Nonlinear GRAPPA is a kernel-based approach for improving parallel imaging reconstruction, by reducing noise-induced error. Virtual coil conception has been applied into the reconstruction process for parallel acquisitions, by generating virtual coils containing conjugate symmetric k-space signals from actual multiple-channel coils. In this work, we proposed a hybrid method to combine nonlinear GRAPPA and virtual coil conception for incorporating additional image- and coil-phase information into the reconstruction process. The experiments of in vivo human brain data show that the proposed method can reduce more noise and artifacts than the traditional GRAPPA and original Nonlinear GRAPPA methods.
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