Multi-Material Decomposition Using Statistical Image Reconstruction in X-Ray CT

Dual-energy (DE) CT scans provide two sets of measurements at two different source energies. In principle, two materials can be accurately decomposed from DECT measurements. For triple-material decomposition, a third constraint, such as volume or mass conservation, is required to solve three sets of unknowns from two sets of measurements. An image-domain (ID) method [1] has been proposed recently to reconstruct multiple materials using DECT. This method assumes each pixel contains at most three materials out of several possible materials and decomposes a mixture pixel by pixel. We propose a penalizedlikelihood (PL) method with edge-preserving regularizers for each material to reconstruct multi-material images using a similar constraint. Comparing with the image-domain method the PL method greatly reduced noise, streak and cross-talk artifacts, and achieved much smaller root-mean-square (RMS) errors.