{"id":"W4380538826","doi":"10.1002/mrm.29754","title":"Optimization of acquisition parameters for cortical inhomogeneous magnetization transfer (ihMT) imaging using a rapid gradient echo readout","year":2023,"lang":"en","type":"article","venue":"Magnetic Resonance in Medicine","topic":"Advanced MRI Techniques and Applications","field":"Medicine","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary; McGill University; Montreal Neurological Institute and Hospital","funders":"","keywords":"Magnetization transfer; Nuclear magnetic resonance; Image resolution; Isotropy; Pulse sequence; Resolution (logic); White matter; Spin echo; Physics; Magnetic resonance imaging; Materials science; Optics; Chemistry; Computer science; Medicine; Artificial intelligence","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":[0.0005997634,0.0006171592,0.0002941635,0.0003316891,0.0001767091,0.0003330945,0.0003661163,0.0003311921,0.0007207323],"category_scores_gemma":[0.002562855,0.0002503089,0.0001984612,0.0002304392,0.0001919257,0.0004011912,0.0002887897,0.0003077082,0.0001940543],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003280371,"about_ca_system_score_gemma":0.0006053149,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001306881,"about_ca_topic_score_gemma":0.001837304,"domain_scores_codex":[0.9998845,0.00004503399,0.000009946188,0.00002449406,0.00002354275,0.00001244326],"domain_scores_gemma":[0.9995167,0.000278242,0.0000850649,0.00003065527,0.00007069163,0.00001863913],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001373449,0.0004071471,0.01209262,0.0007496387,0.0002106646,0.0005079289,0.0003875933,0.5044993,0.4025226,0.002248583,0.0009721364,0.07402837],"study_design_scores_gemma":[0.0003034128,0.001667034,0.01037677,0.0001098428,0.0002678511,0.0006180729,0.0001492588,0.685133,0.2939827,0.003597273,0.003683786,0.0001110745],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.705813,0.001296462,0.2891038,0.0002616098,0.0000274873,0.0002781873,0.0003221531,0.000772243,0.002125007],"genre_scores_gemma":[0.9038041,0.0003802924,0.09510546,0.000039386,0.000005384077,0.0001784615,0.0001203296,0.0001451222,0.0002213719],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001306881,"threshold_uncertainty_score":0.003171861,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03405697516580251,"score_gpt":0.3248212272633202,"score_spread":0.2907642520975177,"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."}}