Estimation of ischemic core volume using computed tomographic perfusion Bayesian versus singular value deconvolution postprocessing

Background and Purpose: Estimation of infarction based on computed tomographic perfusion (CTP) has been challenging, mainly because of noise associated with CTP data. The Bayesian method is a robust probabilistic method that minimizes effects of oscillation, tracer delay, and noise during residue function estimation compared with other deconvolution methods. This study compares CTP-estimated ischemic core volume calculated by the Bayesian method and by the commonly used block-circulant singular value deconvolution technique. Methods: Patients were included if they had (1) anterior circulation ischemic stroke, (2) baseline CTP, (3) successful recanalization defined by thrombolysis in cerebral infarction ≥IIb, and (4) minimum infarction volume of >5 mL on follow-up magnetic resonance imaging (MRI). CTP data were processed with circulant singular value deconvolution and Bayesian methods. Two established CTP methods for estimation of ischemic core volume were applied: cerebral blood flow (CBF) method (relative CBF, <30% within the region of delay >2 seconds) and cerebral blood volume method (<2 mL per 100 g within the region of relative mean transit time >145%). Final infarct volume was determined on MRI (fluid-attenuated inversion recovery images). CTP and MRI-derived ischemic core volumes were compared by univariate and Bland-Altman analysis. Results: Among 35 patients included, the mean/median (mL) difference for CTP-estimated ischemic core volume against MRI was −4/−7 for Bayesian CBF (P=0.770), 20/12 for Bayesian cerebral blood volume (P=0.041), 21/10 for circulant singular value deconvolution CBF (P=0.006), and 35/18 for circulant singular value deconvolution cerebral blood volume (P<0.001). Among all methods, Bayesian CBF provided the narrowest limits of agreement (−28 to 19 mL) in comparison with MRI. Conclusions: Despite existing variabilities between CTP postprocessing methods, Bayesian postprocessing increases accuracy and limits variability in CTP estimation of ischemic core.