  X-ray computed tomography (CT) based on photon counting detectors (PCD)
extends standard CT by counting detected photons in multiple energy bins. PCD
data can be used to increase the contrast-to-noise ratio (CNR), increase
spatial resolution, reduce radiation dose, reduce injected contrast dose, and
compute a material decomposition using a specified set of basis materials.
Current commercial and prototype clinical photon counting CT systems utilize
PCD-CT reconstruction methods that either reconstruct from each spectral bin
separately, or first create an estimate of a material sinogram using a
specified set of basis materials and then reconstruct from these material
sinograms. However, existing methods are not able to utilize simultaneously and
in a modular fashion both the measured spectral information and advanced prior
models in order to produce a material decomposition.
  We describe an efficient, modular framework for PCD-based CT reconstruction
and material decomposition using on Multi-Agent Consensus Equilibrium (MACE).
Our method employs a detector proximal map or agent that uses PCD measurements
to update an estimate of the pathlength sinogram. We also create a prior agent
in the form of a sinogram denoiser that enforces both physical and empirical
knowledge about the material-decomposed sinogram. The sinogram reconstruction
is computed using the MACE algorithm, which finds an equilibrium solution
between the two agents, and the final image is reconstructed from the estimated
sinogram. Importantly, the modularity of our method allows the two agents to be
designed, implemented, and optimized independently. Our results on simulated
data show a substantial (450%) CNR boost vs conventional maximum likelihood
reconstruction when applied to a phantom used to evaluate low contrast
detectability.
