{"id":"W3017550021","doi":"10.1002/mp.14206","title":"Motion tracking of low‐activity fiducial markers using adaptive region of interest with list‐mode positron emission tomography","year":2020,"lang":"en","type":"article","venue":"Medical Physics","topic":"Medical Imaging Techniques and Applications","field":"Medicine","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa; Carleton University","funders":"","keywords":"Imaging phantom; Fiducial marker; Positron emission tomography; Tracking (education); Physics; Nuclear medicine; Monte Carlo method; Torso; Match moving; Medical imaging; Region of interest; Computer vision; Artificial intelligence; Computer science; Motion (physics); Medicine; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006434853,0.000431293,0.0002421795,0.0003642604,0.0001702613,0.0005086229,0.0009129716,0.0004330022,0.0004453292],"category_scores_gemma":[0.002174929,0.00035289,0.000408847,0.0003670524,0.0002664197,0.0005434072,0.000356599,0.0003059434,0.0002552791],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005546303,"about_ca_system_score_gemma":0.0005370744,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002271449,"about_ca_topic_score_gemma":0.002409629,"domain_scores_codex":[0.9997399,0.0000690228,0.00001817412,0.00005082016,0.0001072745,0.00001488172],"domain_scores_gemma":[0.9993286,0.0002681692,0.0001973603,0.00007042683,0.000113502,0.00002192739],"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":[0.0007565576,0.0001228542,0.01628918,0.000522255,0.0001533356,0.0004490919,0.0003433797,0.3328897,0.3847694,0.006020447,0.001146507,0.2565373],"study_design_scores_gemma":[0.00004982826,0.0003532964,0.004730084,0.00002359917,0.00008335731,0.0006515447,0.00001880937,0.800903,0.1875107,0.0008482821,0.004724775,0.000102776],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06850397,0.000460182,0.9295201,0.00004611135,0.00001870585,0.00005530022,0.00003821617,0.0008088469,0.0005484725],"genre_scores_gemma":[0.4127181,0.0003249582,0.5853805,0.00007470368,0.00001366001,0.0001111641,0.0001421166,0.0001582316,0.001076409],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002271449,"threshold_uncertainty_score":0.004516482,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07610329240927144,"score_gpt":0.3280607336834888,"score_spread":0.2519574412742173,"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."}}