Abstract
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.
Similar content being viewed by others
References
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 32:2021–2028
Kudo K, Sasaki M, Ogasawara K, Terae S, Ehara S, Shirato H (2009) Difference in tracer delay-induced effect among deconvolution algorithms in CT perfusion analysis: quantitative evaluation with digital phantoms. Radiology 251:241–249
Kudo K, Sasaki M, Yamada K, Momoshima S, Utsunomiya H, Shirato H, Ogasawara K (2010) Differences in CT perfusion maps generated by different commercial software: quantitative analysis by using identical source data of acute stroke patients. Radiology 254:200–209
Sasaki M, Kudo K, Ogasawara K, Fujiwara S (2009) Tracer delay-insensitive algorithm can improve reliability of CT perfusion imaging for cerebrovascular steno-occlusive disease: comparison with quantitative single-photon emission CT. Am J Neuroradiol 30:188–193
Fahmi F, Marquering HA, Streekstra GJ, Beenen LF, Velthuis BK, Vanbavel E, Majoie CB (2012) Differences in CT perfusion summary maps for patients with acute ischemic stroke generated by 2 software packages. Am J Neuroradiol 33:2074–2080
Kudo K, Christensen S, Sasaki M, Ostergaard L, Shirato H, Ogasawara K, Wintermark M, Warach S, Warach (2013) Accuracy and reliability assessment of CT and MR perfusion analysis software using a digital phantom. Radiology 267:201–211
Mouridsen K, Friston K, Hjort N, Gyldensted L, Ostergaard L, Kiebel S (2006) Bayesian estimation of cerebral perfusion using a physiological model of microvasculature. NeuroImage 33:570–579
Boutelier T, Kudo K, Pautot F, Sasaki M (2012) Bayesian hemodynamic parameter estimation by bolus tracking perfusion weighted imaging. IEEE Trans Med Imaging 31:1381–1395
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 50:164–174
Ostergaard L (2005) Principles of cerebral perfusion imaging by bolus tracking. J Magn Reson Imaging 22:710–717
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 37:212–221
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 8:141–150
van Osch MJ, Vonken EJ, Bakker CJ, Viergever MA (2001) Correcting partial volume artifacts of the arterial input function in quantitative cerebral perfusion MRI. Magn Reson Med 45:477–485
Calamante F, Willats L, Gadian DG, Connelly A (2006) Bolus delay and dispersion in perfusion MRI: implications for tissue predictor models in stroke. Magn Reson Med 55:1180–1185
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 40:2055–2061
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 60:508–517
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 7:299–309
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 7:74–80
Calamante F, Christensen S, Desmond PM, Ostergaard L, Davis SM, Connelly A (2010) The physiological significance of the time-to-maximum (Tmax) parameter in perfusion MRI. Stroke 41:1169–1174
Acknowledgments
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.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Sasaki, M., Kudo, K., Boutelier, T. et al. Assessment of the accuracy of a Bayesian estimation algorithm for perfusion CT by using a digital phantom. Neuroradiology 55, 1197–1203 (2013). https://doi.org/10.1007/s00234-013-1237-7
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00234-013-1237-7