{"id":"W4402626944","doi":"10.1109/jbhi.2024.3446839","title":"DPFNet: Fast Reconstruction of Multi-Coil MRI Based on Dual Domain Parallel Fusion Network","year":2024,"lang":"en","type":"article","venue":"IEEE Journal of Biomedical and Health Informatics","topic":"Advanced MRI Techniques and Applications","field":"Medicine","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Natural Science Foundation of China","keywords":"Computer science; Dual (grammatical number); Electromagnetic coil; Fusion; Domain (mathematical analysis); Iterative reconstruction; Artificial intelligence; Physics; Mathematics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009383336,0.0008682329,0.0007293897,0.0006730038,0.0003162593,0.0004903137,0.001098342,0.0009426124,0.001765832],"category_scores_gemma":[0.001886081,0.0003867099,0.0005909461,0.0005665489,0.0003710482,0.001183815,0.0008589854,0.0009151877,0.0004623461],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005198556,"about_ca_system_score_gemma":0.0007155904,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006109213,"about_ca_topic_score_gemma":0.005666745,"domain_scores_codex":[0.9997436,0.00004420388,0.00001258862,0.00006808866,0.00008921674,0.00004215241],"domain_scores_gemma":[0.9996277,0.0001471125,0.00003597578,0.00004474108,0.0001249111,0.00001949345],"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":[0.0003476995,0.0001058704,0.001850426,0.0001068048,0.0001006851,0.0002226394,0.00007205157,0.444299,0.01060415,0.003653431,0.004914939,0.5337223],"study_design_scores_gemma":[0.000006417851,0.0000312851,0.0001518561,0.000005217775,0.000008559547,0.00007503003,0.000006325973,0.9955719,0.00250873,0.001006438,0.000622738,0.00000546679],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01651461,0.0003503605,0.9805655,0.0001276357,0.00005203345,0.00003907631,0.00008558713,0.0008986597,0.001366575],"genre_scores_gemma":[0.477408,0.0005744509,0.5145406,0.0003108419,0.00006875018,0.0001450608,0.0008756114,0.0001643945,0.005912261],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.006109213,"threshold_uncertainty_score":0.01214731,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03359394148183018,"score_gpt":0.3517292430292638,"score_spread":0.3181353015474336,"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."}}