{"id":"W2142898611","doi":"10.3390/s131216714","title":"Calibrationless Parallel Magnetic Resonance Imaging: A Joint Sparsity Model","year":2013,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced MRI Techniques and Applications","field":"Medicine","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; Qatar National Research Fund; Fonds National de la Recherche Luxembourg","keywords":"Imaging phantom; Interpolation (computer graphics); Calibration; Sampling (signal processing); Computer science; Acceleration; Algorithm; Iterative reconstruction; Joint (building); Cartesian coordinate system; Artificial intelligence; Mathematics; Computer vision; Image (mathematics); Statistics; Nuclear medicine","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":[0.001444044,0.0004966853,0.0005129231,0.0003894393,0.0001754639,0.0006487222,0.001159053,0.0008434979,0.001316039],"category_scores_gemma":[0.003102403,0.0004521212,0.0005268395,0.0006313508,0.0009114497,0.001552575,0.001066191,0.001242471,0.0005581726],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003494697,"about_ca_system_score_gemma":0.0008087403,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001120564,"about_ca_topic_score_gemma":0.001493963,"domain_scores_codex":[0.999373,0.0002431766,0.00002461629,0.0001120764,0.0002118269,0.00003530441],"domain_scores_gemma":[0.9990163,0.0004171121,0.0001286349,0.0002265636,0.0001706845,0.00004069406],"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":[0.0003043768,0.0001453101,0.001341375,0.0002392366,0.00009696298,0.0002743604,0.000209149,0.7500055,0.04538444,0.04276382,0.002630601,0.1566048],"study_design_scores_gemma":[0.00001017112,0.00004448908,0.000210441,0.000005996456,0.00001228755,0.0001398498,0.00001026617,0.9869855,0.004732808,0.006565263,0.001270873,0.00001209745],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.006360105,0.00007558771,0.9925486,0.0001450596,0.000009219839,0.00001824698,0.00002801163,0.0001470411,0.0006681122],"genre_scores_gemma":[0.3930155,0.0009154317,0.5991398,0.0003377464,0.000100076,0.0001917146,0.0003610756,0.0002618974,0.005676824],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.001444044,"threshold_uncertainty_score":0.007636905,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02669055274408243,"score_gpt":0.2724941272813748,"score_spread":0.2458035745372923,"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."}}