Optical flow has been used for matching or tracking of individual image objects through a time sequence of images and is applied to the problem of image interpolation by treating the serial slice images as a spatial sequence. Calculation of optical flow between two images results in a 'velocity vector map' indicating the relative displacement between similar structures in both images. Thus, individual vectors are used to match points in adjoining images and interpolate between their corresponding intensities. The interpolated intensity is then redistributed and normalized by unit area in the interpolated slice. The accuracy of optical flow interpolation is evaluated by generating an interpolated slice, i', from original slices i-l and i+l and then comparing i' with the original middle scanned slice, i. Optical flow interpolation compares favorably to linear interpolation both visually and quantitatively. Quantitative comparison, i-i', shows a 253% improvement over linear interpolation for synthetic binary images and a 13% improvement over linear interpolation for grayscale CT images.