Three-Dimensional Support Function Estimation and Application for Projection Magnetic Resonance Imaging

摘要

Magnetic resonance imaging (MRI) of nuclei that have very short relaxation times is conveniently based on spherical sampling. We have presented a least squares framework for reconstructing three-dimensional (3D) source distribution images from such data. In this paper, we describe a practical algorithm for 3D support function estimation, which forms the basis for a method called focus of attention. By essentially identifying and eliminating equations and unknowns that merely represent background data, this data-driven preprocessing scheme effectively reduces the computational burden associated with our algebraic approach to projection MRI.