Assessment of the accuracy of a Bayesian estimation algorithm for perfusion CT by using a digital phantom - Sasaki, Makoto; Kudo, Kohsuke; Boutelier, Timothé; Pautot, Fabrice; Christensen, Soren; Uwano, Ikuko; Goodwin, Jonathan; Higuchi, Satomi; Ito, Kenji; Yamashita, Fumio (Neuroradiology, 2013)
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.