{"id":"W4387996280","doi":"10.1088/1361-6560/ad078c","title":"The SNR of time-of-flight positron emission tomography data for joint reconstruction of the activity and attenuation images","year":2023,"lang":"en","type":"article","venue":"Physics in Medicine and Biology","topic":"Medical Imaging Techniques and Applications","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institutes of Health; Fonds Wetenschappelijk Onderzoek; National Institute of Biomedical Imaging and Bioengineering; Vlaamse regering; York University","keywords":"Attenuation; Correction for attenuation; Detector; Time of flight; Gaussian; Positron emission tomography; Optics; Physics; Photon; Iterative reconstruction; Sensitivity (control systems); Kernel (algebra); Scintillation; Computer science; Mathematics; Artificial intelligence; Nuclear medicine","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":[0.002077837,0.000458619,0.0003837971,0.0006340224,0.0001823781,0.0007608039,0.0003853427,0.0006282976,0.001083656],"category_scores_gemma":[0.01740448,0.000214607,0.0003653411,0.0004294164,0.0004874088,0.001039111,0.0004929113,0.0004371423,0.0001779821],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005489824,"about_ca_system_score_gemma":0.0004706019,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008931596,"about_ca_topic_score_gemma":0.0006241344,"domain_scores_codex":[0.9992082,0.0002246379,0.00005594624,0.0001212819,0.0003274699,0.00006257161],"domain_scores_gemma":[0.9937442,0.004427375,0.0005519186,0.0003684205,0.0008089108,0.0000991786],"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":[0.003269413,0.0002596563,0.05122425,0.0007297579,0.000294048,0.001301199,0.0002703286,0.4490225,0.3405564,0.01420332,0.0009907978,0.1378784],"study_design_scores_gemma":[0.00001876411,0.000139006,0.01771742,0.00003669037,0.00005850157,0.0006261243,0.00004914567,0.8697944,0.1092204,0.00165608,0.0006491681,0.00003428977],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.6404824,0.0004788479,0.3561952,0.0002186217,0.00005241561,0.00002516685,0.0003241381,0.0005699988,0.00165326],"genre_scores_gemma":[0.9744284,0.0001126502,0.02475086,0.0000198522,0.00001320119,0.00001451541,0.0002944902,0.00009747088,0.0002685845],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.002077837,"threshold_uncertainty_score":0.01098877,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1902716173029659,"score_gpt":0.4182445620538151,"score_spread":0.2279729447508492,"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."}}