{"id":"W2024961026","doi":"10.1117/12.878408","title":"Quantitative evaluation method of noise texture for iteratively reconstructed x-ray CT images","year":2011,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Medical Imaging Techniques and Applications","field":"Medicine","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; National Institutes of Health","keywords":"Artificial intelligence; Image noise; Iterative reconstruction; Noise (video); Computer science; Histogram; Computer vision; Metric (unit); Image texture; Pattern recognition (psychology); Image resolution; Similarity (geometry); Mathematics; Image processing; Image (mathematics)","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003571972,0.0006243391,0.0005590247,0.002761606,0.000310025,0.001337829,0.000667961,0.0007039667,0.0008253276],"category_scores_gemma":[0.01834374,0.0002396583,0.0003912549,0.001178381,0.0008319828,0.001062484,0.0007476706,0.0004623239,0.0001648264],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006518327,"about_ca_system_score_gemma":0.0004677915,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009965061,"about_ca_topic_score_gemma":0.0008568673,"domain_scores_codex":[0.9975586,0.0005821235,0.000198529,0.0002364778,0.001336741,0.0000875847],"domain_scores_gemma":[0.9922531,0.003453809,0.001016261,0.0005452975,0.002560473,0.0001710318],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00180693,0.0002384557,0.02031006,0.0009488777,0.0002229849,0.0004649168,0.000728869,0.1662887,0.4459248,0.009412717,0.001009184,0.3526435],"study_design_scores_gemma":[0.00003931887,0.000564326,0.01737199,0.00005459285,0.00008345527,0.0008509782,0.0001946756,0.8331263,0.1439891,0.00208515,0.001513698,0.0001264635],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1714876,0.0004695825,0.8260508,0.00008128516,0.00004318883,0.0001381342,0.0001503211,0.0005355747,0.001043464],"genre_scores_gemma":[0.6611655,0.000295998,0.3373235,0.00003798807,0.00003811381,0.000149336,0.0003062991,0.0001716136,0.0005115333],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003571972,"threshold_uncertainty_score":0.01889062,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03857005621136782,"score_gpt":0.3223773855402627,"score_spread":0.2838073293288949,"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."}}