The purpose of this work was to improve of the spatial resolution of a whole-body positron emission tomography (PET) system for experimental studies of small animals by incorporation of scanner characteristics into the process of iterative image reconstruction. The image-forming characteristics of the PET camera were characterized by a spatially variant line-spread function (LSF), which was determined from 49 activated copper-64 line sources positioned over a field of view (FOV) of 21.0 cm. This information was used to model the image degradation process. During the course of iterative image reconstruction, the forward projection of the estimated image was blurred with the LSF at each iteration step before the estimated projections were compared with the measured projections. The imaging characteristics of the high-resolution algorithm were investigated in phantom experiments. Moreover, imaging studies of a rat and two nude mice were performed to evaluate the imaging properties of our approach in vivo. The spatial resolution of the scanner perpendicular to the direction of projection could be approximated by a one-dimensional Gaussian-shaped LSF with a full-width at half-maximum increasing from 6.5 mm at the centre to 6.7 mm at a radial distance of 10.5 cm. The incorporation of this blurring kernel into the iteration formula resulted in a significantly improved spatial resolution of about 3.9 mm over the examined FOV. As demonstrated by the phantom and the animal experiments, the high-resolution algorithm not only led to a better contrast resolution in the reconstructed emission scans but also improved the accuracy for quantitating activity concentrations in small tissue structures without leading to an amplification of image noise or image mottle. The presented data-handling strategy incorporates the image restoration step directly into the process of algebraic image reconstruction and obviates the need for ill-conditioned "deconvolution" procedures to be performed on the projections or on the reconstructed image. In our experience, the proposed algorithm is of special interest in experimental studies of small animals.