{"id":"W1975054804","doi":"10.1109/tip.2013.2280185","title":"Adaptive Projection Selection for Computed Tomography","year":2013,"lang":"en","type":"article","venue":"IEEE Transactions on Image Processing","topic":"Medical Imaging Techniques and Applications","field":"Medicine","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Projection (relational algebra); Imaging phantom; Iterative reconstruction; Computer vision; Artificial intelligence; Computer science; Image quality; Tomographic reconstruction; Image-guided radiation therapy; Medical imaging; Mathematics; Tomography; Image (mathematics); Algorithm; Optics; Physics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0000903185,0.0001411399,0.000167266,0.0002372434,0.0003222924,0.00007448436,0.00005341507,0.00008299166,0.00009159267],"category_scores_gemma":[0.0000067886,0.0001258886,0.000117023,0.0005636284,0.00009149376,0.0002793682,5.368317e-7,0.0002639106,0.00002543485],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006576258,"about_ca_system_score_gemma":0.00008517028,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007896014,"about_ca_topic_score_gemma":0.000003247894,"domain_scores_codex":[0.9990911,0.00001503249,0.0002192777,0.0002921811,0.0001652056,0.0002172154],"domain_scores_gemma":[0.9992491,0.00004020305,0.00007203191,0.0001209663,0.0003998554,0.0001178354],"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":[0.0001905824,0.001070077,0.00004063593,0.0004083627,0.00008178288,8.385387e-7,0.0002763586,0.00008525251,0.2546618,0.00004012229,0.008223642,0.7349205],"study_design_scores_gemma":[0.001137316,0.0006307057,0.0002407375,0.0003600334,0.0001922705,0.00008573086,0.0001508797,0.666921,0.3277617,0.0009841602,0.001274223,0.0002612329],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006474684,0.00001970389,0.9889485,0.001916761,0.00007082117,0.001510051,0.000006178351,0.0006212491,0.0004320605],"genre_scores_gemma":[0.6976597,0.000004902498,0.300286,0.0003971617,0.00007698131,0.00118474,0.000006446608,0.00002586714,0.000358211],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7346593,"threshold_uncertainty_score":0.513359,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02475756916716722,"score_gpt":0.307165445416454,"score_spread":0.2824078762492868,"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."}}