An improved iterative SENSE reconstruction method

Abstract

The conjugate-gradient (CG)-based non-Cartesian SENSE reconstruction usually exhibits unstable convergence behavior due to the ill conditioning of the generalized encoding matrix (GEM). To overcome this difficulty, an improved iterative SENSE approach is presented. During a so-called Lanczos iteration process, which is equally efficient as CG, the inversion of GEM can be gradually approximated by calculating inversions of a series of small tridiagonal matrices. In this fashion, inner regularization can be incorporated into the reconstruction without touching the iteration process. The degree of regularization can be determined based on the eigenvalue information provided by the Lanczos process. With inner regularization adaptively applied for every iteration vector, the convergence behavior of iterative SENSE can be significantly improved and noise amplification can be avoided. The feasibility of this novel iterative SENSE technique is demonstrated by radial and spiral MRI experiments. © 2007 Wiley Periodicals, Inc.