{"id":"W1529639780","doi":"10.1155/2015/161797","title":"Accelerated Compressed Sensing Based CT Image Reconstruction","year":2015,"lang":"en","type":"article","venue":"Computational and Mathematical Methods in Medicine","topic":"Medical Imaging Techniques and Applications","field":"Medicine","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto General Hospital; University Health Network; Toronto Metropolitan University; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Imaging phantom; Iterative reconstruction; Interpolation (computer graphics); Algorithm; Computer science; Computation; Compressed sensing; Noise (video); Reconstruction algorithm; Process (computing); Radon transform; Fourier transform; Image quality; Computer vision; Artificial intelligence; Image (mathematics); Mathematics; Optics; Physics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001332085,0.0001261311,0.0004104997,0.0001476164,0.00003995172,0.00001228289,0.00004798391,0.00004007044,0.0001400446],"category_scores_gemma":[0.00157668,0.00009141307,0.00002646215,0.0002485714,0.0003862602,0.00004735586,0.00003072579,0.0002361988,0.000007009283],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004033692,"about_ca_system_score_gemma":0.00008147455,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001119046,"about_ca_topic_score_gemma":1.247879e-7,"domain_scores_codex":[0.998768,0.0001618487,0.0004334798,0.000225918,0.0002655143,0.0001452904],"domain_scores_gemma":[0.9984071,0.0008939459,0.00007601258,0.0001406688,0.0002002999,0.0002819686],"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":[0.0003823966,0.001107261,0.001167167,0.00189382,0.0001274801,0.0003572342,0.001185087,0.0005068575,0.06485662,0.09830666,0.02820969,0.8018997],"study_design_scores_gemma":[0.001649632,0.00009221385,0.0004110939,0.0005407384,0.0000449119,0.00051981,0.0001123709,0.7056344,0.001132427,0.2889741,0.000800402,0.00008791971],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02105197,0.00005870846,0.959542,0.01524435,0.00005634707,0.0003343686,0.000001167109,0.0001164455,0.003594627],"genre_scores_gemma":[0.03952859,0.000005406865,0.9587625,0.001511541,0.00008831341,0.00001525278,0.00002545483,0.00001264372,0.00005026858],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.8018118,"threshold_uncertainty_score":0.3727717,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1849849036275722,"score_gpt":0.4855305657413084,"score_spread":0.3005456621137362,"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."}}