{"id":"W2094344171","doi":"10.1118/1.4914143","title":"GPU‐accelerated regularized iterative reconstruction for few‐view cone beam CT","year":2015,"lang":"en","type":"article","venue":"Medical Physics","topic":"Medical Imaging Techniques and Applications","field":"Medicine","cited_by":44,"is_retracted":false,"has_abstract":true,"ca_institutions":"Centre hospitalier universitaire de Québec; Hôtel-Dieu de Québec; Polytechnique Montréal; Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Iterative reconstruction; Computer science; Image quality; Regularization (linguistics); Algorithm; Computer vision; Image-guided radiation therapy; Convex optimization; Mathematical optimization; Artificial intelligence; Regular polygon; Medical imaging; Mathematics; Image (mathematics)","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":[0.0004103673,0.000507905,0.0003338499,0.0003095994,0.0001712636,0.0005356245,0.0006745186,0.0004730476,0.001504801],"category_scores_gemma":[0.001279848,0.0002910657,0.0005232617,0.0002857094,0.0002595915,0.0002274546,0.0004378035,0.0005784227,0.0005064834],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002677071,"about_ca_system_score_gemma":0.0006521753,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002125457,"about_ca_topic_score_gemma":0.0026773,"domain_scores_codex":[0.9997911,0.0000619599,0.000008479157,0.00001969576,0.0001084948,0.00001032656],"domain_scores_gemma":[0.9997041,0.0001199875,0.00003469038,0.00004642568,0.00007595518,0.0000189071],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0003813012,0.0001212766,0.00300684,0.0003579429,0.0001321047,0.0003887209,0.0002551792,0.6229618,0.1115985,0.008056854,0.004217831,0.2485217],"study_design_scores_gemma":[0.00001050033,0.00002749975,0.0002849863,0.000007418982,0.000006300974,0.0001171451,0.000007441232,0.9907991,0.006779317,0.0005448459,0.001407825,0.000007424093],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01406965,0.000122581,0.9843634,0.00005278513,0.0000123162,0.00003832411,0.00003981566,0.0005538372,0.000747334],"genre_scores_gemma":[0.172649,0.0002473361,0.8247399,0.00004470079,0.00001639664,0.000117609,0.0002610097,0.0003529055,0.001571249],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002125457,"threshold_uncertainty_score":0.005034089,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07277976901500922,"score_gpt":0.3543993438624782,"score_spread":0.281619574847469,"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."}}