{"id":"W2039040939","doi":"10.1002/jmri.22111","title":"Phase and amplitude correction for multi‐echo water–fat separation with bipolar acquisitions","year":2010,"lang":"en","type":"article","venue":"Journal of Magnetic Resonance Imaging","topic":"Advanced MRI Techniques and Applications","field":"Medicine","cited_by":75,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"National Institute of Biomedical Imaging and Bioengineering; National Institute of Diabetes and Digestive and Kidney Diseases; Nanyang Technological University","keywords":"Amplitude; Phase (matter); Echo (communications protocol); Imaging phantom; Separation (statistics); Acoustics; Nuclear magnetic resonance; Computer science; Materials science; Physics; Optics","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":[0.0006348804,0.0008599869,0.00025225,0.0006883368,0.0003283604,0.0005061383,0.0005670905,0.0006708375,0.001554243],"category_scores_gemma":[0.003613983,0.0003227607,0.0001958354,0.0006653969,0.0002914851,0.0006603861,0.000449885,0.0005521157,0.0007508282],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001840037,"about_ca_system_score_gemma":0.0005934227,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005422049,"about_ca_topic_score_gemma":0.001414852,"domain_scores_codex":[0.9996037,0.00008023541,0.00002460305,0.00006606371,0.0002053954,0.0000200969],"domain_scores_gemma":[0.9990539,0.0002253473,0.0002015298,0.0001067418,0.0003655244,0.00004699497],"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":[0.0006106758,0.0001139191,0.003212832,0.0004827619,0.00006626039,0.0002304748,0.0001768958,0.005741292,0.5931942,0.003122161,0.002286634,0.3907618],"study_design_scores_gemma":[0.0002340063,0.0009328389,0.01470538,0.0001141654,0.000260116,0.002990132,0.0001160763,0.1966289,0.7537198,0.003881093,0.02628045,0.0001371246],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03283261,0.0005142901,0.964933,0.0001563289,0.0001603806,0.00007361783,0.00006802398,0.0005132342,0.0007485643],"genre_scores_gemma":[0.1481479,0.0006230837,0.8490155,0.0001243461,0.00008861597,0.00009718889,0.0002010887,0.0001851674,0.001517078],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.001554243,"threshold_uncertainty_score":0.005199492,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01535244542757266,"score_gpt":0.3552925821554252,"score_spread":0.3399401367278526,"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."}}