Column distribution reconstruction algorithm via the alternating direction method

Abstract

With the development of compressive sensing theory, computed tomography (CT) image reconstruction from sparse-view projections has become a research hotspot. And total variation (TV)-based methods have been experimentally proved to be capable of achieving accurate reconstruction from sparse-view data. In this study, a column distributed reconstruction algorithm based on TV minimization and the alternating direction method (ADM) has been developed. The algorithm uses the inexact ADM, which involves linearization and proximal points techniques, hence it is relatively simple. Moreover, it distributes data to individual nodes and computes in parallel, so that can achieve outstanding acceleration factors. Experimental results demonstrate that the proposed method is well suited for distributed computing and can accelerate the alternating direction total variation minimization (ADTVM) algorithm with very little loss of accuracy.