{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009879562,0.0006498481,0.0003150723,0.0006688553,0.0001806643,0.0006594864,0.0007118763,0.0006184885,0.001469672],"category_scores_gemma":[0.002343763,0.0001957523,0.0003382713,0.000469894,0.0002288232,0.0004323491,0.0004037505,0.0003350251,0.0005006985],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004570442,"about_ca_system_score_gemma":0.000689816,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001564381,"about_ca_topic_score_gemma":0.001685943,"domain_scores_codex":[0.9994404,0.0000891027,0.00003427388,0.00008992049,0.0003094744,0.00003672466],"domain_scores_gemma":[0.9989414,0.0002959502,0.0001161905,0.0001157863,0.0004867947,0.00004390835],"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":[0.001268128,0.0002928057,0.003546213,0.0004025023,0.0001218695,0.0004026705,0.000151402,0.1109566,0.4958219,0.002118318,0.00133414,0.3835835],"study_design_scores_gemma":[0.00004453582,0.0005071776,0.003606039,0.00002579444,0.00006082995,0.0007478872,0.0000322112,0.8036266,0.1878604,0.0002539069,0.003205513,0.00002920635],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1151528,0.0003972488,0.8812205,0.0001028511,0.00003405461,0.000164461,0.00008556243,0.00163912,0.001203555],"genre_scores_gemma":[0.2350606,0.0002141614,0.763047,0.00005554509,0.00001389617,0.00008091774,0.0002297805,0.0001746182,0.001123448],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001564381,"threshold_uncertainty_score":0.005224884,"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."}}