{"id":"W2092866498","doi":"10.1063/1.4875256","title":"Ultrasound guided fluorescence molecular tomography with improved quantification by an attenuation compensated born-normalization and <i>in vivo</i> preclinical study of cancer","year":2014,"lang":"en","type":"article","venue":"Review of Scientific Instruments","topic":"Optical Imaging and Spectroscopy Techniques","field":"Medicine","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal; Montreal Heart Institute","funders":"Canadian Institutes of Health Research","keywords":"Molecular imaging; Imaging phantom; Ultrasound; Attenuation; Biomedical engineering; Fluorescence-lifetime imaging microscopy; In vivo; Tomography; Preclinical imaging; Iterative reconstruction; Normalization (sociology); Materials science; Computer science; Fluorescence; Optics; Artificial intelligence; Medicine; Radiology; 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":[0.00138634,0.0007389199,0.0004397813,0.0006642441,0.0001375646,0.0005004468,0.0008435485,0.0007970971,0.0006245304],"category_scores_gemma":[0.001300612,0.0003659424,0.0004074131,0.0006636765,0.0005758755,0.0008622622,0.0004984245,0.0005585633,0.0002713838],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005838035,"about_ca_system_score_gemma":0.0004665825,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007311842,"about_ca_topic_score_gemma":0.0007260089,"domain_scores_codex":[0.9994942,0.000121112,0.00002548302,0.00009024674,0.0002373179,0.00003156321],"domain_scores_gemma":[0.9995473,0.0001110696,0.0001449547,0.00007463379,0.0001010963,0.00002094761],"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":[0.0001222969,0.00006866673,0.0005962101,0.0002036405,0.00002635335,0.0001410289,0.00007588214,0.009293173,0.9458342,0.004230992,0.0002973805,0.03911013],"study_design_scores_gemma":[0.00001115831,0.0001655435,0.001067819,0.00001713035,0.00003536189,0.0005979356,0.00001297784,0.07306606,0.9209026,0.0004913618,0.003590969,0.00004100964],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09035926,0.002087921,0.9044029,0.0002388781,0.00005131846,0.00009383659,0.0001112984,0.0007051086,0.001949591],"genre_scores_gemma":[0.2418694,0.002064565,0.7530199,0.0001032926,0.00003023881,0.0001656572,0.0001874376,0.0001486696,0.002410892],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00138634,"threshold_uncertainty_score":0.007331789,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0197384076087903,"score_gpt":0.3503531308694502,"score_spread":0.3306147232606599,"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."}}