Yu Ding1, Hui Xue2, Ti-chiun Chang3, Christoph Guetter3, Orlando Simonetti1
1Dorothy M. Davis Heart and Lung Research Institute, The Ohio State University, Columbus, OH, United States; 2siemens corporate research; 3Siemens Corporate Research
Several new algorithms have been proposed to take advantage of k-space correlations, such as SPIRiT and PRUNO. However, these methods have no closed-form solutions, and can only be solved using computationally-intensive iterative methods. We propose a new k-space based pMRI technique, self-consistent GRAPPA by including an extra set of linear equations utilizing the intrinsic correlation between skipped k-space points. SC-GRAPPA combines the linear equations of traditional GRAPPA with these additional equations to solve for the missing k-space data. SC-GRAPPA utilizes a least-square solution of the linear equations, and therefore has a closed-form solution without any free parameters.