{"id":"W1998585214","doi":"10.1109/iembs.2011.6090766","title":"Accurate positioning of magnetic microparticles beyond the spatial resolution of clinical MRI scanners using susceptibility artifacts","year":2011,"lang":"en","type":"article","venue":"","topic":"Advanced MRI Techniques and Applications","field":"Medicine","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Artifact (error); Magnetic resonance imaging; Image resolution; Dipole; Nuclear magnetic resonance; SIGNAL (programming language); Resolution (logic); Magnetic nanoparticles; Materials science; Magnetic field; Physics; Optics; Biomedical engineering; Computer vision; Computer science; Artificial intelligence; Nanoparticle; Nanotechnology; Radiology; Engineering","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":[0.000917988,0.0003497889,0.0005973006,0.0002293973,0.0002989042,0.0007828918,0.0003620695,0.0007967489,0.0002728717],"category_scores_gemma":[0.002727235,0.0003297492,0.0002287233,0.0001814851,0.0005968061,0.0009525365,0.0005198424,0.0004071997,0.0002756912],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003573735,"about_ca_system_score_gemma":0.0005751443,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008890883,"about_ca_topic_score_gemma":0.0008304565,"domain_scores_codex":[0.9995628,0.0001102906,0.00004273305,0.0000614655,0.000179417,0.00004317501],"domain_scores_gemma":[0.9986665,0.0006210335,0.0002656409,0.0001878043,0.0002019109,0.00005717525],"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":[0.0002097539,0.00002430625,0.001637577,0.0001715945,0.00001833652,0.0005396396,0.0001756779,0.009292578,0.9751877,0.0009232432,0.0001561183,0.01166355],"study_design_scores_gemma":[0.00005024646,0.0004957947,0.004226983,0.00003163379,0.00005866132,0.001263406,0.0001231819,0.05294183,0.9355461,0.001453711,0.003766981,0.00004130592],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7883813,0.00384981,0.2050626,0.0004026547,0.0001008985,0.00008699711,0.0000602493,0.0004781406,0.001577341],"genre_scores_gemma":[0.9514648,0.000653167,0.04686366,0.0001574664,0.00001772842,0.00003232414,0.00004431791,0.00005114809,0.0007154073],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.000917988,"threshold_uncertainty_score":0.004854858,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1147272526897073,"score_gpt":0.3944530512612633,"score_spread":0.279725798571556,"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."}}