{"id":"W4388907377","doi":"10.3390/electronics12234742","title":"Progressive Feature Reconstruction and Fusion to Accelerate MRI Imaging: Exploring Insights across Low, Mid, and High-Order Dimensions","year":2023,"lang":"en","type":"article","venue":"Electronics","topic":"Sparse and Compressive Sensing Techniques","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Science and Technology Infrastructure Program; Shanghai Institute of Technical Physics, Chinese Academy of Sciences; National Key Research and Development Program of China; National Natural Science Foundation of China","keywords":"Computer science; Artificial intelligence; Block (permutation group theory); Iterative reconstruction; Feature (linguistics); Compressed sensing; Pattern recognition (psychology); Fidelity; Image fusion; High fidelity; Reconstruction algorithm; Acceleration; Computer vision; Image (mathematics); Algorithm; Mathematics; Telecommunications; Physics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00005221929,0.0001816221,0.000159358,0.0001138837,0.0002165817,0.000107756,0.00006396484,0.00007238965,0.000002392904],"category_scores_gemma":[0.00001439352,0.0001746361,0.00001829614,0.000418648,0.00003457374,0.0002309949,0.0001112899,0.000269842,0.000007810282],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005231061,"about_ca_system_score_gemma":0.00001556946,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004420075,"about_ca_topic_score_gemma":0.0000239594,"domain_scores_codex":[0.9990897,0.0000171909,0.0001137591,0.0002725313,0.00009949063,0.000407392],"domain_scores_gemma":[0.9996032,0.00002713312,0.00002562549,0.0001824433,0.00007252343,0.00008914535],"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":[0.00005892634,0.00002638601,0.0006710287,0.00007814311,0.0001162507,0.0001815282,0.003366247,0.003955731,0.5932434,0.0006123181,0.01157609,0.386114],"study_design_scores_gemma":[0.0007825338,0.0001861163,0.004479914,0.000571295,0.0000454865,0.0002965021,0.0006134654,0.0508016,0.8938388,0.005277873,0.04215178,0.0009546216],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9914028,0.006020455,0.0005682504,0.0003565382,0.0003082804,0.0002300012,0.000003101774,0.001053266,0.00005727942],"genre_scores_gemma":[0.9931563,0.004137898,0.002380623,0.00005740061,0.0001035696,0.00005488825,0.000013958,0.00004996927,0.00004546156],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3851593,"threshold_uncertainty_score":0.7121453,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01516556223927148,"score_gpt":0.243748246814201,"score_spread":0.2285826845749295,"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."}}