{"id":"W2263590702","doi":"10.1002/mrm.26125","title":"Accelerated 3D echo‐planar imaging with compressed sensing for time‐resolved hyperpolarized <sup>13</sup>C studies","year":2016,"lang":"en","type":"article","venue":"Magnetic Resonance in Medicine","topic":"Advanced NMR Techniques and Applications","field":"Chemistry","cited_by":28,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; Sunnybrook Health Science Centre","funders":"Canadian Cancer Society","keywords":"Undersampling; Compressed sensing; Imaging phantom; Nuclear magnetic resonance; Magnetic resonance imaging; Pulse sequence; Iterative reconstruction; Image resolution; Planar; Temporal resolution; Magnetic resonance spectroscopic imaging; Physics; Nuclear medicine; Computer science; Materials science; Optics; Artificial intelligence; Medicine; Radiology","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0008204401,0.0006794345,0.0002842032,0.0005778794,0.0001187834,0.0005082551,0.0004799037,0.0006033464,0.001123009],"category_scores_gemma":[0.002100107,0.0003281482,0.000223482,0.0005161008,0.0005320082,0.0006572805,0.000504131,0.000731647,0.0003215632],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002349888,"about_ca_system_score_gemma":0.0004574613,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005733545,"about_ca_topic_score_gemma":0.0008306931,"domain_scores_codex":[0.9997131,0.00008267118,0.0000168073,0.00003007987,0.0001400564,0.00001724395],"domain_scores_gemma":[0.9990553,0.0003981066,0.0002062315,0.00008460761,0.0002001353,0.00005559094],"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":[0.0004302494,0.00005277678,0.001087396,0.0004210026,0.00005098353,0.000398822,0.0001262273,0.01458721,0.8712034,0.002609322,0.0009337077,0.108099],"study_design_scores_gemma":[0.00006936902,0.0004404402,0.003218765,0.00006569099,0.00006417709,0.002660923,0.00005865545,0.2700331,0.7129073,0.001902566,0.008475509,0.0001035698],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1067682,0.001579645,0.8885663,0.0004565544,0.00005177274,0.0001461378,0.000220923,0.0008205524,0.00138986],"genre_scores_gemma":[0.1998089,0.001185841,0.7975185,0.0001987807,0.0000595631,0.0001573128,0.0003006939,0.0001230276,0.000647255],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.001123009,"threshold_uncertainty_score":0.00433898,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02713455001930029,"score_gpt":0.3024981556201439,"score_spread":0.2753636056008436,"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."}}