Improved Iterative Non-Cartesian SENSE Reconstruction using Inner-regularization

Abstract

The conjugate-gradient (CG)-based non-Cartesian SENSE reconstruction in many cases exhibits unstable convergence behavior. This is because the generalized encoding matrix (GEM) is usually seriously ill-conditioned due to the large dimension and the mixed encoding scheme. To overcome this difficulty, an improved iterative SENSE approach is presented. During a so-called Lanczos iteration process, inner-regularization can be incorporated into the reconstruction without touching the iteration process. With inner-regularization adaptively applied for every iteration loop, the convergence behavior of iterative SENSE can be significantly improved and noise booming can be avoided.