Title: Image-domain multi-material decomposition for dual-energy CT based on correlation and sparsity of material images,
Abstract: Dual energy CT (DECT) enhances tissue characterization because it can produce
images of basis materials such as soft-tissue and bone. DECT is of great
interest in applications to medical imaging, security inspection and
nondestructive testing. Theoretically, two materials with different linear
attenuation coefficients can be accurately reconstructed using DECT technique.
However, the ability to reconstruct three or more basis materials is clinically
and industrially important. Under the assumption that there are at most three
materials in each pixel, there are a few methods that estimate multiple
material images from DECT measurements by enforcing sum-to-one and a box
constraint ([0 1]) derived from both the volume and mass conservation
assumption. The recently proposed image-domain multi-material decomposition
(MMD) method introduces edge-preserving regularization for each material image
which neglects the relations among material images, and enforced the assumption
that there are at most three materials in each pixel using a time-consuming
loop over all possible material-triplet in each iteration of optimizing its
cost function. We propose a new image-domain MMD method for DECT that considers
the prior information that different material images have common edges and
encourages sparsity of material composition in each pixel using regularization.