{"id":"W4391465702","doi":"10.1088/1361-6560/ad25c8","title":"Material decomposition with a prototype photon-counting detector CT system: expanding a stoichiometric dual-energy CT method via energy bin optimization and K-edge imaging","year":2024,"lang":"en","type":"article","venue":"Physics in Medicine and Biology","topic":"Advanced X-ray and CT Imaging","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Redlen Technologies (Canada); University of Victoria","funders":"British Columbia Knowledge Development Fund; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; Canada Foundation for Innovation","keywords":"Imaging phantom; Energy (signal processing); Atomic number; Detector; Effective atomic number; Photon counting; Digital Enhanced Cordless Telecommunications; Physics; Subtraction; Materials science; Optics; Mathematics; Computer science; Atomic physics","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":[0.0007332178,0.0004549452,0.0004001044,0.0004858194,0.0001899712,0.0007120486,0.0009517183,0.0006048108,0.001879053],"category_scores_gemma":[0.0008082334,0.000433994,0.0002922681,0.0004448521,0.0002340765,0.000645242,0.0005938935,0.0005757314,0.0006581546],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004935866,"about_ca_system_score_gemma":0.0006278642,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007950497,"about_ca_topic_score_gemma":0.001232751,"domain_scores_codex":[0.9997386,0.00003419392,0.00001509469,0.00006799637,0.0001303314,0.00001365531],"domain_scores_gemma":[0.999629,0.0001030808,0.00004441515,0.00006407687,0.0001303609,0.00002895595],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000420317,0.0001741532,0.002565508,0.0002174695,0.00004064474,0.0001706397,0.00007020096,0.03168862,0.8677134,0.001944697,0.000816113,0.09417834],"study_design_scores_gemma":[0.00003579967,0.0001987992,0.002351612,0.00001972586,0.00004194244,0.0005460759,0.00002535973,0.6381788,0.3534016,0.0006554123,0.004477045,0.00006780212],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0732594,0.000197488,0.9239409,0.0001177379,0.00002785079,0.0001075429,0.0001105068,0.00117264,0.001065968],"genre_scores_gemma":[0.1358269,0.0001038934,0.8629012,0.00006579333,0.000005164845,0.00005932419,0.0001329521,0.000113605,0.0007913117],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001879053,"threshold_uncertainty_score":0.006286085,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02573909324853242,"score_gpt":0.3169384833119526,"score_spread":0.2911993900634202,"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."}}