{"id":"W2010181911","doi":"10.1364/oe.18.021003","title":"Rapid Volumetric OCT Image Acquisition Using Compressive Sampling","year":2010,"lang":"en","type":"article","venue":"Optics Express","topic":"Sparse and Compressive Sensing Techniques","field":"Engineering","cited_by":52,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia; Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; Michael Smith Health Research BC","keywords":"Optical coherence tomography; Image quality; Compressed sensing; Computer vision; Computer science; Artificial intelligence; Iterative reconstruction; Sampling (signal processing); Data acquisition; Image processing; Optics; Image (mathematics); Physics","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":[0.0004065731,0.0003979317,0.0002425219,0.0006868613,0.0001810305,0.0005535036,0.0003437824,0.0003818148,0.001762035],"category_scores_gemma":[0.001456528,0.0002598411,0.0001961295,0.0005429602,0.0002798476,0.0006385819,0.0007432827,0.0005283217,0.000444309],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002665213,"about_ca_system_score_gemma":0.0005052394,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00115833,"about_ca_topic_score_gemma":0.001766324,"domain_scores_codex":[0.9997677,0.00005463748,0.00001336808,0.00002614973,0.0001183238,0.00001981694],"domain_scores_gemma":[0.9993844,0.0002266253,0.0000758214,0.0001348152,0.0001461599,0.00003216594],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00042451,0.0001038655,0.00117693,0.0001705116,0.00003431702,0.0002724419,0.0001641565,0.05216402,0.5884008,0.006976002,0.003414544,0.346698],"study_design_scores_gemma":[0.0000628504,0.0002968504,0.002441443,0.00004200717,0.00002335076,0.001120862,0.00008883086,0.7354961,0.2458595,0.006245351,0.008243888,0.00007886056],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.07212375,0.0002928569,0.9225548,0.0003185201,0.00004186979,0.0001606604,0.0003031264,0.001083668,0.003120791],"genre_scores_gemma":[0.3003952,0.0004675187,0.6970398,0.0001266852,0.00004850369,0.0002023784,0.0004100034,0.00009984718,0.001210166],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.001762035,"threshold_uncertainty_score":0.005894601,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02773417782651211,"score_gpt":0.263534208529478,"score_spread":0.2358000307029659,"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."}}