{"id":"W4402029904","doi":"10.1088/1361-6560/ad75df","title":"Contrast and quantum noise in single-exposure dual-energy thoracic imaging with photon-counting x-ray detectors","year":2024,"lang":"en","type":"article","venue":"Physics in Medicine and Biology","topic":"Radiation Dose and Imaging","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Foundation for Innovation; Ontario Research Foundation","keywords":"Contrast (vision); Detector; Photon counting; Noise (video); Photon; Physics; Dual energy; Optics; Contrast-to-noise ratio; Energy (signal processing); X-ray; Nuclear medicine; Materials science; Medicine; Computer science; Image quality; Artificial intelligence; Quantum mechanics","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":[0.0002881378,0.0001634011,0.0003767645,0.0002244439,0.00003412136,0.0000193572,0.00002979813,0.00004977568,0.00001049163],"category_scores_gemma":[0.00005875402,0.0001091984,0.00001665244,0.0003677776,0.0002593501,0.0001223108,0.00002223409,0.000257812,8.677314e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003232138,"about_ca_system_score_gemma":0.00004520503,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000522503,"about_ca_topic_score_gemma":0.00007339501,"domain_scores_codex":[0.999031,0.00005219186,0.0002538381,0.0003316004,0.00007898387,0.0002523632],"domain_scores_gemma":[0.9995345,0.0002203824,0.00004770322,0.0001028507,0.00002952277,0.00006497032],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001822299,0.00009671328,0.5135784,0.0002947077,0.00006796522,0.000329758,0.002413967,0.00001451962,0.2284951,0.002617158,0.0001504158,0.2517592],"study_design_scores_gemma":[0.02630235,0.007812087,0.4499221,0.01813698,0.001200267,0.002268596,0.01805232,0.3422971,0.05753057,0.02600405,0.04758301,0.00289059],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9859199,0.009098653,0.001872931,0.002080676,0.0001608513,0.0001208588,0.000002357651,0.00004040293,0.0007033981],"genre_scores_gemma":[0.9978691,0.0005011154,0.00008644021,0.001018564,0.000445155,0.00001217327,0.00002367941,0.00001926665,0.00002446502],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3422826,"threshold_uncertainty_score":0.4452981,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05763671472419074,"score_gpt":0.337864028941185,"score_spread":0.2802273142169943,"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."}}