A variety of popular k-space reconstruction methods (e.g., GRAPPA, SPIRiT, SAKE, LORAKS) assume that missing k-space data can be interpolated by convolving the k-space data with appropriate filters. In most of these methods, the kernel shape is usually chosen to be rectangular. However, when these filters are interpreted in the spatial domain, the use of rectangular kernels implies that the filters will have anisotropic resolution. In this work, we investigate the use of elliptical kernels that have more isotropic resolution. Results demonstrate that elliptical kernels have better reconstruction performance, lower computational complexity, and lower memory usage than rectangular kernels.
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