{"id":"W1597169801","doi":"10.1007/978-3-642-15711-0_64","title":"Under-Determined Non-cartesian MR Reconstruction with Non-convex Sparsity Promoting Analysis Prior","year":2010,"lang":"en","type":"article","venue":"Lecture notes in computer science","topic":"Sparse and Compressive Sensing Techniques","field":"Engineering","cited_by":19,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Wavelet; Mathematics; Norm (philosophy); Regular polygon; Cartesian coordinate system; Convex optimization; Inverse problem; Image (mathematics); Iterative reconstruction; Algorithm; Metric (unit); Mathematical optimization; Applied mathematics; Computer science; Artificial intelligence; Mathematical analysis; Geometry","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":[0.0009614198,0.000939378,0.000956916,0.0004894746,0.0002361415,0.001372049,0.0006814211,0.0009658677,0.002279862],"category_scores_gemma":[0.005002567,0.0006448295,0.0005157784,0.0005548566,0.001118299,0.001640609,0.001897159,0.001679412,0.001265542],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002695023,"about_ca_system_score_gemma":0.000964708,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006651966,"about_ca_topic_score_gemma":0.0009477983,"domain_scores_codex":[0.9992794,0.0002488192,0.00003463806,0.0001439668,0.0002459874,0.00004714493],"domain_scores_gemma":[0.9984865,0.0006513846,0.000197581,0.0003371164,0.0002495339,0.00007788435],"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":[0.001203225,0.0001394527,0.002187218,0.0008996835,0.0001781901,0.000728854,0.0004942851,0.4432449,0.09228165,0.2244751,0.009972597,0.2241948],"study_design_scores_gemma":[0.00002825348,0.00007281766,0.0005695069,0.00003481912,0.0000375469,0.0005241212,0.00004972647,0.9396198,0.01447799,0.03957843,0.004969741,0.00003730768],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.005379258,0.0001007045,0.9924883,0.0001695741,0.00002062874,0.0000112323,0.00006881205,0.00009972462,0.001661792],"genre_scores_gemma":[0.2646331,0.0009970181,0.7250484,0.0002947577,0.0001732549,0.0001015378,0.0005843092,0.0002858123,0.007881802],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002279862,"threshold_uncertainty_score":0.007626832,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007623799627371663,"score_gpt":0.2173172203293896,"score_spread":0.2096934207020179,"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."}}