Iterative GRAPPA using Wiener filter
Wan Kim 1 and Yihang Zhou 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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