On the Fusion of Compton Scatter and Attenuation Data for Limited-view X-ray Tomographic Applications

TL;DR

This paper introduces a novel method that fuses Compton scatter and attenuation data to improve limited-view X-ray tomography, enabling more accurate joint recovery of mass density and photoelectric absorption.

Contribution

It develops a physical model and a variational reconstruction approach that combines energy-resolved Compton scatter data with traditional attenuation data for enhanced imaging.

Findings

Fusion of data types improves image accuracy

Energy-resolved data enhances material property recovery

Combined data yields superior results compared to using only one data type

Abstract

In this paper we demonstrate the utility of fusing energy-resolved observations of Compton scattered photons with traditional attenuation data for the joint recovery of mass density and photoelectric absorption in the context of limited view tomographic imaging applications. We begin with the development of a physical and associated numerical model for the Compton scatter process. Using this model, we propose a variational approach recovering these two material properties. In addition to the typical data-fidelity terms, the optimization functional includes regularization for both the mass density and photoelectric coefficients. We consider a novel edge-preserving method in the case of mass density. To aid in the recovery of the photoelectric information, we draw on our recent method in \cite{r15} and employ a non-local regularization scheme that builds on the fact that mass density is more stably imaged. Simulation results demonstrate clear advantages associated with the use of both scattered photon data and energy resolved information in mapping the two material properties of interest. Specifically, comparing images obtained using only conventional attenuation data with those where we employ only Compton scatter photons and images formed from the combination of the two, shows that taking advantage of both types of data for reconstruction provides far more accurate results.