{"id":"W2912840639","doi":"10.1002/mp.13418","title":"Two novel PET image restoration methods guided by PET‐MR kernels: Application to brain imaging","year":2019,"lang":"en","type":"article","venue":"Medical Physics","topic":"Medical Imaging Techniques and Applications","field":"Medicine","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; Montreal Neurological Institute and Hospital","funders":"Centre For Medical Engineering, King’s College London; Menzies Centre for Australian Studies, King's College London, University of London; Canadian Institutes of Health Research; Engineering and Physical Sciences Research Council; Pfizer; Natural Sciences and Engineering Research Council of Canada; King's College London","keywords":"Artificial intelligence; Positron emission tomography; Image quality; Iterative reconstruction; Computer science; Kernel (algebra); Voxel; Partial volume; Computer vision; Pattern recognition (psychology); Image restoration; Image resolution; Image processing; Mathematics; Nuclear medicine; Image (mathematics); Medicine","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":[0.0007280105,0.0005919596,0.0004512611,0.0005483402,0.0002192555,0.0005712982,0.0005782308,0.001126311,0.0008570232],"category_scores_gemma":[0.001271427,0.0003239278,0.0007685276,0.0003134903,0.000448602,0.0004930374,0.0004490589,0.0007198811,0.000371082],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003600126,"about_ca_system_score_gemma":0.0004657275,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001069735,"about_ca_topic_score_gemma":0.001268582,"domain_scores_codex":[0.99975,0.00004545377,0.00001724873,0.00004176047,0.0001229826,0.00002249874],"domain_scores_gemma":[0.9995467,0.0001239585,0.00007733582,0.00007976348,0.0001405176,0.00003178302],"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":[0.0004726086,0.0002709378,0.001615236,0.0006648959,0.0002231111,0.0007795648,0.0003082514,0.09868393,0.3164837,0.006146178,0.002511634,0.5718399],"study_design_scores_gemma":[0.00007830508,0.0004061761,0.002488609,0.00003313123,0.0001067994,0.002935858,0.00004683133,0.8069613,0.1757618,0.001460202,0.009622587,0.00009838231],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03067745,0.0008377082,0.9669456,0.0002363056,0.00006827425,0.00007280089,0.00003212222,0.0005387261,0.0005909418],"genre_scores_gemma":[0.1607182,0.000757543,0.8357312,0.00008016769,0.00005755075,0.00009210232,0.00006611372,0.0001153935,0.002381675],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.001126311,"threshold_uncertainty_score":0.003850162,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02817416040688481,"score_gpt":0.4134445121262523,"score_spread":0.3852703517193675,"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."}}