{"id":"W2949076424","doi":"10.1007/s00330-019-06229-1","title":"Direct attenuation correction of brain PET images using only emission data via a deep convolutional encoder-decoder (Deep-DAC)","year":2019,"lang":"en","type":"article","venue":"European Radiology","topic":"Medical Imaging Techniques and Applications","field":"Medicine","cited_by":108,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"Tehran University of Medical Sciences and Health Services","keywords":"Artificial intelligence; Attenuation; Image quality; Computer science; Convolutional neural network; Encoder; Pattern recognition (psychology); Correction for attenuation; Nuclear medicine; Pixel; Computer vision; Positron emission tomography; Medicine; Physics; Image (mathematics); Optics","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":[0.0004979311,0.0008628984,0.0004240382,0.0004178064,0.000260446,0.0007683017,0.0006953103,0.0007410121,0.002974798],"category_scores_gemma":[0.0015609,0.0004226445,0.0003685914,0.0003731656,0.00025283,0.0005675758,0.0007816953,0.001222949,0.001214296],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005302987,"about_ca_system_score_gemma":0.00189139,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005872032,"about_ca_topic_score_gemma":0.0137269,"domain_scores_codex":[0.9998637,0.00001468442,0.000008489369,0.0000335513,0.00005596315,0.00002352568],"domain_scores_gemma":[0.9996752,0.00009634615,0.00003217598,0.00005526713,0.0001196815,0.00002139314],"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":[0.0007209932,0.0001250299,0.002858418,0.0004531463,0.0001949694,0.0003940738,0.0001492639,0.08802965,0.1875172,0.007783286,0.008285675,0.7034883],"study_design_scores_gemma":[0.00004257721,0.0001401935,0.001813953,0.00006552092,0.000113778,0.0009208662,0.00003574129,0.7684473,0.2123692,0.0041232,0.01188365,0.00004406555],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0239077,0.000691083,0.9690851,0.0002876813,0.0001389706,0.00005285229,0.0002648796,0.002956344,0.002615358],"genre_scores_gemma":[0.4167399,0.000839141,0.5702308,0.0003426591,0.00006192889,0.00008629437,0.0008401936,0.000474572,0.01038449],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005872032,"threshold_uncertainty_score":0.01167572,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03679750417127457,"score_gpt":0.3243212062934852,"score_spread":0.2875237021222106,"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."}}