Distributed Iterative CT Reconstruction using Multi-Agent Consensus Equilibrium

TL;DR

This paper introduces a distributed multi-agent consensus equilibrium algorithm for CT reconstruction that reduces computational costs, enables parallel processing, and incorporates advanced denoisers, achieving real-time large-scale reconstructions.

Contribution

It proposes a novel distributed framework for MBIR in CT that distributes computation, memory, and integrates denoisers, with a practical partial update method ensuring convergence.

Findings

Achieves convergence to global optimum in distributed CT reconstruction.

Enables real-time large-scale reconstruction on supercomputers.

Incorporates advanced denoisers to improve image quality.

Abstract

Model-Based Image Reconstruction (MBIR) methods significantly enhance the quality of computed tomographic (CT) reconstructions relative to analytical techniques, but are limited by high computational cost. In this paper, we propose a multi-agent consensus equilibrium (MACE) algorithm for distributing both the computation and memory of MBIR reconstruction across a large number of parallel nodes. In MACE, each node stores only a sparse subset of views and a small portion of the system matrix, and each parallel node performs a local sparse-view reconstruction, which based on repeated feedback from other nodes, converges to the global optimum. Our distributed approach can also incorporate advanced denoisers as priors to enhance reconstruction quality. In this case, we obtain a parallel solution to the serial framework of Plug-n-play (PnP) priors, which we call MACE-PnP. In order to make MACE practical, we introduce a partial update method that eliminates nested iterations and prove that it converges to the same global solution. Finally, we validate our approach on a distributed memory system with real CT data. We also demonstrate an implementation of our approach on a massive supercomputer that can perform large-scale reconstruction in real-time.