  Spectral CT offers enhanced material discrimination over single-energy
systems and enables quantitative estimation of basis material density images.
Water/iodine decomposition in contrast-enhanced CT is one of the most
widespread applications of this technology in the clinic. However, low
concentrations of iodine can be difficult to estimate accurately, limiting
potential clinical applications and/or raising injected contrast agent
requirements. We seek high-sensitivity spectral CT system designs which
minimize noise in water/iodine density estimates. In this work, we present a
model-driven framework for spectral CT system design optimization to maximize
material separability. We apply this tool to optimize the sensitivity spectra
on a spectral CT test bench using a hybrid design which combines source kVp
control and k-edge filtration. Following design optimization, we scanned a
water/iodine phantom with the hybrid spectral CT system and performed
dose-normalized comparisons to two single-technique designs which use only kVp
control or only kedge filtration. The material decomposition results show that
the hybrid system reduces both standard deviation and crossmaterial noise
correlations compared to the designs where the constituent technologies are
used individually.
