{"id":"W4389667582","doi":"10.1109/nssmicrtsd49126.2023.10338059","title":"Photon-counting detector spectral calibration enabling iodine quantification for spectral CT","year":2023,"lang":"en","type":"article","venue":"","topic":"Advanced X-ray and CT Imaging","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Redlen Technologies (Canada); University of Victoria","funders":"","keywords":"Imaging phantom; Calibration; Detector; Photon counting; Spectral imaging; Noise (video); Wedge (geometry); Pixel; Optics; Hounsfield scale; Materials science; Physics; Computer science; Artificial intelligence; Computed tomography; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001185618,0.0004393834,0.000289538,0.0005494247,0.00015875,0.0005701219,0.0007059961,0.0006783178,0.001752285],"category_scores_gemma":[0.004257283,0.0004869646,0.0004346237,0.0005379505,0.0003241539,0.000637739,0.0006882482,0.0006157283,0.0005424993],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000651542,"about_ca_system_score_gemma":0.0006673453,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008874024,"about_ca_topic_score_gemma":0.001142917,"domain_scores_codex":[0.999255,0.0001490107,0.00003685657,0.0001523589,0.0003746162,0.00003213878],"domain_scores_gemma":[0.998839,0.00060057,0.0001304204,0.0001844804,0.0002229005,0.00002273448],"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":[0.0004255814,0.00007854399,0.005036828,0.0004211306,0.00005642365,0.0001706665,0.0001259028,0.06079116,0.842598,0.005078158,0.0009544174,0.08426324],"study_design_scores_gemma":[0.00002133703,0.0001610964,0.003756427,0.0000480975,0.00006867202,0.0006029768,0.00003819135,0.2992436,0.6872301,0.001628179,0.007133318,0.00006809473],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09397762,0.0005515175,0.9021543,0.0001182454,0.00004394036,0.00009227021,0.0002132058,0.001053121,0.001795848],"genre_scores_gemma":[0.6115004,0.0006920017,0.3858789,0.0001019706,0.00001118805,0.000138001,0.0003548861,0.0001996172,0.0011231],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001752285,"threshold_uncertainty_score":0.00627017,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02141649223498177,"score_gpt":0.2523730593154354,"score_spread":0.2309565670804536,"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."}}