{"id":"W2104590050","doi":"10.1109/tbme.2007.912635","title":"DCT-Based Complexity Regularization for EM Tomographic Reconstruction","year":2008,"lang":"en","type":"article","venue":"IEEE Transactions on Biomedical Engineering","topic":"Medical Imaging Techniques and Applications","field":"Medicine","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"","keywords":"Discrete cosine transform; Regularization (linguistics); Iterative reconstruction; Algorithm; Estimator; Tomographic reconstruction; Mathematics; Tomography; Computer science; Mathematical optimization; Artificial intelligence; Image (mathematics); Statistics","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":[0.001129191,0.0007084011,0.0005549174,0.0005875788,0.0003871988,0.000885645,0.0007725463,0.0009956753,0.00338627],"category_scores_gemma":[0.004941611,0.0004048235,0.0005896276,0.0006669359,0.0005816377,0.000865533,0.001148566,0.001581538,0.001441591],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006583155,"about_ca_system_score_gemma":0.000760184,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001138545,"about_ca_topic_score_gemma":0.001498106,"domain_scores_codex":[0.9993666,0.0001780062,0.00004052696,0.00006904027,0.0003245113,0.00002133252],"domain_scores_gemma":[0.9989156,0.0006259395,0.00008853006,0.0001505476,0.0001703853,0.00004906152],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002080986,0.0000675917,0.0006536113,0.0004379848,0.00007948526,0.0003336097,0.0001354142,0.2379058,0.06895347,0.3507605,0.01058056,0.329884],"study_design_scores_gemma":[0.00002680474,0.00003474635,0.0002694644,0.00002416091,0.00001134882,0.0003712482,0.000009632466,0.940722,0.01331048,0.02798302,0.01720241,0.0000346811],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0005120613,0.00007630587,0.9986218,0.00007921481,0.00001980157,0.0000140009,0.00002206012,0.00009494389,0.0005598235],"genre_scores_gemma":[0.02657977,0.0003864469,0.9703299,0.0001063366,0.00008668259,0.000119908,0.0001746595,0.0001971268,0.002019151],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00338627,"threshold_uncertainty_score":0.01132816,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03365103208729532,"score_gpt":0.2664506909541123,"score_spread":0.232799658866817,"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."}}