{"id":"W2157710589","doi":"10.1393/ncc/i2005-10213-3","title":"Novel Geometrical Concept of a High Performance Brain PET Scanner : Principle, Design and Performance Estimates","year":2004,"lang":"it","type":"article","venue":"CERN Document Server (European Organization for Nuclear Research)","topic":"Medical Imaging Techniques and Applications","field":"Medicine","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"CERN; McGill University","keywords":"Scanner; Scintillator; Parallax; Computer science; Image resolution; Monte Carlo method; Detector; Positron emission tomography; Axial symmetry; Image quality; Resolution (logic); Energy (signal processing); Optics; Sensitivity (control systems); Computer vision; Physics; Artificial intelligence; Electronic engineering; Image (mathematics); Nuclear medicine; Engineering; Mathematics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005810094,0.0005382674,0.0003115266,0.0003408509,0.0002026082,0.0009905226,0.001035618,0.0009003179,0.001553141],"category_scores_gemma":[0.0009164565,0.0003636116,0.0003150306,0.0002614965,0.0008090417,0.001049216,0.0006079583,0.0005217437,0.0008677594],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005388714,"about_ca_system_score_gemma":0.0006679584,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002118377,"about_ca_topic_score_gemma":0.0002460397,"domain_scores_codex":[0.9995433,0.0001082521,0.00001757189,0.00006275414,0.000241058,0.00002716303],"domain_scores_gemma":[0.999508,0.0001245945,0.00008202179,0.00007003342,0.000163152,0.00005226795],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0004094088,0.00008874684,0.002155271,0.0007640285,0.00009584297,0.0006343857,0.0002117843,0.02384752,0.7697237,0.09913518,0.003451116,0.09948299],"study_design_scores_gemma":[0.000123125,0.002580291,0.007568206,0.00009621395,0.0002249602,0.01361452,0.0001410774,0.1938168,0.6689482,0.02346962,0.08912966,0.0002872522],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02767888,0.0009771629,0.9628575,0.0007313539,0.00006246799,0.0001419441,0.0001099097,0.0006991364,0.006741534],"genre_scores_gemma":[0.2484144,0.0006912812,0.7465518,0.0002635217,0.00007143633,0.0002175217,0.0001399954,0.00007879323,0.00357127],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.001553141,"threshold_uncertainty_score":0.005195796,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03528180319559136,"score_gpt":0.3024438745395974,"score_spread":0.267162071344006,"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."}}