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Dual energy computed tomography (DECT) imaging plays an important role inadvanced imaging applications due to its material decomposition capability.Image-domain decomposition operates directly on CT images using linear matrixinversion, but the decomposed material images can be severely degraded by noiseand artifacts. This paper proposes a new method dubbed DECT-MULTRA forimage-domain DECT material decomposition that combines conventional penalizedweighted-least squares (PWLS) estimation with regularization based on a mixedunion of learned transforms (MULTRA) model. Our proposed approach pre-learns aunion of common-material sparsifying transforms from patches extracted from allthe basis materials, and a union of cross-material sparsifying transforms frommulti-material patches. The common-material transforms capture the commonproperties among different material images, while the cross-material transformscapture the cross-dependencies. The proposed PWLS formulation is optimizedefficiently by alternating between an image update step and a sparse coding andclustering step, with both of these steps having closed-form solutions. Theeffectiveness of our method is validated with both XCAT phantom and clinicalhead data. The results demonstrate that our proposed method provides superiormaterial image quality and decomposition accuracy compared to other competingmethods.

DECT-MULTRA: Dual-Energy CT Image Decomposition With Learned Mixed Material Models and Efficient Clustering