论文标题
使用共识平衡的稀疏视图CT重建的多订单融合
Multi-Pose Fusion for Sparse-View CT Reconstruction Using Consensus Equilibrium
论文作者
论文摘要
CT成像通过从投影集合中重建感兴趣的对象来起作用。诸如过滤后背影（FBP）之类的传统方法在围绕固定旋转轴上获取的投影图像上工作。但是，对于某些CT问题，希望从从多个旋转轴获取的投影数据中执行关节重建。 在本文中，我们提出了多孔融合，这是一种新型算法，该算法从单个对象的多个姿势中获得的CT扫描进行了联合断层造影重建，每个对象都有一个独特的旋转轴。我们的方法使用多代理共识均衡（MACE），即插头播放的扩展，作为从不同姿势集成投影数据的框架。我们将我们的方法应用于模拟数据，并证明比单姿势重建可以实现更好的重建结果。
CT imaging works by reconstructing an object of interest from a collection of projections. Traditional methods such as filtered-back projection (FBP) work on projection images acquired around a fixed rotation axis. However, for some CT problems, it is desirable to perform a joint reconstruction from projection data acquired from multiple rotation axes. In this paper, we present Multi-Pose Fusion, a novel algorithm that performs a joint tomographic reconstruction from CT scans acquired from multiple poses of a single object, where each pose has a distinct rotation axis. Our approach uses multi-agent consensus equilibrium (MACE), an extension of plug-and-play, as a framework for integrating projection data from different poses. We apply our method on simulated data and demonstrate that Multi-Pose Fusion can achieve a better reconstruction result than single pose reconstruction.
