{"id":"W2126750265","doi":"10.1109/tns.2003.817388","title":"Application of reconstruction techniques to correctly identify the myocardium in the presence of overlying organs","year":2003,"lang":"en","type":"article","venue":"IEEE Transactions on Nuclear Science","topic":"Medical Imaging Techniques and Applications","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Collimator; Imaging phantom; Iterative reconstruction; Attenuation; Monte Carlo method; Image quality; Correction for attenuation; Image resolution; Computer science; Computer vision; Artificial intelligence; Medical physics; Nuclear medicine; Physics; Biomedical engineering; Optics; Image (mathematics); Mathematics; Medicine","routes":{"ca_aff":true,"ca_fund":false,"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":[0.002385479,0.0009496128,0.000349239,0.0005549637,0.000202669,0.000536119,0.0004863388,0.001041003,0.0008004656],"category_scores_gemma":[0.01278055,0.0004390746,0.0004137391,0.0003886042,0.0004269844,0.0006142627,0.0005943049,0.0007785258,0.0004965142],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001458606,"about_ca_system_score_gemma":0.00041784,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003564184,"about_ca_topic_score_gemma":0.0004197517,"domain_scores_codex":[0.9993083,0.0003096927,0.0000505318,0.00006606151,0.0002213648,0.00004404529],"domain_scores_gemma":[0.9965981,0.002109962,0.0003837075,0.0004201985,0.0004339868,0.00005408687],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0009728982,0.0002063278,0.008832138,0.0003006382,0.0002289072,0.0006051766,0.0004026339,0.1313058,0.547312,0.002699436,0.0005186964,0.3066153],"study_design_scores_gemma":[0.0001043903,0.0009442404,0.008409705,0.00005542865,0.0001212035,0.003841058,0.00007200847,0.5878182,0.3946873,0.001605111,0.002272606,0.0000687678],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2063615,0.0008105045,0.7908422,0.0002003638,0.0000314646,0.0001093258,0.00004276783,0.0009093126,0.000692467],"genre_scores_gemma":[0.3012039,0.000560314,0.6973398,0.00007600177,0.00001540988,0.00005532438,0.000107074,0.0001187356,0.0005234449],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002385479,"threshold_uncertainty_score":0.0126158,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01900106199043976,"score_gpt":0.3129300644054681,"score_spread":0.2939290024150283,"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."}}