{"id":"W1897972544","doi":"10.1002/nbm.2958","title":"Rapid acquisition of multifrequency, multislice and multidirectional MR elastography data with a fractionally encoded gradient echo sequence","year":2013,"lang":"en","type":"article","venue":"NMR in Biomedicine","topic":"Ultrasound Imaging and Elastography","field":"Medicine","cited_by":92,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Pulse sequence; Spin echo; Sequence (biology); Multislice; Nuclear magnetic resonance; Voxel; Materials science; Sampling (signal processing); Phase (matter); Undersampling; Artifact (error); Physics; Optics; Computer science; Chemistry; Magnetic resonance imaging; Artificial intelligence; Medicine; Radiology","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":[0.000660903,0.0004927118,0.0003326078,0.0006756644,0.0001237362,0.0002598686,0.0002950235,0.0005377547,0.0009536653],"category_scores_gemma":[0.001125474,0.0002942039,0.0002029231,0.000286371,0.0001943869,0.0006038078,0.0004491882,0.0004921476,0.0004059316],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007267587,"about_ca_system_score_gemma":0.0002380775,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001529495,"about_ca_topic_score_gemma":0.0003354219,"domain_scores_codex":[0.9998465,0.00003094132,0.00001559627,0.00003965645,0.00005311399,0.00001413063],"domain_scores_gemma":[0.9996258,0.0001192743,0.00006802061,0.00007233467,0.00007929272,0.00003530653],"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":[0.0001570409,0.00003505034,0.0004025928,0.00008193542,0.00001601123,0.0001374654,0.0000375841,0.0004639786,0.9597474,0.0004886166,0.000229483,0.03820278],"study_design_scores_gemma":[0.0001061467,0.001336646,0.01771189,0.00004952872,0.00009605633,0.006833109,0.00006224058,0.0251548,0.9306461,0.001690882,0.01620758,0.0001049764],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.282422,0.00238663,0.7105308,0.0002640631,0.0001143939,0.0004688326,0.0005742247,0.001198155,0.002041021],"genre_scores_gemma":[0.2713025,0.001309094,0.7232799,0.0002741191,0.00008636511,0.0005576747,0.000917682,0.000192957,0.002079834],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0009536653,"threshold_uncertainty_score":0.003495216,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02254502875681177,"score_gpt":0.2786066475541519,"score_spread":0.2560616187973402,"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."}}