{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001677063,0.00009035718,0.000165767,0.00002425939,0.00007446655,0.00001059937,0.00009168559,0.00004347003,0.000007341112],"category_scores_gemma":[0.00005749092,0.00006484964,0.00005868549,0.0003078172,0.0001276324,0.00007022549,0.00004986776,0.0001440321,0.000009623036],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001850249,"about_ca_system_score_gemma":0.00007378942,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001252046,"about_ca_topic_score_gemma":4.03777e-7,"domain_scores_codex":[0.9992227,0.00002234347,0.00021794,0.0001773633,0.0002354158,0.0001242489],"domain_scores_gemma":[0.9993505,0.00004374031,0.0001148692,0.0003605023,0.00006767982,0.00006274685],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003629493,0.001576281,0.2279667,0.00169118,0.000178498,0.000005664913,0.001728415,0.01544786,0.5653439,0.005674078,0.0747335,0.1052909],"study_design_scores_gemma":[0.0004177155,0.00006109596,0.02945912,0.0002038159,0.00006917882,0.000001246392,0.000007002996,0.8703931,0.09697875,0.0003331768,0.001986113,0.0000897052],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.98348,0.000004114844,0.01095566,0.004724678,0.00005522564,0.0003984575,0.00002688428,0.0001220296,0.0002329651],"genre_scores_gemma":[0.9971803,0.00004578591,0.001182404,0.000393732,0.0001830924,0.00004597718,0.00008598217,0.00001805295,0.0008647032],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8549452,"threshold_uncertainty_score":0.2644492,"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."}}