{"id":"W4385715822","doi":"10.1002/jmri.28944","title":"Synthesized <scp>7T MPRAGE</scp> From <scp>3T MPRAGE</scp> Using Generative Adversarial Network and Validation in Clinical Brain Imaging: A Feasibility Study","year":2023,"lang":"en","type":"article","venue":"Journal of Magnetic Resonance Imaging","topic":"Advanced MRI Techniques and Applications","field":"Medicine","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"National Natural Science Foundation of China","keywords":"Contrast (vision); Wilcoxon signed-rank test; Image quality; Intraclass correlation; Nuclear medicine; Medicine; Computer science; Artificial intelligence; Image (mathematics)","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":[0.002088031,0.0007496139,0.000415715,0.0004680837,0.0001743404,0.0005419328,0.000689536,0.0007461776,0.002215193],"category_scores_gemma":[0.004925651,0.0002986339,0.0006869098,0.0002656182,0.000598822,0.0002919295,0.0007351271,0.00054506,0.0006256794],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003611438,"about_ca_system_score_gemma":0.0004363905,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001735286,"about_ca_topic_score_gemma":0.001596356,"domain_scores_codex":[0.9995988,0.000177589,0.0000206404,0.0001015066,0.00007349675,0.00002798442],"domain_scores_gemma":[0.998351,0.0009307308,0.000161455,0.0002563061,0.0002377027,0.00006280807],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00229573,0.0006165601,0.01879997,0.0005552739,0.0006233677,0.001760831,0.0003454477,0.7181994,0.06895401,0.001708413,0.004777004,0.181364],"study_design_scores_gemma":[0.00008870272,0.0005805304,0.009334392,0.00004816736,0.00008752412,0.001244565,0.0000504897,0.9600314,0.02583289,0.0013331,0.001317115,0.00005106912],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.718149,0.0005695639,0.275067,0.0003579844,0.0001045263,0.0004680267,0.001288947,0.001473733,0.002521173],"genre_scores_gemma":[0.9295963,0.0002158126,0.0664766,0.0001643658,0.00003097902,0.0001912212,0.002036734,0.0002090514,0.001078934],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002215193,"threshold_uncertainty_score":0.01104265,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04967064395036864,"score_gpt":0.3792436509755568,"score_spread":0.3295730070251882,"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."}}