Fast X-ray CT image reconstruction using a linearized augmented Lagrangian method with ordered subsets.

ABSTRACT: Augmented Lagrangian (AL) methods for solving convex optimization problems with linear constraints are attractive for imaging applications with composite cost functions due to the empirical fast convergence rate under weak conditions. However, for problems such as X-ray computed tomography (CT) image reconstruction, where the inner least-squares problem is challenging and requires iterations, AL methods can be slow. This paper focuses on solving regularized (weighted) least-squares problems using a linearized variant of AL methods that replaces the quadratic AL penalty term in the scaled augmented Lagrangian with its separable quadratic surrogate function, leading to a simpler ordered-subsets (OS) accelerable splitting-based algorithm, OS-LALM. To further accelerate the proposed algorithm,

SUBMITTER: Nien H 

PROVIDER: S-EPMC4315772 | biostudies-literature | 2015 Feb

REPOSITORIES: biostudies-literature