Tailored 3D Random Sampling Patterns for Nonlinear Parallel Imaging

Florian Knoll¹, Christian Clason², Rudolf Stollberger¹

The idea of randomized 3D Cartesian subsampling was proposed within the framework of compressed sensing. The optimal design of these sampling patterns is an open problem, especially the determination of the correct ratio of low to high frequency sample points. The goal of this work is to show that it is possible to construct an adapted random sampling pattern by using measured k-space data as a reference, which automatically ensures an appropriate distribution of sample points for different types of scans. In this work, these sampling patterns were used in combination with regularized nonlinear inversion for parallel imaging. This allows the use of very high acceleration factors while still yielding images with excellent image quality.