{"id":"W2617215951","doi":"10.1007/s10334-017-0628-x","title":"Motion-compensated data decomposition algorithm to accelerate dynamic cardiac MRI","year":2017,"lang":"en","type":"article","venue":"Magnetic Resonance Materials in Physics Biology and Medicine","topic":"Advanced MRI Techniques and Applications","field":"Medicine","cited_by":7,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Saskatchewan; Saskatchewan Health Authority; Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada; University of Southern California","keywords":"Computer science; Motion estimation; Motion compensation; Algorithm; Artificial intelligence; Computer vision; Compressed sensing; Imaging phantom; Dynamic contrast-enhanced MRI; Magnetic resonance imaging; Iterative reconstruction; Acceleration; Physics; Optics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003384995,0.0001675337,0.0004860983,0.00004863647,0.0001945124,0.00002340289,0.0003365616,0.0001040332,0.00006581407],"category_scores_gemma":[0.00005802709,0.0001344384,0.00001365457,0.00007506707,0.0003216486,0.00008436007,0.0002941129,0.0001087337,0.00001201667],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002574348,"about_ca_system_score_gemma":0.00001949999,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001756711,"about_ca_topic_score_gemma":0.000008960142,"domain_scores_codex":[0.9988572,0.00004534821,0.0002996195,0.0004777508,0.00008273332,0.0002373544],"domain_scores_gemma":[0.9986341,0.00003792068,0.0001168556,0.001057375,0.00006126663,0.0000924794],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0001089599,0.0000721771,0.001392911,0.00003232585,0.000005487134,0.00001108083,0.00006406738,0.000001379558,0.3588335,0.002443979,0.000635882,0.6363983],"study_design_scores_gemma":[0.006580393,0.00266839,0.6638573,0.001618021,0.0002821278,0.00008633226,0.0001274437,0.007114572,0.117542,0.0670561,0.1320913,0.0009758744],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7826468,0.007253715,0.169092,0.03074984,0.001619333,0.004246866,0.001329828,0.0002745953,0.002787076],"genre_scores_gemma":[0.9299451,0.005611187,0.06047762,0.001153627,0.0007856507,0.0002342763,0.001446634,0.00003402251,0.0003119626],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6624644,"threshold_uncertainty_score":0.548224,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03222943821322816,"score_gpt":0.3929414152903793,"score_spread":0.3607119770771511,"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."}}