{"id":"W4233677886","doi":"10.32920/14639349","title":"Accelerated Compressed Sensing Based CT Image Reconstruction","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Medical Imaging Techniques and Applications","field":"Medicine","cited_by":0,"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); Compressed sensing; Algorithm; Computer science; Computation; Noise (video); Reconstruction algorithm; Fourier transform; Process (computing); Radon 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004928936,0.0005626965,0.0003987162,0.0004464709,0.0001700137,0.0004827116,0.0005267725,0.000739598,0.002269851],"category_scores_gemma":[0.001545276,0.0002270276,0.0003944081,0.0005653137,0.0003902328,0.000682105,0.0005962435,0.000811558,0.0004915372],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000297114,"about_ca_system_score_gemma":0.000575266,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002004665,"about_ca_topic_score_gemma":0.00172537,"domain_scores_codex":[0.9996406,0.00008517034,0.00001636316,0.00003810921,0.0002036479,0.00001611749],"domain_scores_gemma":[0.9995174,0.0002676664,0.00004104538,0.00005536814,0.0001013254,0.00001730246],"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":[0.000747197,0.0001399967,0.0008078713,0.0004702241,0.00008229912,0.0004566154,0.000252401,0.3985367,0.1747786,0.02744562,0.004468351,0.3918142],"study_design_scores_gemma":[0.0000181003,0.00004230037,0.0001627901,0.00000872766,0.000007431905,0.0002036412,0.000006518699,0.9780005,0.01757614,0.001975677,0.001985399,0.00001272185],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01413181,0.0003872261,0.9833149,0.0001982701,0.00005474275,0.00005398989,0.00007821252,0.0005694955,0.001211336],"genre_scores_gemma":[0.1495395,0.0005881572,0.8462123,0.0001521047,0.00007220799,0.00009827969,0.0003074873,0.0001353087,0.002894669],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002269851,"threshold_uncertainty_score":0.007593453,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05892505664576744,"score_gpt":0.3458337542484063,"score_spread":0.2869086976026389,"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."}}