Conventional non-Cartesian parallel imaging reconstruction in k-space necessitates large amounts of calibration data for successful estimation of region-specific interpolation kernels. In this work, we propose a self-calibration strategy for obtaining region-specific non-Cartesian interpolation kernels from a single calibration dataset. This enables simple and efficient high-quality reconstruction of non-Cartesian parallel imaging.
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