Ultra-Sparse View Reconstruction for Flash X-Ray Imaging using Consensus Equilibrium

TL;DR

This paper introduces a novel Multi-Agent Consensus Equilibrium framework for ultra-sparse view 3D reconstruction in Flash X-ray CT, significantly improving image quality from only four views.

Contribution

It develops a generalized Plug-and-Play approach called MACE that incorporates complex priors for ultra-sparse CT reconstruction, outperforming traditional methods.

Findings

MACE reduces artifacts in reconstructed images.

It uncovers features indiscernible with prior methods.

Demonstrated effectiveness on simulated and real data.

Abstract

A growing number of applications require the reconstructionof 3D objects from a very small number of views. In this research, we consider the problem of reconstructing a 3D object from only 4 Flash X-ray CT views taken during the impact of a Kolsky bar. For such ultra-sparse view datasets, even model-based iterative reconstruction (MBIR) methods produce poor quality results. In this paper, we present a framework based on a generalization of Plug-and-Play, known as Multi-Agent Consensus Equilibrium (MACE), for incorporating complex and nonlinear prior information into ultra-sparse CT reconstruction. The MACE method allows any number of agents to simultaneously enforce their own prior constraints on the solution. We apply our method on simulated and real data and demonstrate that MACE reduces artifacts, improves reconstructed image quality, and uncovers image features which were otherwise indiscernible.