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Fig. 6C —Fourier spaces and k-space [7, 8]. Fourier transform (FT) of an image is represented by a 2D gray-scale magnitude image in which each pixel represents a particular spatial frequency. By convention, high frequencies are mapped to the periphery and low frequencies to the origin. Pixel intensity corresponds to the relative contribution of that frequency to the entire image. Any image (which can be thought of as a complicated wave of varying pixel intensity) can be constructed by the combination of different spatial frequencies (simple waves). Fourier transform of a simple white square on a black background, for instance, shows a cruciate pattern of increased intensity along the traditional x- and y-axes. This reflects the contribution of spatial frequencies (given by the inverse FT = iFT) most necessary to recreate the image, which happen to be orthogonal to the edges of the square. Because essentially no diagonals or curves are present in the image, these spatial frequencies are not as highly represented in the Fourier space. (Fourier transform and inverse Fourier transform images (iFT) generated with ImageJ, National Institutes of Health, Bethesda, MD)