  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.
