{"id":"W1995684209","doi":"10.1118/1.2214305","title":"Octree indexing of DICOM images for voxel number reduction and improvement of Monte Carlo simulation computing efficiency","year":2006,"lang":"en","type":"article","venue":"Medical Physics","topic":"Medical Imaging Techniques and Applications","field":"Medicine","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"Hôtel-Dieu de Québec; Université Laval","funders":"SLAC National Accelerator Laboratory; U.S. Nuclear Regulatory Commission","keywords":"Voxel; Computer science; Monte Carlo method; Imaging phantom; Octree; Algorithm; DICOM; Artificial intelligence; Mathematics; Statistics; Physics; Optics","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":[0.0007316103,0.0003959079,0.0004386214,0.001740314,0.00033893,0.0008341682,0.0006847907,0.0003088774,0.002295414],"category_scores_gemma":[0.006846257,0.0001973522,0.0003234084,0.002248293,0.0002342278,0.0008992716,0.0006795388,0.0003942942,0.000537992],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004376941,"about_ca_system_score_gemma":0.0007580792,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00243039,"about_ca_topic_score_gemma":0.002803445,"domain_scores_codex":[0.9993312,0.0002253138,0.00006654866,0.00003563062,0.0002899715,0.00005130123],"domain_scores_gemma":[0.9982163,0.0007661962,0.0001422505,0.0004510803,0.0003753309,0.00004882076],"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":[0.0006507044,0.0001798997,0.001908904,0.0002380951,0.00004322165,0.0001817962,0.0002948117,0.07080925,0.0483847,0.03139202,0.006972812,0.8389437],"study_design_scores_gemma":[0.0001017513,0.0002130639,0.001907214,0.00004392971,0.00004064237,0.000638497,0.00008706209,0.8824105,0.08248973,0.01250376,0.01951065,0.00005315546],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.04472507,0.0006388657,0.9500713,0.0001768299,0.000077587,0.0001365402,0.0001931325,0.001658852,0.002321915],"genre_scores_gemma":[0.1862444,0.0005189726,0.8113176,0.00006259947,0.00005009635,0.0001334801,0.0003935084,0.0003007753,0.0009785227],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00243039,"threshold_uncertainty_score":0.007678866,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01530003371055012,"score_gpt":0.3269950476094031,"score_spread":0.311695013898853,"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."}}