{"id":"W2040856723","doi":"10.1088/0031-9155/54/3/022","title":"Motion correction of PET brain images through deconvolution: II. Practical implementation and algorithm optimization","year":2009,"lang":"en","type":"article","venue":"Physics in Medicine and Biology","topic":"Medical Imaging Techniques and Applications","field":"Medicine","cited_by":33,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Sivas Cumhuriyet Üniversitesi","keywords":"Deblurring; Deconvolution; Computer vision; Computer science; Artificial intelligence; Image quality; Imaging phantom; Motion blur; Algorithm; Blind deconvolution; Modality (human–computer interaction); Image restoration; Image processing; Image (mathematics); Nuclear medicine; Medicine","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001349442,0.0006864324,0.0004785248,0.0004031488,0.000381209,0.0005900321,0.0005240191,0.0008575877,0.000896132],"category_scores_gemma":[0.00266964,0.0004513226,0.0004005694,0.0003697019,0.0005425753,0.0006493373,0.0006576367,0.0006420301,0.0004887244],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004959674,"about_ca_system_score_gemma":0.001033846,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002489973,"about_ca_topic_score_gemma":0.002860439,"domain_scores_codex":[0.9996434,0.0001229674,0.00002199429,0.00004585486,0.0001374346,0.00002824365],"domain_scores_gemma":[0.9994879,0.000299365,0.00003947443,0.00006366632,0.00009927297,0.00001037132],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002818827,0.000105605,0.001487489,0.0003797709,0.00007359131,0.0001751781,0.0002659202,0.2554256,0.1352714,0.01274914,0.0008062766,0.5929781],"study_design_scores_gemma":[0.00004241924,0.0001606648,0.001165545,0.00003176496,0.00002932903,0.0004016029,0.00004913112,0.9061953,0.08163599,0.005373426,0.004872886,0.00004203582],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006609502,0.0001421916,0.9926161,0.00006775588,0.000004478849,0.00003359995,0.000005848845,0.0001888474,0.0003317975],"genre_scores_gemma":[0.06128548,0.0003046326,0.9375363,0.00001502057,0.000008788068,0.00009202604,0.00002494218,0.00007414046,0.0006586587],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002489973,"threshold_uncertainty_score":0.007136643,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1318242695283202,"score_gpt":0.4795418695648893,"score_spread":0.3477176000365692,"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."}}