{"id":"W2768140591","doi":"10.1016/j.mri.2017.11.004","title":"Nonrigid motion compensation in compressed sensing reconstruction of cardiac cine MRI","year":2017,"lang":"en","type":"article","venue":"Magnetic Resonance Imaging","topic":"Advanced MRI Techniques and Applications","field":"Medicine","cited_by":25,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Saskatchewan; Saskatchewan Health Authority; University of Waterloo; Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Motion compensation; Smoothness; Computer vision; Artificial intelligence; Computer science; Imaging phantom; Iterative reconstruction; Compressed sensing; Data consistency; Real-time MRI; Minification; Motion estimation; Motion (physics); Reconstruction algorithm; Compensation (psychology); Algorithm; Mathematics; Magnetic resonance imaging; Nuclear medicine; Medicine; Radiology","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0005890738,0.0004897331,0.0002892163,0.0004055941,0.0002119581,0.0005898749,0.0004560406,0.0005624957,0.001658446],"category_scores_gemma":[0.003745314,0.0003121536,0.0002028842,0.0003979641,0.0003411479,0.0006320607,0.0004928462,0.0006662424,0.000449917],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001348384,"about_ca_system_score_gemma":0.0005589169,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001618172,"about_ca_topic_score_gemma":0.002907155,"domain_scores_codex":[0.9997233,0.00009182098,0.00001833808,0.00002880867,0.0001176951,0.00002003172],"domain_scores_gemma":[0.9993255,0.0003852728,0.00007449195,0.00008869639,0.0000925194,0.00003346663],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0005339872,0.0001628612,0.001574257,0.0005411119,0.00006181241,0.0003383549,0.000343031,0.1270195,0.2769576,0.01868552,0.003774683,0.5700073],"study_design_scores_gemma":[0.00001508117,0.0001265567,0.001339158,0.00003690452,0.00001755473,0.000596763,0.00004701512,0.9221932,0.06777356,0.003808397,0.004015893,0.00002997242],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03711653,0.0009556529,0.9597507,0.000303114,0.00007180238,0.00004464566,0.00009252462,0.0003552688,0.001309709],"genre_scores_gemma":[0.4093273,0.001531406,0.5833563,0.0002130502,0.0001296079,0.00008744295,0.0003882158,0.0002751687,0.004691507],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001658446,"threshold_uncertainty_score":0.00554806,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01528275821632108,"score_gpt":0.2962718847053468,"score_spread":0.2809891264890257,"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."}}