{"id":"W3208822704","doi":"10.1016/j.mri.2021.10.031","title":"Robust brain MR image compressive sensing via re-weighted total variation and sparse regression","year":2021,"lang":"en","type":"article","venue":"Magnetic Resonance Imaging","topic":"Sparse and Compressive Sensing Techniques","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University; Montreal Neurological Institute and Hospital","funders":"","keywords":"Compressed sensing; Computer science; Regularization (linguistics); Redundancy (engineering); Artificial intelligence; Imaging phantom; Similarity (geometry); Pattern recognition (psychology); Total variation denoising; Image (mathematics); Sparse approximation; Computer vision; Algorithm; Physics; Optics","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":[0.0009789394,0.000974533,0.0009853805,0.0005809242,0.0002275019,0.0007477147,0.000968272,0.001114221,0.001126879],"category_scores_gemma":[0.005539566,0.000501844,0.0006569226,0.0007935136,0.0009045601,0.001314213,0.001347067,0.001532571,0.0004273511],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002835363,"about_ca_system_score_gemma":0.0007309316,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002230587,"about_ca_topic_score_gemma":0.002832017,"domain_scores_codex":[0.9994093,0.0001973052,0.00003182578,0.0001165323,0.0002067499,0.00003838117],"domain_scores_gemma":[0.9985436,0.0008147686,0.0001727874,0.0001911654,0.0002305569,0.00004700341],"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":[0.0002476349,0.0001244646,0.0006296035,0.0003696486,0.0001594715,0.0001666629,0.0001665299,0.6001578,0.03775097,0.05336874,0.006587157,0.3002712],"study_design_scores_gemma":[0.000005917585,0.00002581111,0.0001211978,0.000008155115,0.000009683922,0.00004961623,0.000009290937,0.9885066,0.002571799,0.007748818,0.0009311654,0.00001198447],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00337048,0.000230747,0.9956482,0.0001520561,0.00002783806,0.00001285437,0.00004046013,0.00009819868,0.0004190787],"genre_scores_gemma":[0.2621806,0.001499578,0.7300277,0.0003112045,0.000276778,0.0001483344,0.0005748813,0.0001928174,0.004788174],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002230587,"threshold_uncertainty_score":0.0051772,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01096309721186785,"score_gpt":0.2142103860163035,"score_spread":0.2032472888044357,"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."}}