{"id":"W2539724844","doi":"10.1109/nssmic.2008.4774209","title":"Iterative CT reconstruction using LabPET™ detector modules","year":2008,"lang":"en","type":"article","venue":"","topic":"Medical Imaging Techniques and Applications","field":"Medicine","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Detector; Iterative reconstruction; Interpolation (computer graphics); Computer science; Context (archaeology); Image resolution; Computer vision; Raster scan; Raster graphics; Iterative method; Image quality; Single-photon emission computed tomography; Artificial intelligence; Optics; Algorithm; Physics; Nuclear medicine; Image (mathematics)","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":[0.001016507,0.0006495595,0.0004159459,0.0004664353,0.0002248051,0.0009653557,0.001241821,0.0005430435,0.01396318],"category_scores_gemma":[0.002260673,0.0006868685,0.0003187064,0.000692288,0.0002076287,0.000614698,0.0008320264,0.0006233518,0.004119609],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005502703,"about_ca_system_score_gemma":0.001110773,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001404017,"about_ca_topic_score_gemma":0.001678625,"domain_scores_codex":[0.9996724,0.00006129142,0.00002871312,0.0000590075,0.0001542697,0.00002426381],"domain_scores_gemma":[0.9992533,0.0003162947,0.00006866863,0.0001156549,0.0002132177,0.00003291154],"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":[0.001542136,0.0002296927,0.004485036,0.0007480711,0.0001783522,0.0008681306,0.0004492673,0.1584792,0.2955587,0.01317916,0.01119623,0.513086],"study_design_scores_gemma":[0.0001161171,0.0003720309,0.002690598,0.00005562631,0.00007097609,0.001449424,0.0000510371,0.6249335,0.3389534,0.002039965,0.02916851,0.00009887773],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01400295,0.00006694595,0.9787923,0.00005376935,0.000009290923,0.0001345799,0.0002821111,0.00425101,0.002406958],"genre_scores_gemma":[0.1180195,0.0001377857,0.874863,0.00008874627,0.00000690271,0.0003413661,0.0006541091,0.0008069431,0.005081659],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01396318,"threshold_uncertainty_score":0.0467115,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07545906198514475,"score_gpt":0.3407949678204226,"score_spread":0.2653359058352778,"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."}}