{"id":"W4378952000","doi":"10.1186/s40658-023-00555-6","title":"NEMA NU 1-2018 performance characterization and Monte Carlo model validation of the Cubresa Spark SiPM-based preclinical SPECT scanner","year":2023,"lang":"en","type":"article","venue":"EJNMMI Physics","topic":"Medical Imaging Techniques and Applications","field":"Medicine","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Discovery Centre; Manitoba Beekeepers' Association; Dalhousie University","funders":"Natural Sciences and Engineering Research Council of Canada; IWK Health Centre","keywords":"Silicon photomultiplier; Pinhole (optics); Collimator; Scanner; Image resolution; Detector; Monte Carlo method; DICOM; Optics; Gamma camera; Lyso-; Spect imaging; Nuclear medicine; Physics; Scintillator; Medical physics; Materials science; Computer science; Artificial intelligence; Medicine; Mathematics","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":[0.001211617,0.0003926546,0.0004163107,0.0003123855,0.0002570861,0.0005561008,0.0008884866,0.0005017522,0.002176389],"category_scores_gemma":[0.001850408,0.0002405798,0.0002934615,0.0002826861,0.0003433281,0.0003773286,0.000373753,0.0002781264,0.0006219376],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001194265,"about_ca_system_score_gemma":0.001444612,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002792042,"about_ca_topic_score_gemma":0.003106769,"domain_scores_codex":[0.9996564,0.00006424169,0.00001888385,0.00004777622,0.0001791168,0.0000336589],"domain_scores_gemma":[0.999271,0.000222043,0.00008134938,0.0001067519,0.0002750023,0.00004384241],"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":[0.001692378,0.0003294614,0.01980037,0.0009336365,0.0001096049,0.001107019,0.0006578002,0.4968421,0.4052426,0.01379776,0.00973288,0.0497543],"study_design_scores_gemma":[0.00006652033,0.0008727401,0.006882695,0.00004585101,0.00005417687,0.0005486635,0.00009188626,0.7630189,0.2123462,0.001155854,0.01484377,0.00007280276],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.763692,0.0004938398,0.2068687,0.0003433789,0.00004749873,0.0005131347,0.00272053,0.003563113,0.02175773],"genre_scores_gemma":[0.9180531,0.0002065814,0.07531565,0.00006361265,0.000006507732,0.0003867883,0.002069728,0.0003490173,0.00354919],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002792042,"threshold_uncertainty_score":0.008665085,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05301156904200601,"score_gpt":0.3224851058020861,"score_spread":0.2694735367600801,"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."}}