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Abstract #4593

Scalable self-calibrated interpolation of non-Cartesian data with GRAPPA

Seng-Wei Chieh1, Mostafa Kaveh1, Mehmet Akcakaya1,2, and Steen Moeller2

1Electrical and Computer Engineering, University of Minnesota-Twin Cities, Minneapolis, MN, United States, 2Center for Magnetic Resonance Research, Minneapolis, MN, United States

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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