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Open Access Distributed Framework for Fast Iterative CT Reconstruction from View-subsets

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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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Keywords: COMPUTATIONAL IMAGING; CT RECONSTRUCTION; MODULAR FRAMEWORK

Document Type: Research Article

Publication date: January 1, 2018

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  • For more than 30 years, the Electronic Imaging Symposium has been serving those in the broad community - from academia and industry - who work on imaging science and digital technologies. The breadth of the Symposium covers the entire imaging science ecosystem, from capture (sensors, camera) through image processing (image quality, color and appearance) to how we and our surrogate machines see and interpret images. Applications covered include augmented reality, autonomous vehicles, machine vision, data analysis, digital and mobile photography, security, virtual reality, and human vision. IS&T began sole sponsorship of the meeting in 2016. All papers presented at EIs 20+ conferences are open access.

    Please note: For purposes of its Digital Library content, IS&T defines Open Access as papers that will be downloadable in their entirety for free in perpetuity. Copyright restrictions on papers vary; see individual paper for details.

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