, Volume 55, Issue 10, pp 1197–1203 | Cite as

Assessment of the accuracy of a Bayesian estimation algorithm for perfusion CT by using a digital phantom

  • Makoto SasakiEmail author
  • Kohsuke Kudo
  • Timothé Boutelier
  • Fabrice Pautot
  • Soren Christensen
  • Ikuko Uwano
  • Jonathan Goodwin
  • Satomi Higuchi
  • Kenji Ito
  • Fumio Yamashita
Diagnostic Neuroradiology



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.


Perfusion 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.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Makoto Sasaki
    • 1
    Email author
  • Kohsuke Kudo
    • 1
  • Timothé Boutelier
    • 2
  • Fabrice Pautot
    • 2
  • Soren Christensen
    • 3
  • Ikuko Uwano
    • 1
  • Jonathan Goodwin
    • 1
  • Satomi Higuchi
    • 1
  • Kenji Ito
    • 1
  • Fumio Yamashita
    • 1
  1. 1.Division of Ultrahigh Field MRI, Institute for Biomedical SciencesIwate Medical UniversityYahabaJapan
  2. 2.Department of Research & InnovationOlea MedicalLa CiotatFrance
  3. 3.Department of Neurology and Radiology, Royal Melbourne HospitalUniversity of MelbourneVictoriaAustralia

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