Inverse problems spanning four or more dimensions such as space, time andother independent parameters have become increasingly important. State-of-the-art 4D reconstruction methods use model based iterative reconstruction (MBIR), but depend critically on the quality of the prior modeling. Recently, plug-and-play (PnP) methods have been shown to be an effective way to incorporate advanced prior models using state-of-the-art denoising algorithms. However, state-of-the-art denoisers such as BM4D and deep convolutional neural networks (CNNs) are primarily available for 2D or 3D images and extending them to higher dimensions is difficult due to algorithmic complexity and the increased difficulty of effective training. In this paper, we present multi-slice fusion , a novel algorithm for 4D reconstruction, based on the fusion of multiple low-dimensional denoisers. Our approach uses multi-agent consensus equilibrium (MACE), an extension of plug-and-play, as a framework for integrating the multiple lower-dimensional models. We apply our method to 4D cone-beam X-ray CT reconstruction for non destructive evaluation (NDE) of samples that are dynamically moving during acquisition. We implement multi-slice fusion on distributed, heterogeneous clusters in order to reconstruct large 4D volumes in reasonable time and demonstrate the inherent parallelizable nature of the algorithm. We present simulated and real experimental results on sparse-view and limited-angle CT data to demonstrate that multi-slice fusion can substantially improve the quality of reconstructions relative to traditional methods, while also being practical to implement and train.

中文翻译：

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用于稀疏视图和有限角度 4D CT 重建的多切片融合


跨越四个或更多维度（例如空间、时间和其他独立参数）的逆问题变得越来越重要。最先进的 4D 重建方法使用基于模型的迭代重建 (MBIR)，但主要取决于先前建模的质量。最近，即插即用（PnP）方法已被证明是使用最先进的去噪算法合并先进先验模型的有效方法。然而，BM4D 和深度卷积神经网络 (CNN) 等最先进的降噪器主要可用于 2D 或 3D 图像，由于算法复杂性和有效训练难度的增加，将它们扩展到更高的维度很困难。在本文中，我们提出了多切片融合，这是一种基于多个低维降噪器融合的 4D 重建新算法。我们的方法使用多主体共识均衡（MACE）（即插即用的扩展）作为集成多个低维模型的框架。我们将我们的方法应用于 4D 锥束 X 射线 CT 重建，以对采集过程中动态移动的样本进行无损评估 (NDE)。我们在分布式异构集群上实现多切片融合，以便在合理的时间内重建大型 4D 体积，并证明该算法固有的可并行性。我们展示了稀疏视图和有限角度 CT 数据的模拟和真实实验结果，以证明多切片融合相对于传统方法可以显着提高重建质量，同时也易于实施和训练。