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Measurement of the rate constant parameters of the tracer 18F-FDG, used with positron emission tomography (PET) to determine the cerebral metabolic rate of glucose (CMRGlc), can provide a clear understanding of the physiological processes in the human brain. At present, the methods that are widely used to obtain CMRGlc, such as nonlinear least squares (NLS), first require the reconstruction of a time sequence of images. The reconstruction of these images requires a large amount of computation, especially in 3D Depth-of Interaction PET (DOI-PET), and the nonlinear based methods also require a large amount of computation. In this paper, we propose a fast parametric image reconstruction method for 18F-FDG dynamic PET studies. In our method a deconvolving process is first employed on the time sequential projection data to remove the effect of the measured plasma time activity. The deconvolved terms are integrated over three different time intervals and the parameters for determining CMRGlc can be obtained analytically. Our method requires only three reconstructing processes and reduces the computational demand to estimate CMRGlc. The algorithm performance is evaluated using a digital phantom and a clinical data set and the results show that the proposed method produces images with the same or better quality as the images from the NLS method, with much less computation compared to the NLS method.

Fast determination method of cerebral metabolic rate images of glucose using dynamic PET data.