Abstract #2431
KerNL: Parallel imaging reconstruction using Kernel-based NonLinear method
Jingyuan Lyu 1 , Yihang Zhou 1 , Ukash Nakarmi 1 , Chao Shi 1 , and Leslie Ying 1,2
1
Department of Electrical Engineering, State
University of New York at Buffalo, Buffalo, NY, United
States,
2
Department
of Biomedical Engineering, State University of New York
at Buffalo, Buffalo, NY, United States
The linear model cannot describe the relationship
between the missing and acquired k-space data in GRAPPA.
Here we propose a more general nonlinear framework for
auto-calibrated parallel imaging. In this framework,
kernel tricks are employed to represent the general
nonlinear relationship between acquired and unacquired
k-space data without increasing the computational
complexity. Identification of the nonlinear relationship
is still performed by solving linear equations. We name
the proposed method Kernel-based NonLinear (KerNL)
method. Experimental results demonstrate that the
proposed method is able to improve both image quality
and computation efficiency at high reduction factors,
compared with GRAPPA and nonlinear GRAPPA.
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