005363466,0.1780828,0.00004078864,0.0000650647,0.0001581017,0.000009636143,0.000007280644,0.00003278876],"genre_scores_gemma":[0.9941978,0.0001124026,0.005584492,0.00002512148,0.00002611457,0.00002603101,0.00002110045,0.000005610146,0.000001367333],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5875541,"threshold_uncertainty_score":0.2572806,"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."}}