Affiliations: National Digital Switching System Engineering and
Technological Research Center, Zhengzhou, Henan, China | The Beijing City Key Lab of Medical Physics and
Engineering, Peking University, Beijing, China
Note: [] Corresponding author: Bin Yan, National Digital Switching System
Engineering and Technological Research Center, Zhengzhou 450002, Henan, China.
E-mail: [email protected]
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.
Keywords: Sparse-view reconstruction, total variation minimization, inexact alternating direction method, distributed reconstruction algorithm, block-splitting distribution
