{"id":"W1978568604","doi":"10.1002/mrm.20799","title":"Relaxometry model of strong dipolar perturbers for balanced‐SSFP: Application to quantification of SPIO loaded cells","year":2006,"lang":"en","type":"article","venue":"Magnetic Resonance in Medicine","topic":"Advanced MRI Techniques and Applications","field":"Medicine","cited_by":35,"is_retracted":false,"has_abstract":true,"ca_institutions":"Robarts Clinical Trials; Western University","funders":"","keywords":"Relaxometry; Flip angle; Imaging phantom; Steady-state free precession imaging; Superparamagnetism; Precession; Nuclear magnetic resonance; Relaxation (psychology); Magnetic resonance imaging; Larmor precession; Chemistry; Phase (matter); Sensitivity (control systems); Dipole; Computational physics; Spin echo; Magnetization; Physics; Condensed matter physics; Magnetic field; 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":[0.0003061587,0.0005125495,0.0003397067,0.0002151674,0.0001720145,0.0003322835,0.0006698438,0.0007357404,0.0006348408],"category_scores_gemma":[0.0008347888,0.0002451485,0.0003636523,0.0001412371,0.0003828975,0.0004883631,0.0002788837,0.0003261017,0.000269081],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007001144,"about_ca_system_score_gemma":0.0006758944,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003833631,"about_ca_topic_score_gemma":0.001582535,"domain_scores_codex":[0.9999161,0.00002136064,0.000004040077,0.00002327744,0.00002625628,0.000008969083],"domain_scores_gemma":[0.9998298,0.00007583443,0.00002897171,0.00001214278,0.00004182339,0.00001146876],"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":[0.0001024301,0.00004342651,0.0005657736,0.00009316339,0.00002570532,0.0003057162,0.0001220649,0.738165,0.2349756,0.01765845,0.0004103854,0.007532312],"study_design_scores_gemma":[0.000003710034,0.00001463032,0.00006714919,0.000001156178,0.000002292547,0.00002335547,0.000002974445,0.996172,0.002859646,0.0006155199,0.0002341534,0.000003477777],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1062219,0.0003018268,0.8899847,0.0003134735,0.00002860006,0.0000683031,0.0001055803,0.0002410786,0.002734553],"genre_scores_gemma":[0.8714188,0.0007981696,0.1182595,0.000170943,0.0000300858,0.0003203536,0.0002067235,0.0001023485,0.008692962],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003833631,"threshold_uncertainty_score":0.0076226,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0224934768926825,"score_gpt":0.3120331969472203,"score_spread":0.2895397200545378,"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."}}