{"id":"W1978768524","doi":"10.1118/1.4875688","title":"Noise, sampling, and the number of projections in cone-beam CT with a flat-panel detector","year":2014,"lang":"en","type":"article","venue":"Medical Physics","topic":"Digital Radiography and Breast Imaging","field":"Medicine","cited_by":50,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"National Institutes of Health; National Cancer Institute; Tianjin University; Johns Hopkins University","keywords":"Noise (video); Quantum noise; Optical transfer function; Gradient noise; Image noise; Value noise; Sampling (signal processing); Noise power; Clutter; Flat panel detector; Detector; Imaging phantom; Noise reduction; Cone beam computed tomography; Optics; Aliasing; Physics; Noise measurement; Mathematics; Noise floor; Computer science; Acoustics; Computer vision; Filter (signal processing); Power (physics); Telecommunications; Quantum; Image (mathematics); Radar; Medicine","routes":{"ca_aff":true,"ca_fund":false,"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":[0.00296606,0.0005327863,0.0004392381,0.0005270231,0.0002754645,0.0007437613,0.0005672335,0.0006342799,0.0005033063],"category_scores_gemma":[0.01790569,0.000599413,0.0002710657,0.0005054105,0.0008213108,0.001203755,0.0004903661,0.0003839466,0.0001836192],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000888448,"about_ca_system_score_gemma":0.0005868935,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001579414,"about_ca_topic_score_gemma":0.001892978,"domain_scores_codex":[0.9982302,0.0005482945,0.00007396993,0.0003079501,0.0007660753,0.00007344806],"domain_scores_gemma":[0.9856936,0.01166247,0.001171981,0.0004147906,0.000827257,0.0002299709],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.005195828,0.000456021,0.1001822,0.0008080502,0.0001956227,0.001408719,0.0007825761,0.1715342,0.5952098,0.002705394,0.0003913707,0.1211301],"study_design_scores_gemma":[0.00007467923,0.001997759,0.1642192,0.0001077818,0.0002676926,0.00303541,0.0001890171,0.5245864,0.3015772,0.002777709,0.001032026,0.0001351526],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7803871,0.001458841,0.2168512,0.0001456319,0.0000146545,0.00006596469,0.00007789162,0.0002396224,0.0007590691],"genre_scores_gemma":[0.9620318,0.0002730326,0.03717019,0.00006496086,0.00001122476,0.00003847747,0.00009000578,0.00005098122,0.0002692306],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00296606,"threshold_uncertainty_score":0.01568621,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01697866907836315,"score_gpt":0.2700747909607712,"score_spread":0.253096121882408,"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."}}