Multi-Pose Fusion for Sparse-View CT Reconstruction Using Consensus Equilibrium

EasyChair Preprint 8959

Abstract

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.

Keyphrases: Computed Tomography, Consensus equilibrium, Model-based reconstruction, Sparse-view CT, inverse problems, plug and play

@booklet{EasyChair:8959,
  author    = {Diyu Yang and Craig Kemp and Gregery Buzzard and Charles Bouman},
  title     = {Multi-Pose Fusion for Sparse-View CT Reconstruction Using Consensus Equilibrium},
  howpublished = {EasyChair Preprint 8959},
  year      = {EasyChair, 2022}}