{"id":"W2808442653","doi":"","title":"Deep CEST MRI: 9.4T spectral super-resolution from 3T CEST MRI data","year":2018,"lang":"de","type":"article","venue":"Max Planck Digital Library","topic":"Advanced MRI Techniques and Applications","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Tongji Medical College, Huazhong University of Science and Technology; School of Medicine, Stanford University; Centre Hospitalier Universitaire de Rennes; Feinberg School of Medicine; Southern Medical University; Max-Planck-Institut für Kognitions- und Neurowissenschaften; Huazhong University of Science and Technology; Universität Zürich; Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital; Université de Montréal; Centre National de la Recherche Scientifique; Vanderbilt University; Università degli Studi di Pavia; Tongji University; Institut National de la Santé et de la Recherche Médicale; University of Toronto; Eidgenössische Technische Hochschule Zürich; Wellcome Trust; Polytechnique Montréal; University College London; King's College London; McGill University; Institut national de recherche en informatique et en automatique (INRIA); Aix-Marseille Université; Vanderbilt University Medical Center; Johns Hopkins University; Northwestern University","keywords":"Nuclear magnetic resonance; Magnetic resonance imaging; Physics; Radiology; Medicine","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006714564,0.0005439164,0.0003714582,0.0002792288,0.0001912861,0.0005056358,0.0006583573,0.0007232481,0.00311202],"category_scores_gemma":[0.001156817,0.000433302,0.0003617709,0.000328988,0.0003219444,0.0006813782,0.0005885737,0.0009685553,0.0007623393],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003750781,"about_ca_system_score_gemma":0.0004815141,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001903459,"about_ca_topic_score_gemma":0.002785186,"domain_scores_codex":[0.9999179,0.00001875485,0.000003106548,0.00001688035,0.0000313464,0.0000120061],"domain_scores_gemma":[0.9998596,0.00005176242,0.00001710388,0.00002251891,0.00003421365,0.00001491673],"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":[0.001695864,0.0001904562,0.004172964,0.0007613627,0.000207894,0.001728349,0.0004051932,0.3513654,0.4796421,0.0176323,0.01114726,0.1310509],"study_design_scores_gemma":[0.00005903316,0.000164459,0.002480254,0.00003347039,0.00005234549,0.0006114825,0.00004156537,0.8557535,0.1206499,0.01262216,0.007472375,0.00005935522],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2160816,0.000698465,0.7686828,0.001139156,0.00009307396,0.0001464009,0.001814268,0.004433228,0.006911003],"genre_scores_gemma":[0.7177725,0.0009780197,0.2723795,0.000305951,0.00003494839,0.0001760582,0.002088068,0.0005236979,0.005741191],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00311202,"threshold_uncertainty_score":0.01041073,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02665544646872245,"score_gpt":0.2690289833811848,"score_spread":0.2423735369124624,"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."}}