  Increased noise is a general concern for dual-energy material decomposition.
Here, we develop an image-domain material decomposition algorithm for
dual-energy CT (DECT) by incorporating an edge-preserving filter into the Local
HighlY constrained backPRojection Reconstruction (HYPR-LR) framework. With
effective use of the non-local mean, the proposed algorithm, which is referred
to as HYPR-NLM, reduces the noise in dual energy decomposition while preserving
the accuracy of quantitative measurement and spatial resolution of the
material-specific dual energy images. We demonstrate the noise reduction and
resolution preservation of the algorithm with iodine concentrate numerical
phantom by comparing the HYPR-NLM algorithm to the direct matrix inversion,
HYPR-LR and iterative image-domain material decomposition (Iter-DECT). We also
show the superior performance of the HYPR-NLM over the existing methods by
using two sets of cardiac perfusing imaging data. The reference drawn from the
comparison study includes: (1) HYPR-NLM significantly reduces the DECT material
decomposition noise while preserving quantitative measurements and
high-frequency edge information, and (2) HYPR-NLM is robust with respect to
parameter selection.
