{"id":"W2577660281","doi":"10.1109/mmsp.2016.7813392","title":"Robust MRI reconstruction via re-weighted total variation and non-local sparse regression","year":2016,"lang":"en","type":"article","venue":"","topic":"Sparse and Compressive Sensing Techniques","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Regularization (linguistics); Computer science; Artificial intelligence; Redundancy (engineering); Iterative reconstruction; Compressed sensing; Pattern recognition (psychology); Imaging phantom; Total variation denoising; Image (mathematics); Regression; Computer vision; Similarity (geometry); Algorithm; Mathematics; Statistics","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":[0.000764704,0.0007460933,0.000790201,0.0006465772,0.000204326,0.0005338208,0.0007995996,0.0008979086,0.0007881754],"category_scores_gemma":[0.002585718,0.0003758011,0.0007611688,0.0007193746,0.0006184251,0.0008603386,0.001002131,0.001060817,0.000298096],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002605138,"about_ca_system_score_gemma":0.0004383413,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001202326,"about_ca_topic_score_gemma":0.001115007,"domain_scores_codex":[0.9995393,0.000142187,0.00002014939,0.00008925526,0.0001833965,0.00002578833],"domain_scores_gemma":[0.999393,0.0003121455,0.0001129938,0.0000790992,0.00007513197,0.00002766739],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001665681,0.00008285994,0.0004846246,0.0002297418,0.000106067,0.0003745873,0.0001609036,0.6672471,0.08261929,0.03783939,0.002597299,0.2080915],"study_design_scores_gemma":[0.000006295786,0.00003185425,0.0000840963,0.000005053415,0.000006411626,0.0001256879,0.000006547244,0.9893133,0.006172075,0.003352459,0.0008851403,0.00001105191],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00497011,0.0001193006,0.9943944,0.00007562269,0.00001370083,0.000009892896,0.00001473256,0.0001004504,0.0003018516],"genre_scores_gemma":[0.2514589,0.0006819211,0.7435016,0.0001618449,0.0001140961,0.00009548751,0.0002777278,0.0001794208,0.003529093],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.001202326,"threshold_uncertainty_score":0.004044175,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01288545232292298,"score_gpt":0.1972530401715889,"score_spread":0.1843675878486659,"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."}}