Abstract
With the development of compressive sensing theory, image reconstruction from few-view projections has been paid considerable research attention in the field of computed tomography (CT). Total variation (TV)-based CT image reconstruction has been shown experimentally to be capable of producing accurate reconstructions from sparse-view data. Motivated by the need of solving few-view reconstruction problem with large scale data, a general block distribution reconstruction algorithm based on TV minimization and the alternating direction method (ADM) has been developed in this study. By utilizing the inexact ADM, which involves linearization and proximal point techniques, the algorithm is relatively simple and hence convenient for the derivation and distributed implementation. And because the data as well as the computation are distributed to individual nodes, an outstanding acceleration factor is achieved. Experimental results demonstrate that the proposed method can accelerate the alternating direction total variation minimization (ADTVM) algorithm with nearly no loss of accuracy, which means compared with ADTVM, the proposed algorithm has a better accuracy with same running time.