A Bayesian estimation method for cerebral blood flow measurement by area-detector CT perfusion imaging.

ABSTRACT

PURPOSE: Bayesian estimation with advanced noise reduction (BEANR) in CT perfusion (CTP) could deliver more reliable cerebral blood flow (CBF) measurements than the commonly used reformulated singular value decomposition (rSVD). We compared the efficacy of CBF measurement by CTP using BEANR and rSVD, evaluating both relative to N-isopropyl-p-[(123) I]- iodoamphetamine (123I-IMP) single-photon emission computed tomography (SPECT) as a reference standard, in patients with cerebrovascular disease. METHODS: Thirty-one patients with suspected cerebrovascular disease underwent both CTP on a 320 detector-row CT system and SPECT. We applied rSVD and BEANR in the ischemic and contralateral regions to create CBF maps and calculate CBF ratios from the ischemic side to the healthy contralateral side (CBF index). The analysis involved comparing the CBF index between CTP methods and SPECT using Pearson's correlation and limits of agreement determined with Bland-Altman analyses, before comparing the mean difference in the CBF index between each CTP method and SPECT using the Wilcoxon matched pairs signed-rank test. RESULTS: The CBF indices of BEANR and 123I-IMP SPECT were significantly and positively correlated (r = 0.55, p < 0.0001), but there was no significant correlation between the rSVD method and SPECT (r = 0.15, p > 0.05). BEANR produced smaller limits of agreement for CBF than rSVD. The mean difference in the CBF index between BEANR and SPECT differed significantly from that between rSVD and SPECT (p < 0.001). CONCLUSIONS: BEANR has a better potential utility for CBF measurement in CTP than rSVD compared to SPECT in patients with cerebrovascular disease.