{"id":"W4406449714","doi":"10.1186/s40658-024-00711-6","title":"Improving timing resolution of BGO for TOF-PET: a comparative analysis with and without deep learning","year":2025,"lang":"en","type":"article","venue":"EJNMMI Physics","topic":"Radiation Detection and Scintillator Technologies","field":"Physics and Astronomy","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Sherbrooke","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada; Fonds de Recherche du Québec - Santé; Bundesministerium für Bildung und Forschung; Québec Consortium for Drug Discovery","keywords":"Computer science; Deep learning; Resolution (logic); Nuclear medicine; Artificial intelligence; Medical physics; Medicine","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":[0.001198169,0.0004275487,0.0003253034,0.0006145507,0.000127726,0.0004608668,0.0004802959,0.0005217348,0.00135634],"category_scores_gemma":[0.002364009,0.0001221883,0.0002689553,0.0006225802,0.0001231673,0.0005509307,0.0002360334,0.0002120681,0.0002532665],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005922432,"about_ca_system_score_gemma":0.0004283975,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004803751,"about_ca_topic_score_gemma":0.006353611,"domain_scores_codex":[0.9996598,0.00005356066,0.00002057334,0.00007916726,0.0001229265,0.00006384734],"domain_scores_gemma":[0.9989508,0.0005201891,0.00008923453,0.00007639305,0.0003249401,0.00003837392],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.002661233,0.0003010097,0.0257375,0.0004532768,0.000348166,0.0002413289,0.00005833481,0.1856156,0.1326573,0.001028999,0.002240158,0.6486572],"study_design_scores_gemma":[0.00004004172,0.0003704803,0.02534915,0.00002511585,0.0001449292,0.0001791686,0.00003025744,0.8575824,0.11418,0.0003090999,0.001760735,0.00002857816],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8761731,0.002934419,0.1134515,0.0002914319,0.0000654969,0.00005295993,0.0006459961,0.002027016,0.004357974],"genre_scores_gemma":[0.9580284,0.0004663754,0.03879502,0.00006342871,0.00001842372,0.00001861386,0.0008611936,0.000105654,0.001642934],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004803751,"threshold_uncertainty_score":0.009551585,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01557269639547692,"score_gpt":0.2775112601481849,"score_spread":0.261938563752708,"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."}}