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.
@article{arxiv.2010.01371,
title = {A Preconditioned Algorithm for Model-Based Iterative CT Reconstruction and Material Decomposition from Spectral CT Data},
author = {Matthew Tivnan and Wenying Wang and J. Webster Stayman},
journal= {arXiv preprint arXiv:2010.01371},
year = {2020}
}