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

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.

References in corpus (2)

• Penalized-Likelihood Reconstruction with High-Fidelity Measurement Models for High-Resolution Cone-Beam Imaging
• Three material decomposition for spectral computed tomography enabled by block-diagonal step-preconditioning