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Inclusion of brain tissues with different rates of blood flow and metabolism within a voxel or region of interest is an unavoidable problem with positron emission tomography due to its limited spatial resolution. Because regional cerebral blood flow (rCBF) is higher in gray matter than in white matter, the partial volume effect leads to underestimation of rCBF in gray matter when rCBF in the region as a whole is determined. Furthermore, weighted-average rCBF itself is underestimated if the kinetic model used in the analysis fails to account for the tissue heterogeneity. The authors have derived a computationally efficient method for estimating both gray matter and weighted-average rCBF in heterogeneous tissues and validated the method in simulation studies. The method is based on a model that represents a heterogeneous tissue as a weighted mixture of two homogeneous tissues. A linear least squares algorithm is used to estimate the model parameters.