{"id":"W4402572771","doi":"10.1002/acm2.14516","title":"Evaluation of a Metal Artifact Reduction Algorithm for Image Reconstruction on a Novel CBCT Platform","year":2024,"lang":"en","type":"article","venue":"Journal of Applied Clinical Medical Physics","topic":"Advanced X-ray and CT Imaging","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Nova Scotia Health Authority; Dalhousie University","funders":"","keywords":"Imaging phantom; Hounsfield scale; Artifact (error); Image quality; Voxel; Nuclear medicine; Mean squared error; Signal-to-noise ratio (imaging); Iterative reconstruction; Noise (video); Image noise; Biomedical engineering; Artificial intelligence; Computer science; Mathematics; Computed tomography; Medicine; Image (mathematics); Radiology; Statistics","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":[0.002975404,0.0001352558,0.0003888043,0.00007105013,0.00003008306,0.00001854127,0.0001100142,0.0001379092,0.00004232464],"category_scores_gemma":[0.0003714385,0.0001107888,0.000299911,0.0001804254,0.0001228897,0.0002816377,0.00001122408,0.0007251068,0.000008374569],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001140861,"about_ca_system_score_gemma":0.0001884925,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":2.40028e-7,"about_ca_topic_score_gemma":1.038086e-7,"domain_scores_codex":[0.9976295,0.00001952723,0.001012162,0.0001475697,0.001032653,0.0001585452],"domain_scores_gemma":[0.9987861,0.0004843525,0.0002167492,0.0001086165,0.0002587811,0.0001454025],"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":[0.00006456955,0.0001319677,3.172827e-7,0.00005628423,0.0002491144,0.00000175781,0.00005688948,0.004288446,0.006720625,0.001161458,0.0001428628,0.9871257],"study_design_scores_gemma":[0.002086989,0.0003733725,0.00002538293,0.0004376674,0.000638333,0.0001047628,0.0001990273,0.8734336,0.02996898,0.09192684,0.0006255415,0.0001794863],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08687586,0.0001352011,0.9082103,0.0001123569,0.002954865,0.0002608474,0.00001304909,0.00005949776,0.001377998],"genre_scores_gemma":[0.9464681,0.0001179887,0.05005994,0.00003523982,0.003250273,0.00001939254,0.00000639892,0.00003920708,0.000003456802],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9869462,"threshold_uncertainty_score":0.4517835,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06589326775162008,"score_gpt":0.3746636526095269,"score_spread":0.3087703848579069,"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."}}