{"id":"W2311555926","doi":"10.1117/12.2214894","title":"A novel structured dictionary for fast processing of 3D medical images, with application to computed tomography restoration and denoising","year":2016,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"University of British Columbia","keywords":"Computer science; Sparse approximation; K-SVD; Noise reduction; Neural coding; Artificial intelligence; Orthonormal basis; Pattern recognition (psychology); Associative array; Iterative reconstruction; Wavelet; Dictionary learning; Greedy algorithm; Signal processing; Noise (video); Compressed sensing; Image (mathematics); Algorithm; Digital signal processing","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":[0.0004554865,0.0004809985,0.0005648456,0.0007919948,0.0002670557,0.0005058332,0.0006686304,0.0008131593,0.001578993],"category_scores_gemma":[0.001687726,0.0003242009,0.0006771758,0.001014925,0.0004742753,0.0008269835,0.001165442,0.001020655,0.001133668],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002763017,"about_ca_system_score_gemma":0.0008456341,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001061911,"about_ca_topic_score_gemma":0.001847393,"domain_scores_codex":[0.9996473,0.00008092725,0.00002297275,0.00005667646,0.00016924,0.00002291363],"domain_scores_gemma":[0.9995282,0.0001322685,0.00005060021,0.00009643834,0.0001538694,0.0000386913],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002254011,0.000109819,0.0009508977,0.0004342791,0.00009846687,0.0002159779,0.0002019016,0.1705654,0.1436826,0.06222171,0.01340035,0.607893],"study_design_scores_gemma":[0.00003527138,0.0001298645,0.0003832242,0.0000320057,0.00001982857,0.0003743825,0.00002470743,0.9510213,0.01852415,0.01065418,0.01877362,0.00002747443],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001517948,0.00009841081,0.9977115,0.00007579208,0.00002624748,0.00001855658,0.00004596688,0.0001393961,0.000366182],"genre_scores_gemma":[0.02971212,0.0003916797,0.9678634,0.0001435122,0.00006221724,0.0001065491,0.0003486116,0.0001030361,0.001268904],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.001578993,"threshold_uncertainty_score":0.005282223,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00888067714386491,"score_gpt":0.2374607349174778,"score_spread":0.2285800577736128,"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."}}