{"id":"W2009620901","doi":"10.1118/1.3021117","title":"Optimization of super‐resolution processing using incomplete image sets in PET imaging","year":2008,"lang":"en","type":"article","venue":"Medical Physics","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"Occupational and Environmental Medical Association of Canada","funders":"","keywords":"Medical imaging; Image processing; Image resolution; Iterative reconstruction; Computer vision; Image registration; Resolution (logic); Computer science; Pet imaging; Positron emission tomography; Artificial intelligence; Medical physics; Nuclear medicine; Image (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":[0.001187085,0.0006626652,0.0006307641,0.0004172895,0.0001663223,0.0005970701,0.0007831921,0.0005577992,0.0006382965],"category_scores_gemma":[0.002664423,0.0005136002,0.0005675158,0.0004509348,0.0004813594,0.00100949,0.0005865476,0.0005717754,0.0002202102],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004303648,"about_ca_system_score_gemma":0.0006284168,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001083255,"about_ca_topic_score_gemma":0.001622466,"domain_scores_codex":[0.9994074,0.0001580395,0.00003466821,0.00007251888,0.000287948,0.00003942343],"domain_scores_gemma":[0.9989942,0.0005696557,0.0001451891,0.0001093916,0.0001523259,0.00002930074],"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":[0.0004731166,0.0001483103,0.0008190359,0.0003037125,0.0001482698,0.0002550518,0.0002096401,0.6016431,0.2245356,0.006953362,0.000691964,0.1638188],"study_design_scores_gemma":[0.00001708418,0.0001168688,0.000575773,0.000008687087,0.0000268754,0.000114533,0.00001942275,0.9514264,0.04572848,0.0009344452,0.001012126,0.00001935463],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05256316,0.0002142424,0.9461411,0.00007216569,0.00001109344,0.00004527726,0.00002468495,0.0002834275,0.0006448583],"genre_scores_gemma":[0.1791436,0.000296932,0.8196201,0.00005258811,0.00001263524,0.00007720852,0.0001002099,0.0001100645,0.0005866644],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001187085,"threshold_uncertainty_score":0.006277978,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02602655143014419,"score_gpt":0.3016534115770466,"score_spread":0.2756268601469024,"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."}}