{"id":"W2111338112","doi":"10.1109/tmi.2003.817787","title":"Cone-beam reprojection using projection-matrices","year":2003,"lang":"en","type":"article","venue":"IEEE Transactions on Medical Imaging","topic":"Medical Imaging Techniques and Applications","field":"Medicine","cited_by":59,"is_retracted":false,"has_abstract":true,"ca_institutions":"Robarts Clinical Trials","funders":"","keywords":"Reprojection error; Maximum intensity projection; Projection (relational algebra); Computer vision; Voxel; Iterative reconstruction; Volume rendering; Artificial intelligence; Computer science; Pixel; Rendering (computer graphics); Iterative method; Algorithm; Visualization; Image (mathematics)","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000662374,0.0002166429,0.0002967545,0.0003295833,0.0003614685,0.00004212557,0.0001123069,0.0001417985,0.001204551],"category_scores_gemma":[0.0001885574,0.0001907277,0.000175122,0.000746127,0.0002451064,0.0001648581,0.000001483441,0.00081877,0.00006342283],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001752376,"about_ca_system_score_gemma":0.0003898506,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001864876,"about_ca_topic_score_gemma":0.000004054926,"domain_scores_codex":[0.9977612,0.00008349669,0.0004613012,0.0005111386,0.0008073054,0.0003755507],"domain_scores_gemma":[0.998722,0.0001454946,0.00009688532,0.0004449249,0.0001439866,0.0004467076],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0005746504,0.009846575,0.005136937,0.001211963,0.000796142,0.00103787,0.001400646,0.0008362812,0.2468129,0.003275757,0.0399716,0.6890987],"study_design_scores_gemma":[0.006470999,0.0004660539,0.0002229726,0.002431243,0.001265305,0.01032127,0.001552032,0.3372258,0.5098234,0.001277213,0.1273851,0.001558642],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03977188,0.00009908545,0.9508888,0.00373535,0.0006025172,0.0006301901,0.000004543061,0.0006475491,0.003620156],"genre_scores_gemma":[0.974063,0.0002374747,0.02255418,0.002191231,0.0001723811,0.0001438673,0.00000430975,0.00005109086,0.0005825089],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9342911,"threshold_uncertainty_score":0.9997085,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03012679137420248,"score_gpt":0.3435861829366216,"score_spread":0.3134593915624191,"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."}}