Model-Based Image Reconstruction (MBIR) methods significantly enhance the quality of tomographic reconstruction in contrast to analytical techniques. However, the intensive computational time and memory required by MBIR limit its use for many practical real-time applications. But, with
increasing availability of parallel computing resources, distributed MBIR algorithms can overcome this limitation on computational performance. In this paper, we propose a novel distributed and iterative approach to Computed Tomography (CT) reconstruction based on the Multi-Agent Consensus
Equilibrium (MACE) framework. We formulate CT reconstruction as a consensus optimization problem wherein the objective function, and consequently the system matrix, is split across multiple disjoint view-subsets. This produces multiple regularized sparse-view reconstruction problems that are
tied together by a consensus constraint, and these problems can be solved in parallel within the MACE framework. Further, we solve each sub-problem inexactly, using only 1 full pass of the Iterative Coordinate Descent (ICD) optimization technique. Yet, our distributed approach is convergent.
Finally, we validate our approach with experiments on real 2D CT data.
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Document Type: Research Article
January 1, 2018
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