{"id":"W1967581593","doi":"10.1109/42.993134","title":"Edge-preserving tomographic reconstruction with nonlocal regularization","year":2002,"lang":"en","type":"article","venue":"IEEE Transactions on Medical Imaging","topic":"Medical Imaging Techniques and Applications","field":"Medicine","cited_by":152,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"National Cancer Institute; National Institutes of Health; National Science Foundation","keywords":"Smoothing; Iterative reconstruction; Regularization (linguistics); Algorithm; Image restoration; Mathematical optimization; Mathematics; Boundary (topology); Pixel; Computer science; Tomographic reconstruction; Simulated annealing; Image processing; Artificial intelligence; Computer vision; 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":[0.0009767551,0.0004353908,0.000552913,0.000457086,0.0001962143,0.000574342,0.0009008204,0.0009710791,0.0006720117],"category_scores_gemma":[0.002749694,0.0003553522,0.0004771966,0.0005372971,0.0005595899,0.0009835922,0.0008536165,0.0006791865,0.0003399443],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002657305,"about_ca_system_score_gemma":0.0003981662,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005075572,"about_ca_topic_score_gemma":0.0008891217,"domain_scores_codex":[0.999589,0.0001263046,0.00001965002,0.00005605399,0.000189403,0.00001959891],"domain_scores_gemma":[0.9992684,0.0003191711,0.0001050552,0.0001890532,0.00009123098,0.00002710665],"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":[0.0002763096,0.0001477122,0.001750593,0.0002235999,0.0001198891,0.000362835,0.000189689,0.592829,0.143132,0.04327604,0.00168211,0.2160102],"study_design_scores_gemma":[0.0000139771,0.0000257149,0.0003124886,0.000003109757,0.000008150232,0.0001092568,0.00000595503,0.9806381,0.01248112,0.00552091,0.0008696605,0.00001153489],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01042711,0.00005612777,0.9889655,0.00005346135,0.000004528985,0.00001031923,0.000009733037,0.0002021048,0.0002711617],"genre_scores_gemma":[0.1690196,0.0001479381,0.8292533,0.00006428329,0.00001596619,0.00006118543,0.00007184894,0.0001525435,0.001213407],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.0009767551,"threshold_uncertainty_score":0.005165637,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01515465016785639,"score_gpt":0.2610944762209714,"score_spread":0.245939826053115,"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."}}