{"id":"W4315621534","doi":"10.1002/nbm.4899","title":"Phase‐regularized and displacement‐regularized compressed sensing for fast magnetic resonance elastography","year":2023,"lang":"en","type":"article","venue":"NMR in Biomedicine","topic":"Ultrasound Imaging and Elastography","field":"Medicine","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Magnetic resonance elastography; Compressed sensing; Undersampling; Wavelet; Imaging phantom; Computer science; Regularization (linguistics); Artificial intelligence; Pattern recognition (psychology); Algorithm; Mathematics; Physics; Elastography; Acoustics; 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":[0.001390467,0.0006351056,0.0003622494,0.0007197651,0.000172576,0.0004919753,0.000581244,0.0007242965,0.001028541],"category_scores_gemma":[0.004602633,0.0002107727,0.0004296811,0.0005723133,0.0004845841,0.0006347095,0.0005134778,0.0007077836,0.000233362],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003565373,"about_ca_system_score_gemma":0.00070313,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002300453,"about_ca_topic_score_gemma":0.00167958,"domain_scores_codex":[0.9995266,0.0001447547,0.00002785056,0.00007637267,0.0001989709,0.00002534695],"domain_scores_gemma":[0.9981743,0.001062582,0.0001964694,0.0001496777,0.0003515986,0.00006536903],"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":[0.0009898847,0.0002732897,0.001762394,0.0005494914,0.0001235406,0.0003332556,0.0001623313,0.4920326,0.1542388,0.009659257,0.003266967,0.3366082],"study_design_scores_gemma":[0.00002012739,0.00008918253,0.0004251605,0.00001158507,0.00001008158,0.00007019009,0.00001140184,0.9827639,0.01453428,0.001183881,0.0008615525,0.00001864121],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08080451,0.001255363,0.915243,0.0005099003,0.00008079161,0.0001201589,0.0002714652,0.0007172592,0.0009976038],"genre_scores_gemma":[0.4176365,0.0008348891,0.578779,0.0001882926,0.00008036783,0.0001832132,0.0008761332,0.0001538371,0.001267686],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002300453,"threshold_uncertainty_score":0.007353604,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01374026860541556,"score_gpt":0.2953889634912161,"score_spread":0.2816486948858005,"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."}}