{"id":"W4411923916","doi":"10.1007/978-3-031-85078-3_10","title":"Photon-Counting Computed Tomography: Clinical Applications, Detector Technology, and Artificial Intelligence","year":2025,"lang":"en","type":"book-chapter","venue":"","topic":"Advanced X-ray and CT Imaging","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"Royal Columbian Hospital; Redlen Technologies (Canada); University of Victoria","funders":"","keywords":"Detector; Photon counting; Computed tomography; Artificial intelligence; Photon; Medical physics; Physics; Optics; Computer science; Nuclear medicine; Computer vision; Medicine; Radiology","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001252492,0.0003009944,0.0003857344,0.0005217521,0.00009891951,0.00004025961,0.0002263567,0.0004255731,0.00006627174],"category_scores_gemma":[0.00001492565,0.0003254785,0.0001068449,0.0001876984,0.0002244315,0.00006025598,0.000112837,0.0007576545,0.00004015977],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002513567,"about_ca_system_score_gemma":0.00002055987,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001528287,"about_ca_topic_score_gemma":0.00001103459,"domain_scores_codex":[0.9987031,0.000003738063,0.000582587,0.0003974569,0.00009369185,0.0002193745],"domain_scores_gemma":[0.9993022,0.0001501216,0.00007479756,0.0003479501,0.00006921903,0.00005569588],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000004061158,0.000007764794,0.00006414273,0.0001268469,0.00009695772,0.000004246642,0.000004919724,0.0001625038,0.00007511962,0.2557316,0.0001237683,0.7435981],"study_design_scores_gemma":[0.0001240524,0.00005067886,0.00002752164,0.0006610105,0.0001947607,0.00002451832,0.00009871481,0.06170857,0.007656893,0.4245526,0.5035198,0.001380924],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00009333203,0.003680337,0.8251435,0.00007719684,0.0003395152,0.0005612206,0.00002497409,0.001510308,0.1685697],"genre_scores_gemma":[0.4315608,0.007285519,0.430016,0.001351411,0.003143616,0.0008543951,0.0003142523,0.0008102785,0.1246637],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7422172,"threshold_uncertainty_score":0.9999197,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01743141349558044,"score_gpt":0.2727607079658848,"score_spread":0.2553292944703044,"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."}}