{"id":"W2517734444","doi":"10.1118/1.4961779","title":"Poster - 05: Automated analysis of MR distortion using a novel anthropomorphic phantom and open source software","year":2016,"lang":"en","type":"article","venue":"Medical Physics","topic":"Medical Imaging and Analysis","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Saskatchewan Cancer Agency","funders":"","keywords":"Imaging phantom; Distortion (music); Radiosurgery; Computer science; Computer vision; Software; Artificial intelligence; Image quality; Quality assurance; Medical imaging; Nuclear medicine; Medicine; Radiology; Image (mathematics)","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":[0.0002158019,0.0001337064,0.0004483743,0.0000957085,0.00005035285,0.00002614909,0.0002208594,0.00007988374,0.0003465635],"category_scores_gemma":[0.0001973307,0.00009107486,0.0001017956,0.0006463098,0.0002556063,0.000158503,0.0001210562,0.00009940343,0.000005872597],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004154864,"about_ca_system_score_gemma":0.00003199849,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000140423,"about_ca_topic_score_gemma":0.00001063191,"domain_scores_codex":[0.9988291,0.00002458308,0.000291742,0.0001971662,0.0004579182,0.0001995297],"domain_scores_gemma":[0.9993345,0.0001182178,0.00006652179,0.0002092951,0.00004976509,0.0002217718],"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":[0.00004232003,0.001138604,0.1193723,0.0007161398,0.0108489,0.00007165131,0.001918032,0.005151634,0.09829161,0.00009010789,0.006873437,0.7554852],"study_design_scores_gemma":[0.001323842,0.00002995096,0.004815803,0.000489668,0.002241339,0.000008392798,0.00003083976,0.9821028,0.007487431,0.0001471964,0.0009285044,0.0003942313],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3153029,0.0001121238,0.683945,0.0001944241,0.0000782269,0.00004725836,0.00003350735,0.0002408483,0.00004569377],"genre_scores_gemma":[0.9989803,0.0000486488,0.0006763909,0.0001033415,0.00007805967,0.000002857324,0.00002757429,0.00002333962,0.00005949651],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9769512,"threshold_uncertainty_score":0.3794627,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02333645771433836,"score_gpt":0.2859244126017325,"score_spread":0.2625879548873941,"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."}}