{"id":"W2599414549","doi":"10.1016/j.mri.2017.03.008","title":"Fast single image super-resolution using estimated low-frequency k-space data in MRI","year":2017,"lang":"en","type":"article","venue":"Magnetic Resonance Imaging","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":false,"ca_institutions":"Canadian Nautical Research Society","funders":"National Natural Science Foundation of China","keywords":"k-space; Mathematics; Artificial intelligence; Peak signal-to-noise ratio; Robustness (evolution); Image (mathematics); Image resolution; Imaging phantom; Computer vision; Interpolation (computer graphics); Algorithm; Image scaling; Computer science; Image processing; 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0006342042,0.0003534963,0.00033427,0.0002410103,0.000719088,0.001650212,0.005119644,0.00006605282,0.00001531164],"category_scores_gemma":[0.0008821582,0.0003881859,0.00004012013,0.0003977266,0.0005563878,0.006656351,0.002693711,0.0003623481,0.00002442797],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002353629,"about_ca_system_score_gemma":0.0001700109,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007242936,"about_ca_topic_score_gemma":0.00005960479,"domain_scores_codex":[0.996861,0.00009073913,0.0004845381,0.001255283,0.0004615897,0.0008468098],"domain_scores_gemma":[0.9949311,0.00006679209,0.0003157969,0.004376973,0.0001975274,0.0001118031],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001791179,0.0002426057,0.02975984,0.0001155272,0.000002239715,0.0006377471,0.0005208067,0.00009346442,0.3681235,0.001335272,0.0007503455,0.5984007],"study_design_scores_gemma":[0.0005217248,0.00003431429,0.02072864,0.0007380568,0.000006642972,0.0001079426,0.00002209707,0.9632405,0.005880866,0.007277106,0.000942525,0.0004995802],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00899102,0.009419584,0.9758527,0.002555182,0.0002610832,0.0003389542,0.0000175552,0.0006114417,0.001952462],"genre_scores_gemma":[0.1745804,0.00008181681,0.8249794,0.0001179544,0.00005868149,0.00001686722,0.000009204028,0.00004107018,0.0001146164],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.963147,"threshold_uncertainty_score":0.999857,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0414991350076261,"score_gpt":0.3246107882026478,"score_spread":0.2831116531950217,"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."}}