  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.
