{"id":"W2013478821","doi":"10.1002/mrm.20006","title":"Removing the effect of SVD algorithmic artifacts present in quantitative MR perfusion studies","year":2004,"lang":"en","type":"article","venue":"Magnetic Resonance in Medicine","topic":"Advanced MRI Techniques and Applications","field":"Medicine","cited_by":94,"is_retracted":false,"has_abstract":true,"ca_institutions":"Foothills Medical Centre; University of Calgary","funders":"Canadian Institutes of Health Research; Alberta Heritage Foundation for Medical Research; University of Calgary; Natural Sciences and Engineering Research Council of Canada; Heart and Stroke Foundation of Canada","keywords":"Deconvolution; Singular value decomposition; Artifact (error); Cerebral blood flow; Algorithm; Computer science; Contrast (vision); Fourier transform; Perfusion; Nuclear magnetic resonance; Mathematics; Artificial intelligence; Physics; Medicine; Radiology; Mathematical analysis; Cardiology","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":[0.002001218,0.0007564101,0.0005902819,0.0005273693,0.0003204205,0.0009935877,0.0004669618,0.0007041711,0.001204215],"category_scores_gemma":[0.009138154,0.0002605123,0.0004141292,0.0005649374,0.0005688822,0.0005200256,0.0006506819,0.0005582737,0.0005617177],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002583961,"about_ca_system_score_gemma":0.0007370204,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001439637,"about_ca_topic_score_gemma":0.001417212,"domain_scores_codex":[0.9993581,0.0001929913,0.00006492052,0.00005735044,0.0002854809,0.00004122167],"domain_scores_gemma":[0.9970108,0.001912462,0.0001962748,0.0003676806,0.0004731683,0.00003960811],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0009897683,0.0001875395,0.003089963,0.0006639011,0.000144567,0.0007627745,0.0003027455,0.2827695,0.1776208,0.01091212,0.001213166,0.5213431],"study_design_scores_gemma":[0.0000698871,0.0007471386,0.002164021,0.00005770597,0.0000754084,0.001549449,0.00006579515,0.9049501,0.08048278,0.00555485,0.004237457,0.00004546171],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.06077323,0.000306668,0.9365727,0.00007524186,0.00006768433,0.00006753513,0.00003708901,0.0009280746,0.001171851],"genre_scores_gemma":[0.2816932,0.0003279956,0.7166629,0.00004194521,0.00002914496,0.00008007404,0.0001047167,0.0001857436,0.0008742412],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002001218,"threshold_uncertainty_score":0.01058352,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0338424578301789,"score_gpt":0.3825773126991359,"score_spread":0.348734854868957,"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."}}