INTRODUCTION: A new deconvolution algorithm, the Bayesian estimation algorithm, was reported to improve the precision of parametric maps created using perfusion computed tomography. However, it remains unclear whether quantitative values generated by this method are more accurate than those generated using optimized deconvolution algorithms of other software packages. Hence, we compared the accuracy of the Bayesian and deconvolution algorithms by using a digital phantom. METHODS: The digital phantom data, in which concentration-time curves reflecting various known values for cerebral blood flow (CBF), cerebral blood volume (CBV), mean transit time (MTT), and tracer delays were embedded, were analyzed using the Bayesian estimation algorithm as well as delay-insensitive singular value decomposition (SVD) algorithms of two software packages that were the best benchmarks in a previous cross-validation study. Correlation and agreement of quantitative values of these algorithms with true values were examined. RESULTS: CBF, CBV, and MTT values estimated by all the algorithms showed strong correlations with the true values (r = 0.91-0.92, 0.97-0.99, and 0.91-0.96, respectively). In addition, the values generated by the Bayesian estimation algorithm for all of these parameters showed good agreement with the true values [intraclass correlation coefficient (ICC) = 0.90, 0.99, and 0.96, respectively], while MTT values from the SVD algorithms were suboptimal (ICC = 0.81-0.82). CONCLUSIONS: Quantitative analysis using a digital phantom revealed that the Bayesian estimation algorithm yielded CBF, CBV, and MTT maps strongly correlated with the true values and MTT maps with better agreement than those produced by delay-insensitive SVD algorithms.
INTRODUCTION: A new deconvolution algorithm, the Bayesian estimation algorithm, was reported to improve the precision of parametric maps created using perfusion computed tomography. However, it remains unclear whether quantitative values generated by this method are more accurate than those generated using optimized deconvolution algorithms of other software packages. Hence, we compared the accuracy of the Bayesian and deconvolution algorithms by using a digital phantom. METHODS: The digital phantom data, in which concentration-time curves reflecting various known values for cerebral blood flow (CBF), cerebral blood volume (CBV), mean transit time (MTT), and tracer delays were embedded, were analyzed using the Bayesian estimation algorithm as well as delay-insensitive singular value decomposition (SVD) algorithms of two software packages that were the best benchmarks in a previous cross-validation study. Correlation and agreement of quantitative values of these algorithms with true values were examined. RESULTS: CBF, CBV, and MTT values estimated by all the algorithms showed strong correlations with the true values (r = 0.91-0.92, 0.97-0.99, and 0.91-0.96, respectively). In addition, the values generated by the Bayesian estimation algorithm for all of these parameters showed good agreement with the true values [intraclass correlation coefficient (ICC) = 0.90, 0.99, and 0.96, respectively], while MTT values from the SVD algorithms were suboptimal (ICC = 0.81-0.82). CONCLUSIONS: Quantitative analysis using a digital phantom revealed that the Bayesian estimation algorithm yielded CBF, CBV, and MTT maps strongly correlated with the true values and MTT maps with better agreement than those produced by delay-insensitive SVD algorithms.
Authors: Kim Mouridsen; Karl Friston; Niels Hjort; Louise Gyldensted; Leif Østergaard; Stefan Kiebel Journal: Neuroimage Date: 2006-09-12 Impact factor: 6.556
Authors: Henry Ma; Mark W Parsons; Soren Christensen; Bruce C V Campbell; Leonid Churilov; Alan Connelly; Bernard Yan; Chris Bladin; Than Phan; Alan P Barber; Stephen Read; Graeme J Hankey; Romesh Markus; Tissa Wijeratne; R Grimley; N Mahant; Tim Kleinig; John Sturm; A Lee; D Blacker; Richard Gerraty; M Krause; P M Desmond; S J McBride; Leanne Carey; David W Howells; C Y Hsu; Stephen M Davis; Geoffrey A Donnan Journal: Int J Stroke Date: 2012-01 Impact factor: 5.266
Authors: F Fahmi; H A Marquering; G J Streekstra; L F M Beenen; B K Velthuis; E VanBavel; C B Majoie Journal: AJNR Am J Neuroradiol Date: 2012-05-03 Impact factor: 3.825
Authors: M H Lev; A Z Segal; J Farkas; S T Hossain; C Putman; G J Hunter; R Budzik; G J Harris; F S Buonanno; M A Ezzeddine; Y Chang; W J Koroshetz; R G Gonzalez; L H Schwamm Journal: Stroke Date: 2001-09 Impact factor: 7.914
Authors: Werner Hacke; Anthony J Furlan; Yasir Al-Rawi; Antoni Davalos; Jochen B Fiebach; Franz Gruber; Markku Kaste; Leslie J Lipka; Salvador Pedraza; Peter A Ringleb; Howard A Rowley; Dietmar Schneider; Lee H Schwamm; Joaquin Serena Leal; Mariola Söhngen; Phil A Teal; Karin Wilhelm-Ogunbiyi; Max Wintermark; Steven Warach Journal: Lancet Neurol Date: 2008-12-25 Impact factor: 44.182
Authors: S Hara; Y Tanaka; S Hayashi; M Inaji; T Maehara; M Hori; S Aoki; K Ishii; T Nariai Journal: AJNR Am J Neuroradiol Date: 2019-10-10 Impact factor: 3.825
Authors: K Nael; B Mossadeghi; T Boutelier; W Kubal; E A Krupinski; J Dagher; J P Villablanca Journal: AJNR Am J Neuroradiol Date: 2014-11-27 Impact factor: 3.825
Authors: K Nael; E Tadayon; D Wheelwright; A Metry; J T Fifi; S Tuhrim; R A De Leacy; A H Doshi; H L Chang; J Mocco Journal: AJNR Am J Neuroradiol Date: 2019-08-14 Impact factor: 3.825
Authors: Robert J Harris; Timothy F Cloughesy; Anthony J Hardy; Linda M Liau; Whitney B Pope; Phioanh L Nghiemphu; Albert Lai; Benjamin M Ellingson Journal: J Neurooncol Date: 2015-03-15 Impact factor: 4.130
Authors: K Nael; A Doshi; R De Leacy; J Puig; M Castellanos; J Bederson; T P Naidich; J Mocco; M Wintermark Journal: AJNR Am J Neuroradiol Date: 2017-12-07 Impact factor: 3.825
Authors: Manuel Alexander Schmidt; Michael Knott; Philip Hoelter; Tobias Engelhorn; Elna Marie Larsson; Than Nguyen; Marco Essig; Arnd Doerfler Journal: Br J Radiol Date: 2019-10-24 Impact factor: 3.039