  The potential huge advantage of spectral computed tomography (CT) is its
capability to provide accuracy material identification and quantitative tissue
information. This can benefit clinical applications, such as brain angiography,
early tumor recognition, etc. To achieve more accurate material components with
higher material image quality, we develop a dictionary learning based
image-domain material decomposition (DLIMD) for spectral CT in this paper.
First, we reconstruct spectral CT image from projections and calculate material
coefficients matrix by selecting uniform regions of basis materials from image
reconstruction results. Second, we employ the direct inversion (DI) method to
obtain initial material decomposition results, and a set of image patches are
extracted from the mode-1 unfolding of normalized material image tensor to
train a united dictionary by the K-SVD technique. Third, the trained dictionary
is employed to explore the similarities from decomposed material images by
constructing the DLIMD model. Fourth, more constraints (i.e., volume
conservation and the bounds of each pixel within material maps) are further
integrated into the model to improve the accuracy of material decomposition.
Finally, both physical phantom and preclinical experiments are employed to
evaluate the performance of the proposed DLIMD in material decomposition
accuracy, material image edge preservation and feature recovery.
