{"id":"W4403737322","doi":"10.1007/s13246-024-01491-0","title":"Investigating 4D respiratory cone-beam CT imaging for thoracic interventions on robotic C-arm systems: a deformable phantom study","year":2024,"lang":"en","type":"article","venue":"Physical and Engineering Sciences in Medicine","topic":"Advanced Radiotherapy Techniques","field":"Physics and Astronomy","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"National Institute of Biomedical Imaging and Bioengineering; National Institutes of Health; Siemens Healthineers; University of Sydney","keywords":"Imaging phantom; Cone beam computed tomography; Cone beam ct; Image quality; Breathing; Computer vision; Nuclear medicine; Computer science; Medicine; Artificial intelligence; Radiology; Computed tomography; Image (mathematics); Anatomy","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":[0.0004984724,0.0001359419,0.000233757,0.0001691763,0.0001156784,0.00007673533,0.0001234796,0.000005636551,0.000003352678],"category_scores_gemma":[0.00002725068,0.00009769876,0.00004241553,0.0003870625,0.0001860137,0.0002332452,0.0000246337,0.0001603892,3.909189e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003357733,"about_ca_system_score_gemma":0.00001997644,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008083129,"about_ca_topic_score_gemma":7.081951e-7,"domain_scores_codex":[0.9991304,0.00001706768,0.0002112541,0.0002664117,0.0001531845,0.000221696],"domain_scores_gemma":[0.9995946,0.000217999,0.00003301093,0.00008899376,0.00001439712,0.00005096587],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002309781,0.0010847,0.02893924,0.002742689,0.0002462011,0.00003253494,0.01070544,0.6502414,0.03819586,0.1844191,0.0004349985,0.08293471],"study_design_scores_gemma":[0.0003269399,0.0006625286,0.0004067635,0.002722024,0.00003310521,0.000002314674,0.001244512,0.9908227,0.0009523652,0.002230241,0.0004030181,0.0001934492],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7056885,0.001555938,0.2914078,0.0001160627,0.0003518575,0.0005317981,0.000003681298,0.0001659412,0.0001784507],"genre_scores_gemma":[0.9978607,0.000002503865,0.00151543,0.0000284099,0.0003979575,0.000161935,0.000001473289,0.00001402708,0.00001761569],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3405813,"threshold_uncertainty_score":0.398404,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03955481811018861,"score_gpt":0.3790653881514605,"score_spread":0.3395105700412719,"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."}}