{"id":"W2794161539","doi":"10.1117/1.jbo.23.1.016010","title":"Tissue perfusion rate estimation with compression-based photoacoustic-ultrasound imaging","year":2018,"lang":"en","type":"article","venue":"Journal of Biomedical Optics","topic":"Photoacoustic and Ultrasonic Imaging","field":"Engineering","cited_by":6,"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; Alberta Innovates; Canada Foundation for Innovation; Terry Fox Foundation; Alberta Innovates - Health Solutions; Ministry of Advanced Education, Government of Alberta; Cancer Research Institute","keywords":"Perfusion; Ultrasound; Biomedical engineering; Photoacoustic imaging in biomedicine; Perfusion scanning; Materials science; Medicine; Radiology; Optics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004588726,0.0001855381,0.0002733128,0.0002003896,0.0001157795,0.00007081719,0.0002067706,0.00007169408,0.0001771419],"category_scores_gemma":[0.000238029,0.000119973,0.00004881446,0.0002509353,0.000330949,0.0001805945,0.00001501265,0.0003567489,0.00001630537],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001035025,"about_ca_system_score_gemma":0.000139628,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003406241,"about_ca_topic_score_gemma":4.805323e-7,"domain_scores_codex":[0.9985691,0.00003262563,0.0004737122,0.0001120072,0.0005040247,0.0003084802],"domain_scores_gemma":[0.99878,0.00038397,0.0001702549,0.0001518551,0.0002459029,0.0002680221],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001776339,0.0002289,0.0002815867,0.0001969893,0.00008685515,0.0003475617,0.0004968701,0.04330421,0.8656535,0.00001439227,0.00940953,0.07980201],"study_design_scores_gemma":[0.001081244,0.0002583647,0.0003673116,0.000464256,0.0001074508,0.000452157,0.0001428199,0.9418476,0.05236386,0.0000692597,0.002650777,0.000194959],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1227617,0.0001274126,0.8753652,0.000230503,0.0008500342,0.00008711895,0.00001105224,0.00007538731,0.0004915999],"genre_scores_gemma":[0.924428,0.00003741979,0.07481505,0.0001791826,0.0004738482,0.000001292455,0.000008282886,0.00003507093,0.00002187411],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8985434,"threshold_uncertainty_score":0.4892358,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004575633488446268,"score_gpt":0.2280001589715195,"score_spread":0.2234245254830732,"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."}}