{"id":"W1994575631","doi":"10.1364/biomed.2012.bsu3a.46","title":"Iterative Algorithm for Multiple Illumination Photoacoustic Tomography using Transducer Channel Data","year":2012,"lang":"en","type":"article","venue":"","topic":"Photoacoustic and Ultrasonic Imaging","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Photoacoustic tomography; Hessian matrix; Photoacoustic imaging in biomedicine; Photon diffusion; Channel (broadcasting); Diffuse optical imaging; Transducer; Computer science; Algorithm; Tomography; Photoacoustic spectroscopy; Optics; Photoacoustic effect; Iterative reconstruction; Acoustics; Artificial intelligence; Physics; Mathematics; Telecommunications","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":[0.001072929,0.000762259,0.0006756689,0.0005258113,0.0004756523,0.000955793,0.001596652,0.001235391,0.003770006],"category_scores_gemma":[0.003438762,0.0006395293,0.0005500934,0.0008199156,0.0006867017,0.001194933,0.001559634,0.001668198,0.001497195],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009371932,"about_ca_system_score_gemma":0.002132259,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003691902,"about_ca_topic_score_gemma":0.005188128,"domain_scores_codex":[0.9995647,0.000102534,0.00002349918,0.00007271341,0.0001997796,0.00003673102],"domain_scores_gemma":[0.9990399,0.0005023236,0.00009002045,0.00009952473,0.0002364484,0.00003171889],"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":[0.0001829128,0.00007449133,0.0006652771,0.0002166339,0.00006995972,0.000123275,0.0002475748,0.6515245,0.02531908,0.04753089,0.003094421,0.2709509],"study_design_scores_gemma":[0.00001450431,0.00002157536,0.00007051569,0.000006736458,0.000004789605,0.00003658505,0.00001165012,0.9881375,0.003234138,0.00676715,0.001681833,0.00001290554],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0008327786,0.00001710835,0.9987285,0.00003080079,0.000004564344,0.00001289395,0.00001488451,0.0001373957,0.0002211273],"genre_scores_gemma":[0.02724444,0.00005080249,0.9711379,0.0000317129,0.000009086053,0.0002207928,0.0001103958,0.0001040376,0.00109066],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003770006,"threshold_uncertainty_score":0.01261187,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03990075308423241,"score_gpt":0.2658269968460792,"score_spread":0.2259262437618468,"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."}}