Improved parametric images of blood flow and vascular volume by

Abstract The values of cerebral blood flow (CBF) and vascular volume (V0) can be estimated using H215O dynamic PET and the two-compartment model. In this study, we present a method that can generate parametric images of both CBF and V0, with improvement of image quality, by a single computational procedure. This method is based on the two-compartment model, and employs linear least square algorithm and cluster analysis for parameter estimation and suppressing noise on image data, respectively. The results in computer simulation showed that this method could provide the reduction of error in parameter estimation, as well as noise on parametric images of both CBF and V0, and the smaller effect of changes of CBF and V0 on the estimation of both parameters. In the PET study, this method could provide the images of CBF and V0 with improvement in quality, compared with those without clustering by showing the clear location of arterial vascular components on the V0 image. In the simulation, the error in parameter estimation was sufficiently small for K1, but not for V0. These results reveal that the presented method has the potential to make a contribution to the improved diagnosis of cerebral vascular disease and activation.