  Spectral CT is an emerging modality that uses a data acquisition scheme with
varied spectral responses to provide enhanced material discrimination in
addition to the structural information of conventional CT. Existing clinical
and preclinical designs with this capability include kV-switching,
split-filtration, and dual-layer detector systems to provide two spectral
channels of projection data. In this work, we examine an alternate design based
on a spatial-spectral filter. This source-side filter is made up a linear array
of materials that divide the incident x-ray beam into spectrally varied
beamlets. This design allows for any number of spectral channels; however, each
individual channel is sparse in the projection domain. Model-based iterative
reconstruction methods can accommodate such sparse spatial-spectral sampling
patterns and allow for the incorporation of advanced regularization. With the
goal of an optimized physical design, we characterize the effects of design
parameters including filter tile order and filter tile width and their impact
on material decomposition performance. We present results of numerical
simulations that characterize the impact of each design parameter using a
realistic CT geometry and noise model to demonstrate feasibility. Results for
filter tile order show little change indicating that filter order is a
low-priority design consideration. We observe improved performance for narrower
filter widths; however, the performance drop-off is relatively flat indicating
that wider filter widths are also feasible designs.
