{"id":"W4387025298","doi":"10.32920/24192345","title":"3D Opto-Acoustic Image Reconstruction and Motion Tracking Using Convex Optimization Algorithms","year":2023,"lang":"en","type":"preprint","venue":"","topic":"Photoacoustic and Ultrasonic Imaging","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Computer vision; Visualization; Iterative reconstruction; Image quality; Artificial intelligence; Imaging phantom; Transducer; SIGNAL (programming language); Biomedical engineering; Algorithm; Acoustics; 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":[0.001094529,0.001349546,0.001054986,0.0008074198,0.0003948002,0.001418863,0.001287251,0.001676819,0.002685467],"category_scores_gemma":[0.002850354,0.0009445857,0.001196487,0.0008501537,0.0009725546,0.0009568389,0.001497097,0.001634645,0.001144502],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001225833,"about_ca_system_score_gemma":0.001677444,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008137731,"about_ca_topic_score_gemma":0.00605625,"domain_scores_codex":[0.9994794,0.0001633814,0.00003087953,0.0001086496,0.0001731634,0.00004471149],"domain_scores_gemma":[0.999089,0.0005256488,0.0001161124,0.00007059821,0.0001595676,0.00003905748],"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":[0.00006748622,0.00003888635,0.000272285,0.00007425233,0.00003805321,0.00006346076,0.00005831446,0.9325337,0.003535217,0.007987371,0.001689163,0.05364182],"study_design_scores_gemma":[0.00000272831,0.000004813414,0.00001976587,0.000002903058,0.000001185659,0.000008609273,0.000002726234,0.9982873,0.0004216778,0.0008609298,0.0003841071,0.000003312552],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001817257,0.00008514606,0.9969291,0.0001229147,0.00001392173,0.00002176574,0.00002893701,0.0002383684,0.0007426476],"genre_scores_gemma":[0.1004337,0.0004749847,0.8912983,0.0001643929,0.00005725181,0.0003158057,0.0004429259,0.0003479557,0.006464648],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.008137731,"threshold_uncertainty_score":0.01618075,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02733196585104242,"score_gpt":0.2493286653922565,"score_spread":0.2219966995412141,"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."}}