{"id":"W2077925928","doi":"10.1109/tmi.2008.2007825","title":"The Application of Compressed Sensing for Photo-Acoustic Tomography","year":2008,"lang":"en","type":"article","venue":"IEEE Transactions on Medical Imaging","topic":"Photoacoustic and Ultrasonic Imaging","field":"Engineering","cited_by":313,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Iterative reconstruction; Compressed sensing; Computer science; Tomography; Computer vision; Artificial intelligence; Image quality; Medical imaging; Image resolution; Reconstruction algorithm; Optics; Physics; Image (mathematics)","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002000839,0.0001381366,0.000168654,0.0000898887,0.0003536957,0.000013477,0.0001608017,0.00004679988,0.00001778877],"category_scores_gemma":[0.00002441808,0.0001149369,0.000124161,0.0002195558,0.0002636299,0.00006908849,6.730301e-7,0.0002706748,0.000003766385],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003427391,"about_ca_system_score_gemma":0.00004204275,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003220606,"about_ca_topic_score_gemma":0.000009582925,"domain_scores_codex":[0.9989182,0.00001861816,0.0002904925,0.0001532781,0.0003313882,0.0002880094],"domain_scores_gemma":[0.9989036,0.000661197,0.00003638487,0.000221098,0.00006512867,0.0001126143],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00009188503,0.0001723056,0.00005518173,0.0003109264,0.0002276506,0.00002393129,0.0008274717,0.2748166,0.250539,0.00003776003,0.002313881,0.4705834],"study_design_scores_gemma":[0.0004409115,0.000008217582,0.00003192857,0.00005658917,0.00004303529,0.00006965044,0.0001256237,0.9376876,0.05995369,0.00005363425,0.001408867,0.000120258],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007359352,0.0002005858,0.9908366,0.0001310615,0.0005723182,0.0002832733,0.00001886805,0.0002303283,0.0003676523],"genre_scores_gemma":[0.9970718,0.0001612465,0.002476521,0.0001306757,0.00005796101,0.00004723475,0.000002539871,0.00003331403,0.00001873084],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9897124,"threshold_uncertainty_score":0.4686989,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008043845530114153,"score_gpt":0.2291255629977165,"score_spread":0.2210817174676024,"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."}}