{"id":"W2938474704","doi":"10.1088/1361-6560/ab1af1","title":"The potential of photon-counting CT for quantitative contrast-enhanced imaging in radiotherapy","year":2019,"lang":"en","type":"article","venue":"Physics in Medicine and Biology","topic":"Advanced X-ray and CT Imaging","field":"Engineering","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"Centre Hospitalier de l’Université de Montréal; Université de Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; Fonds Québécois de la Recherche sur la Nature et les Technologies","keywords":"Photon counting; Nuclear medicine; Computer science; Contrast (vision); Materials science; Medical physics; Medicine; Optics; Photon; Physics; Artificial intelligence","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":[0.001855111,0.000587091,0.0004490474,0.0003716553,0.0002496061,0.001438493,0.0008942381,0.001135462,0.001050676],"category_scores_gemma":[0.007511033,0.0003689989,0.0005127912,0.0003742734,0.0005451103,0.0007689573,0.0009265455,0.0008592239,0.0002911232],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000622323,"about_ca_system_score_gemma":0.0009657103,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003005146,"about_ca_topic_score_gemma":0.00186017,"domain_scores_codex":[0.9991449,0.0005100132,0.00002940067,0.00005944187,0.0002297543,0.00002640908],"domain_scores_gemma":[0.9978548,0.001562123,0.0001449655,0.0001823992,0.0001943571,0.00006126388],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0003167955,0.0000853325,0.002900306,0.0001722123,0.00004942558,0.0001653528,0.00008114779,0.9493366,0.01610564,0.006779214,0.000396492,0.0236114],"study_design_scores_gemma":[0.00003207022,0.0001122304,0.0005259269,0.00003646071,0.00002209771,0.0002041107,0.00002448771,0.9841446,0.01015021,0.002671788,0.002050714,0.00002540575],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1414108,0.001585561,0.848217,0.0008163669,0.0001420153,0.0001952145,0.0002187412,0.0009495153,0.006464957],"genre_scores_gemma":[0.8042391,0.0007945846,0.1932331,0.0002302686,0.00002302588,0.0001627847,0.0001356838,0.0002017907,0.000979736],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003005146,"threshold_uncertainty_score":0.009810865,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03629617382353446,"score_gpt":0.3419652430160789,"score_spread":0.3056690691925445,"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."}}