Yuchou Chang1, Dong Liang2, Leslie Ying3
1Electrical Engineering, University of Wisconsin - Milwaukee, Milwaukee, WI, United States; 2Paul C. Lauterbur Research Centre for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, China; 3Department of Biomedical Engineering, Department of Electrical Engineering, State University of New York at Buffalo, Buffalo, NY, United States
To reduce the reconstruction complexity in parallel imaging, principal component analysis (PCA) has been used to compress large array coils into a new set of fewer virtual channels. In this study, a novel kernel (nonlinear) PCA approach is proposed to achieve noise suppression and channel reductions simultaneously. Using GRAPPA as the reconstruction method, experimental results demonstrate that the reconstruction from channels compressed by the proposed kernel PCA method has a higher SNR than those compressed by PCA or uncompressed conventional GRAPPA, while the proposed method takes almost the same computation time as the PCA method.