{"id":"W4414830139","doi":"10.1109/tbme.2025.3617575","title":"A Tutorial on MRI Reconstruction: From Modern Methods to Clinical Implications","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Biomedical Engineering","topic":"Advanced MRI Techniques and Applications","field":"Medicine","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Leverage (statistics); Toolbox; Python (programming language); Iterative reconstruction; Image quality; Variety (cybernetics); Medical imaging","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":[0.0008960578,0.001940303,0.0008851498,0.001667807,0.0003116894,0.001809522,0.001336218,0.001853877,0.04288143],"category_scores_gemma":[0.003945304,0.0008524298,0.0009914015,0.001555271,0.0006612625,0.002394555,0.001143926,0.003319306,0.02613933],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004760982,"about_ca_system_score_gemma":0.0007720297,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007417748,"about_ca_topic_score_gemma":0.001007102,"domain_scores_codex":[0.9996313,0.00007506739,0.0000363203,0.00007873531,0.0001490981,0.00002947375],"domain_scores_gemma":[0.9989478,0.0006478331,0.00004847596,0.00006539404,0.0002114079,0.00007915548],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00008210833,0.00007397989,0.0002610963,0.002803288,0.00007880842,0.0003642138,0.0001352122,0.009469295,0.006627371,0.03912531,0.3253426,0.6156367],"study_design_scores_gemma":[0.00001650885,0.00009414594,0.0004638297,0.001071777,0.00003342011,0.002031032,0.00004535141,0.01744458,0.002608717,0.04707772,0.9290406,0.00007226905],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001385916,0.2060826,0.7367539,0.005149284,0.007271918,0.0001767146,0.001989311,0.007040661,0.03414975],"genre_scores_gemma":[0.01307756,0.3200322,0.577976,0.004898804,0.01417199,0.0004755486,0.005051658,0.004450701,0.05986543],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.04288143,"threshold_uncertainty_score":0.1434526,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02929031200175934,"score_gpt":0.4061259902421658,"score_spread":0.3768356782404064,"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."}}