Spectral x-ray imaging based on photon-counting x-ray detectors (PCXD) is an area of growing interest. By measuring
the energy of x-ray photons, a spectral CT system can better differentiate elements using a single scan. However, the
spatial resolution achievable with most PCXDs limits their application, particularly in preclinical CT imaging.
Consequently, our group is developing a hybrid micro-CT scanner based on a high-resolution, energy-integrating (EID)
detector and a lower-resolution, PCXD. To complement this system, we propose and demonstrate a hybrid, spectral CT
reconstruction algorithm which robustly combines the spectral contrast of the PCXD with the spatial resolution of the
EID. Specifically, the high-resolution, spectrally resolved data (X) is recovered as the sum of two matrices: one with low
column rank (XL) determined from the EID data and one with intensity gradient sparse columns (XS) corresponding to the
upsampled spectral contrast obtained from the PCXD data. We test the proposed algorithm in a feasibility study focused
on molecular imaging of atherosclerotic plaque using activatable iodine and gold nanoparticles. The results show
accurate estimation of material concentrations at increased spatial resolution for a voxel size ratio between the PCXD
and the EID of 500 μm3:100 μm3. Specifically, regularized, iterative reconstruction of the MOBY mouse phantom
around the K-edges of iodine (33.2 keV) and gold (80.7 keV) reduces the reconstruction error by more than a factor of
three relative to least-squares, algebraic reconstruction. Likewise, the material decomposition accuracy into iodine, gold,
calcium, and water improves by more than a factor of two.