A Preconditioned Algorithm for Model-Based Iterative CT Reconstruction and Material Decomposition from Spectral CT Data

TL;DR

This paper introduces a preconditioned iterative algorithm for spectral CT reconstruction and material decomposition, improving stability and accuracy by addressing the ill-conditioned inverse problem inherent in basis material estimation.

Contribution

The paper presents a novel preconditioned optimization algorithm tailored for nonlinear penalized weighted least-squares in spectral CT, enhancing material decomposition accuracy.

Findings

Reduces beam-hardening artifacts in spectral CT images

Improves stability of basis material density estimation

Demonstrates superior performance over existing methods

Abstract

Model-based material decomposition is a statisticaliterative reconstruction framework where basis material densityimages are estimated directly from spectral CT data. This methoduses a physical model for polyenergetic x-ray transmission andattenuation and therefore it does not typically suffer frombeam-hardening artifacts. However, this estimation is a poorly-conditioned inverse problem due to the strong anticorrelationbetween basis materials. In this work we propose an precondi-tioned optimization algorithm for a nonlinear penalized weightedleast-squares objective function.