{"id":"W2002670875","doi":"10.1016/j.mri.2007.08.002","title":"Rapid passive MR catheter visualization for endovascular therapy using nonsymmetric truncated k-space sampling strategies","year":2008,"lang":"en","type":"article","venue":"Magnetic Resonance Imaging","topic":"Advanced MRI Techniques and Applications","field":"Medicine","cited_by":7,"is_retracted":false,"has_abstract":false,"ca_institutions":"Foothills Medical Centre; University of Calgary","funders":"","keywords":"Sampling (signal processing); Tracking (education); Computer vision; k-space; Computer science; Catheter; Artificial intelligence; Projection (relational algebra); Visualization; Data acquisition; Algorithm; Radiology; Medicine; Magnetic resonance imaging","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":[0.0001124173,0.0002199591,0.000298074,0.0002191527,0.0003108537,0.00004164609,0.0001079904,0.00005574257,0.00004321587],"category_scores_gemma":[0.00007148362,0.0002015636,0.0001527216,0.0006598803,0.0001351756,0.0001875084,0.00002563606,0.000121748,0.000003363892],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009144887,"about_ca_system_score_gemma":0.0001285149,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009123954,"about_ca_topic_score_gemma":9.721623e-7,"domain_scores_codex":[0.9986557,0.00002525304,0.000309254,0.0004143338,0.0002236727,0.0003718278],"domain_scores_gemma":[0.9990476,0.0001014213,0.0001268289,0.0003869018,0.0002577844,0.00007942827],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000245029,0.0004714731,0.00444269,0.00008785459,0.00002079987,0.00004407288,0.001097951,0.0007152429,0.1351373,0.005995644,0.0006976013,0.8510443],"study_design_scores_gemma":[0.008236702,0.001115317,0.03072252,0.0004931055,0.0002194422,0.0008959775,0.001202087,0.2223873,0.09881346,0.004323476,0.630311,0.001279607],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1156708,0.0275137,0.8539754,0.0005024517,0.00005426044,0.001578814,0.00001361231,0.0002655821,0.0004254088],"genre_scores_gemma":[0.6702196,0.007046053,0.3206568,0.0005952431,0.0002518376,0.0005553256,0.00007975483,0.000118306,0.0004770597],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8497647,"threshold_uncertainty_score":0.8219525,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05026128765778465,"score_gpt":0.3402528643167915,"score_spread":0.2899915766590069,"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."}}