{"id":"W2012704047","doi":"10.1118/1.4800642","title":"MTF behavior of compressed sensing MR spectroscopic imaging","year":2013,"lang":"en","type":"article","venue":"Medical Physics","topic":"Advanced MRI Techniques and Applications","field":"Medicine","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Imaging phantom; Iterative reconstruction; Optical transfer function; Optics; Reconstruction algorithm; Nyquist frequency; Compressed sensing; Image resolution; Signal-to-noise ratio (imaging); Spatial frequency; Physics; Algorithm; Computer science; Artificial intelligence; Computer vision","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":[0.0009010659,0.0003627291,0.0001856721,0.000354591,0.0001615823,0.0002187635,0.000341739,0.0004981886,0.0005398305],"category_scores_gemma":[0.006971982,0.0001212429,0.0001394644,0.0002562531,0.0004674316,0.0005594904,0.0001928743,0.0002935169,0.0001152957],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004402692,"about_ca_system_score_gemma":0.0002481144,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00108939,"about_ca_topic_score_gemma":0.0005700733,"domain_scores_codex":[0.9997619,0.00005926353,0.0000117257,0.00003284813,0.0001143665,0.00001986301],"domain_scores_gemma":[0.9979596,0.00143362,0.0002010805,0.00009821746,0.0002742448,0.00003329865],"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":[0.001036478,0.0001687892,0.005196222,0.0004090118,0.00006406901,0.0006284252,0.0004111445,0.3372447,0.5611324,0.005714076,0.0005132658,0.08748138],"study_design_scores_gemma":[0.00001548382,0.0002214731,0.002737808,0.0000189472,0.00001192044,0.0002612571,0.00002116,0.8191358,0.176035,0.001059026,0.0004598663,0.0000223475],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7577322,0.0006040351,0.2380895,0.0003521517,0.00001905595,0.00006499435,0.0001324992,0.0005528921,0.002452652],"genre_scores_gemma":[0.9678894,0.0001205803,0.03141924,0.00004596265,0.000005239594,0.00003755461,0.00009590875,0.00004126145,0.0003448356],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00108939,"threshold_uncertainty_score":0.004765391,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01629326490716281,"score_gpt":0.3284051563510221,"score_spread":0.3121118914438593,"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."}}