{"id":"W2041751667","doi":"10.1016/j.mri.2011.02.024","title":"Deconvolution with simple extrapolation for improved cerebral blood flow measurement in dynamic susceptibility contrast magnetic resonance imaging during acute ischemic stroke","year":2011,"lang":"en","type":"article","venue":"Magnetic Resonance Imaging","topic":"Advanced MRI Techniques and Applications","field":"Medicine","cited_by":13,"is_retracted":false,"has_abstract":false,"ca_institutions":"Foothills Medical Centre; University of Calgary; Alberta Health Services","funders":"Canadian Institutes of Health Research","keywords":"Deconvolution; Cerebral blood flow; Extrapolation; Magnetic resonance imaging; Ischemia; Contrast (vision); Penumbra; Blood flow; White matter; Perfusion; Perfusion scanning; Stroke (engine); Nuclear medicine; Nuclear magnetic resonance; Medicine; Computer science; Mathematics; Artificial intelligence; Physics; Cardiology; Radiology; Algorithm; Statistics","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":[0.001969419,0.0006275623,0.0008003901,0.0005739839,0.0004140447,0.0006023681,0.0006933823,0.001016684,0.0007126059],"category_scores_gemma":[0.004529169,0.0006316825,0.0005205718,0.0004807695,0.0003682012,0.001066478,0.001068531,0.001231841,0.0003293768],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003165163,"about_ca_system_score_gemma":0.001186518,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001606305,"about_ca_topic_score_gemma":0.002418237,"domain_scores_codex":[0.9996806,0.0001570254,0.00002328879,0.00004452857,0.00006120233,0.00003333452],"domain_scores_gemma":[0.9994425,0.0003039214,0.00005379476,0.00006768139,0.00009778976,0.00003420988],"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":[0.004837899,0.0005310923,0.003403386,0.0009077122,0.0003195584,0.0004641321,0.000354199,0.05605928,0.5479769,0.00433633,0.001936091,0.3788733],"study_design_scores_gemma":[0.0001452547,0.0004711964,0.005053919,0.00007688952,0.0002611046,0.0009159354,0.0000406515,0.7550692,0.2320016,0.003682852,0.002150853,0.0001304891],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1507407,0.002476679,0.8444469,0.0002227685,0.0001147636,0.0001197461,0.00008156583,0.00106071,0.0007362442],"genre_scores_gemma":[0.4872369,0.001569619,0.5093569,0.0001704534,0.00009771805,0.0001779857,0.000157527,0.0002346713,0.0009982619],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001969419,"threshold_uncertainty_score":0.01041538,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01252210135723334,"score_gpt":0.2541429228337403,"score_spread":0.241620821476507,"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."}}