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Abstract #2413

Iterative GRAPPA using Wiener filter

Wan Kim 1 and Yihang Zhou 1

1 The State University of New York at Buffalo, Buffalo, NY, United States

We present a new iterative method using Wiener filter. In contrast to the conventional GRAPPA where only the auto calibration signals (ACS) are used to find the convolution weights, our proposed method iteratively updates the convolution weights using both the acquired and reconstructed data from previous iterations in the entire k-space. To avoid error propagation, the method applies adaptive Wiener filter on the reconstructed data. Experimental results demonstrate that even with a smaller number of ACS lines the proposed method improves the SNR when compared to GRAPPA.

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