Among the most important parameters that characterize CT data collection are the linear sample spacing, and the nominal beam-width. In this paper, we study the effects of these parameters on CT imaging using both a theoretical model and simulated images. An exact treatment of the point spread function in our model, which properly accounts for aliasing and reconstruction algorithm effects, is given for the case of parallel beam geometry. It is shown that it is always desirable to make the sample spacing as small as possible. In particular, aliasing artifacts are shown to decrease as sample spacing is decreased, both in the theoretical point spread function and in simulated CT images. Furthermore, abasing is shown to destroy the spatial invariance of the point spread function, whose shape will then depend on the exact location of the point in the scan field. Also investigated is the “volcano effect”, in which the point spread function develops a central depression when small sample spacing is used. It is shown that this effect produces no unusual distortions in the reconstruction of objects larger in diameter than the beam width; and further, that an attempt to avoid the volcano effect by means of inadequate sampling can have disastrous consequences for the reconstructed image.