Assessment of the accuracy of a Bayesian estimation algorithm for perfusion CT by using a digital phantom
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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.
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.
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).
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.
KeywordsPerfusion computed tomography Digital phantom Bayesian estimation algorithm Mean transit time Cerebral blood flow
This work was supported in part by a Grant-in-Aid for Strategic Medical Science Research from the Ministry of Education, Culture, Sports, Science and Technology of Japan.
Conflict of interest
MS has served on the advisory board for Olea Medical. TB and FP are employees of Olea Medical.
- 1.Lev MH, Segal AZ, Farkas J, Hossain ST, Putman C, Hunter GJ, Budzik R, Harris GJ, Buonanno FS, Ezzeddine MA, Chang Y, Koroshetz WJ, Gonzalez RG, Schwamm LH (2001) Utility of perfusion-weighted CT imaging in acute middle cerebral artery stroke treated with intra-arterial thrombolysis: prediction of final infarct volume and clinical outcome. Stroke 32:2021–2028PubMedCrossRefGoogle Scholar
- 9.Wu O, Ostergaard L, Weisskoff RM, Benner T, Rosen BR, Sorensen AG (2003) Tracer arrival timing-insensitive technique for estimating flow in MR perfusion-weighted imaging using singular value decomposition with a block-circulant deconvolution matrix. Magn Reson Med 50:164–174PubMedCrossRefGoogle Scholar
- 11.Hanson EH, Roach CJ, Day KJ, Peters KR, Bradley WG Jr, Ghosh K, Patton PW, McMurray RC, Orrison WW Jr (2013) Assessment of the tracer delay effect in whole-brain computed tomography perfusion: results in patients without known neuroanatomic abnormalities. J Comput Assist Tomogr 37:212–221PubMedCrossRefGoogle Scholar
- 12.Hacke W, Furlan AJ, Al-Rawi Y, Davalos A, Fiebach JB, Gruber F, Kaste M, Lipka LJ, Pedraza S, Ringleb PA, Rowley HA, Schneider D, Schwamm LH, Leal JS, Sohngen M, Teal PA, Wilhelm-Ogunbiyi K, Wintermark M, Warach S (2009) Intravenous desmoteplase in patients with acute ischaemic stroke selected by MRI perfusion-diffusion weighted imaging or perfusion CT (DIAS-2): a prospective, randomised, double-blind, placebo-controlled study. Lancet Neurol 8:141–150PubMedCrossRefGoogle Scholar
- 15.Christensen S, Mouridsen K, Wu O, Hjort N, Karstoft H, Thomalla G, Rother J, Fiehler J, Kucinski T, Ostergaard L (2009) Comparison of 10 perfusion MRI parameters in 97 sub-6-hour stroke patients using voxel-based receiver operating characteristics analysis. Stroke 40:2055–2061PubMedCrossRefGoogle Scholar
- 16.Albers GW, Thijs VN, Wechsler L, Kemp S, Schlaug G, Skalabrin E, Bammer R, Kakuda W, Lansberg MG, Shuaib A, Coplin W, Hamilton S, Moseley M, Marks MP (2006) Magnetic resonance imaging profiles predict clinical response to early reperfusion: the diffusion and perfusion imaging evaluation for understanding stroke evolution (DEFUSE) study. Ann Neurol 60:508–517PubMedCrossRefGoogle Scholar
- 17.Davis SM, Donnan GA, Parsons MW, Levi C, Butcher KS, Peeters A, Barber PA, Bladin C, De Silva DA, Byrnes G, Chalk JB, Fink JN, Kimber TE, Schultz D, Hand PJ, Frayne J, Hankey G, Muir K, Gerraty R, Tress BM, Desmond PM (2008) Effects of alteplase beyond 3 h after stroke in the Echoplanar Imaging Thrombolytic Evaluation Trial (EPITHET): a placebo-controlled randomised trial. Lancet Neurol 7:299–309PubMedCrossRefGoogle Scholar
- 18.Ma H, Parsons MW, Christensen S, Campbell BC, Churilov L, Connelly A, Yan B, Bladin C, Phan T, Barber AP, Read S, Hankey GJ, Markus R, Wijeratne T, Grimley R, Mahant N, Kleinig T, Sturm J, Lee A, Blacker D, Gerraty R, Krause M, Desmond PM, McBride SJ, Carey L, Howells DW, Hsu CY, Davis SM, Donnan GA (2012) A multicentre, randomized, double-blinded, placebo-controlled Phase III study to investigate EXtending the time for Thrombolysis in Emergency Neurological Deficits (EXTEND). Int J Stroke 7:74–80PubMedCrossRefGoogle Scholar