{"id":"W2116746070","doi":"10.5565/rev/elcvia.508","title":"Comprehensive Analysis of High-Performance Computing Methods for Filtered Back-Projection","year":2013,"lang":"en","type":"article","venue":"ELCVIA Electronic Letters on Computer Vision and Image Analysis","topic":"Medical Imaging Techniques and Applications","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Servier; Technische Universität München; University of Alberta","keywords":"Computer science; CUDA; DirectX; OpenGL; Pipeline (software); Computer graphics (images); Projection (relational algebra); Graphics processing unit; Imaging phantom; Graphics; Graphics pipeline; Computational science; Parallel computing; Artificial intelligence; 3D computer graphics; Algorithm; Visualization","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":[0.0003107146,0.0002227428,0.0008645681,0.0009774364,0.0001314173,0.00006106636,0.0001405719,0.0000662204,0.0001877263],"category_scores_gemma":[0.0000144935,0.0001751906,0.0004217677,0.002016042,0.000111792,0.0001209714,0.00006940608,0.0002435982,0.000009735627],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007500307,"about_ca_system_score_gemma":0.00002370801,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001778438,"about_ca_topic_score_gemma":0.000001863945,"domain_scores_codex":[0.9982945,0.0001142218,0.0005020403,0.0005156707,0.0002033403,0.0003701959],"domain_scores_gemma":[0.99862,0.0003043573,0.0002333118,0.0004688086,0.0002528566,0.0001206728],"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":[0.0002121287,0.0005967513,0.002185898,0.0003070474,0.01585439,0.000002382939,0.000241188,0.001278503,0.3602415,0.0004744899,0.02896901,0.5896367],"study_design_scores_gemma":[0.0007077202,0.0005814952,0.03258088,0.00003849076,0.004431776,0.00000475838,0.000008211954,0.9486444,0.0107379,0.00004253213,0.002044323,0.0001775387],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4105403,0.00003557702,0.5852208,0.003753391,0.00001597834,0.0003662327,0.000003173941,0.00004871999,0.00001587118],"genre_scores_gemma":[0.6502678,0.00008450648,0.3453369,0.003987311,0.00006440884,0.00003152659,0.0001742257,0.00001384695,0.00003946541],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9473659,"threshold_uncertainty_score":0.7144065,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01290012997851623,"score_gpt":0.3451562243317155,"score_spread":0.3322560943531992,"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."}}