{"id":"W4280569132","doi":"10.1002/mrm.29273","title":"Simultaneous high‐resolution T<sub>2</sub>‐weighted imaging and quantitative <scp>T</scp><sub>2</sub> mapping at low magnetic field strengths using a multiple TE and multi‐orientation acquisition approach","year":2022,"lang":"en","type":"article","venue":"Magnetic Resonance in Medicine","topic":"Advanced MRI Techniques and Applications","field":"Medicine","cited_by":45,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institute on Drug Abuse; National Institutes of Health; Medical Research Council Canada; Wellcome Trust; Medical Research Council; Bill and Melinda Gates Foundation","keywords":"Orientation (vector space); Imaging phantom; Voxel; Computer science; Image resolution; Resolution (logic); Visualization; Isotropy; Artificial intelligence; Intraclass correlation; Image quality; Computer vision; Nuclear magnetic resonance; Pattern recognition (psychology); Physics; Mathematics; Reproducibility; Optics; Image (mathematics); Statistics; Geometry","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004141029,0.0003702331,0.0005445782,0.000414556,0.0004713409,0.0000222984,0.0001188841,0.000113097,0.0000167154],"category_scores_gemma":[0.0007715626,0.0003719079,0.00004724411,0.0008294898,0.0003541993,0.0001407192,0.0001956421,0.0005262562,0.000002086655],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003458328,"about_ca_system_score_gemma":0.00004960163,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002039958,"about_ca_topic_score_gemma":0.00004765884,"domain_scores_codex":[0.9972431,0.0001618707,0.0006805692,0.0008522728,0.0005344602,0.0005277068],"domain_scores_gemma":[0.9977419,0.001272265,0.0002714506,0.0003773315,0.0001503441,0.0001866703],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00026209,0.0003457684,0.007137349,0.0002740822,0.000008293418,0.0002092655,0.004248062,0.00175813,0.7139446,0.0002707811,0.0004048045,0.2711368],"study_design_scores_gemma":[0.00618088,0.001619321,0.01998412,0.0007037834,0.0001418302,0.0005004792,0.008777999,0.9270419,0.03254773,0.0005945442,0.001599028,0.0003083933],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9194126,0.01517996,0.06296091,0.0005171982,0.00009401543,0.001597847,0.00004255599,0.0001324731,0.00006247772],"genre_scores_gemma":[0.9540529,0.002326213,0.04211689,0.0006453453,0.000113436,0.0004211418,0.0002158238,0.00005765161,0.00005062156],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9252837,"threshold_uncertainty_score":0.9998733,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01439513665593454,"score_gpt":0.2774020967611787,"score_spread":0.2630069601052441,"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."}}