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.

中文翻译：

---

使用多代理共识均衡的分布式迭代 CT 重建

相对于分析技术，基于模型的图像重建 (MBIR) 方法显着提高了计算机断层扫描 (CT) 重建的质量，但受到高计算成本的限制。在本文中，我们提出了一种多智能体共识均衡（MACE）算法，用于在大量并行节点上分配 MBIR 重建的计算和内存。在MACE中，每个节点只存储一个稀疏的视图子集和系统矩阵的一小部分，每个并行节点执行局部稀疏视图重建，基于其他节点的重复反馈，收敛到全局最优。我们的分布式方法还可以将高级降噪器作为先验来提高重建质量。在这种情况下，我们获得了即插即用 (PnP) 先验的串行框架的并行解决方案，我们称之为 MACE-PnP。为了使 MACE 实用，我们引入了一种部分更新方法，该方法消除了嵌套迭代并证明它收敛到相同的全局解。最后，我们在具有真实 CT 数据的分布式存储系统上验证了我们的方法。我们还在一台可以实时执行大规模重建的大型超级计算机上演示了我们的方法的实现。