{"id":"W1511871232","doi":"10.1002/mrm.24699","title":"‐corrected water–fat imaging using compressed sensing and parallel imaging","year":2013,"lang":"en","type":"article","venue":"Magnetic Resonance in Medicine","topic":"Advanced MRI Techniques and Applications","field":"Medicine","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"Compressed sensing; Acceleration; Image quality; Computer science; Iterative reconstruction; Image resolution; Computer vision; Artificial intelligence; Biomedical engineering; Image (mathematics); Physics; Medicine","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":[0.0007114809,0.0006939691,0.0002657761,0.0007625667,0.0002306494,0.0004821047,0.000577251,0.0005793903,0.001926832],"category_scores_gemma":[0.001845573,0.0002923239,0.000267726,0.0006383674,0.0005290161,0.0007702829,0.0006459111,0.0007853486,0.0003411692],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002553794,"about_ca_system_score_gemma":0.0006393511,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009997755,"about_ca_topic_score_gemma":0.001621189,"domain_scores_codex":[0.9997576,0.00004007591,0.00001261994,0.00003791171,0.0001344674,0.00001737899],"domain_scores_gemma":[0.9994736,0.0001616556,0.0001086753,0.00009061408,0.0001309655,0.00003446937],"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":[0.0006881467,0.0001591352,0.001842964,0.0003520859,0.00008944131,0.0005619228,0.0001547522,0.0111464,0.7520317,0.004314191,0.001342349,0.2273169],"study_design_scores_gemma":[0.000104391,0.0006534018,0.005457159,0.00007442349,0.0001034029,0.0046144,0.00006714254,0.2205523,0.7535741,0.003184063,0.01151794,0.00009736665],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1437976,0.001168098,0.8492275,0.0006320478,0.0001145576,0.0002229408,0.0001858412,0.001120352,0.003530843],"genre_scores_gemma":[0.2353417,0.0007295164,0.7611734,0.0001858767,0.00008955377,0.0001152826,0.0002784999,0.0001571139,0.001929131],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001926832,"threshold_uncertainty_score":0.006445944,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01759939014703425,"score_gpt":0.2982283370957558,"score_spread":0.2806289469487215,"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."}}