{"id":"W4393393666","doi":"10.1117/12.3005975","title":"Anti-correlated noise reduction in triple-energy, photon-counting x-ray angiography","year":2024,"lang":"en","type":"article","venue":"","topic":"Advanced X-ray and CT Imaging","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Photon counting; Image quality; Imaging phantom; Optics; Detector; Photon; Noise (video); Angiography; Materials science; Physics; Photon energy; Nuclear medicine; Radiology; Medicine; Computer science; Artificial intelligence","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":[0.0001132992,0.0001699557,0.0001586263,0.0005088171,0.0000410608,0.00007570181,0.00008079095,0.00007829536,0.0001004924],"category_scores_gemma":[0.00000665094,0.0001698473,0.0001391593,0.001320206,0.00002326079,0.0004999868,0.00001490261,0.0002357165,0.00003817548],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005007555,"about_ca_system_score_gemma":0.000009445607,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000131011,"about_ca_topic_score_gemma":0.00001014649,"domain_scores_codex":[0.9990925,0.00001095583,0.0002489534,0.0002339163,0.000108889,0.0003047657],"domain_scores_gemma":[0.9997576,0.00002318863,0.00001235469,0.0001448908,0.00001695457,0.00004502666],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0000335408,0.000124105,0.003150687,0.0004850506,0.0004074761,0.0002872212,0.002295709,0.5363428,0.3561742,0.006160955,0.01003706,0.08450118],"study_design_scores_gemma":[0.0006321259,0.00002151009,0.002095585,0.0004658612,0.00006199014,0.00006087997,0.0007556939,0.9263294,0.03617317,0.001779764,0.03087741,0.0007465687],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7719548,0.02251531,0.1511507,0.0001469094,0.005848866,0.0003037892,0.000009406255,0.004619367,0.04345084],"genre_scores_gemma":[0.9977041,0.000328937,0.00123839,0.0000224432,0.0001236268,0.00001984417,0.00001538707,0.00005099149,0.0004962306],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3899866,"threshold_uncertainty_score":0.6926171,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004443088481526714,"score_gpt":0.2034894278507333,"score_spread":0.1990463393692066,"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."}}