{"id":"W2581160999","doi":"10.1109/tuffc.2017.2661238","title":"Photoacoustic–Ultrasound Tomography With S-Sequence Aperture Encoding","year":2017,"lang":"en","type":"article","venue":"IEEE Transactions on Ultrasonics Ferroelectrics and Frequency Control","topic":"Photoacoustic and Ultrasonic Imaging","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Cancer Society Research Institute; Canadian Institutes of Health Research","keywords":"Imaging phantom; Coded aperture; Tomography; Optics; Aperture (computer memory); Encoding (memory); Ultrasound; Photoacoustic imaging in biomedicine; Image resolution; Diffraction; Physics; Materials science; Acoustics; Computer science; Artificial intelligence; Detector","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":[0.0005508639,0.0003974502,0.0002981636,0.0003647399,0.000149119,0.0004867847,0.0005844098,0.0005938847,0.001234046],"category_scores_gemma":[0.0007958206,0.000369953,0.0002332655,0.0004809498,0.0005271424,0.0007628302,0.0006855237,0.000431519,0.000375129],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003869859,"about_ca_system_score_gemma":0.0004339719,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005229573,"about_ca_topic_score_gemma":0.0005869545,"domain_scores_codex":[0.9996945,0.00006913752,0.00002109891,0.00006545896,0.0001224546,0.00002739708],"domain_scores_gemma":[0.9993371,0.0001827348,0.000220349,0.00008980272,0.0001020728,0.00006778612],"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":[0.0001372068,0.00002711261,0.0003551903,0.00005937467,0.000006263048,0.000111976,0.00003267104,0.0007205343,0.9857394,0.0008594229,0.0001791294,0.01177175],"study_design_scores_gemma":[0.00002752453,0.0003937331,0.001060256,0.00001055364,0.00001761286,0.001153855,0.00001373363,0.02715633,0.9662561,0.0001810922,0.003697583,0.00003145163],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5208635,0.002173741,0.4682792,0.0005994923,0.0001293257,0.0002451164,0.0001958565,0.001789645,0.005724123],"genre_scores_gemma":[0.5381159,0.0004936886,0.4585138,0.0001510783,0.00005937671,0.0001208279,0.0000981751,0.00007126184,0.002375819],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001234046,"threshold_uncertainty_score":0.004128337,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01010012591753773,"score_gpt":0.2108328524376721,"score_spread":0.2007327265201344,"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."}}