Image-domain multile-material decomposition for dual-energy CT via total nuclear norm and L0 norm

Qiaoqiao Ding, Tianye Niu, Xiaoqun Zhang, Yong Long


Published in:Fully3D 2017 Proceedings


Theoretically two materials with different linear attenuation coefficients can be accurately reconstructed using dual-energy CT (DECT) technique. However, the ability to reconstruct three or more basis materials is clinically and industrially important. We propose a new image-domain multi-material decomposition (MMD) method using DECT measurements. The proposed PWLS-TNV-`0 method uses penalized weighted leastsquare (PWLS) reconstruction with three regularization terms. The first term is a total nuclear norm (TNV) that accounts for the image property that basis material images share common or complementary boundaries and each material image is piecewise constant. The second term is a `0 norm that encourages each pixel containing a small subset of material types out of several possible materials. The third term is a characteristic function based on sum-to-one and box constraint derived from the volume and mass conservation assumption. We apply an Alternating Direction Method of Multipliers (ADMM) to optimize the cost function of the PWLS-TNV-`0 method. Our results on simulated digital phantom and clinical data indicate that the proposed PWLS-TNV-`0 method reduces noise and improves accuracy of decomposed material images, compared to a recently proposed image-domain MMD method for DECT.
Qiaoqiao Ding
School of Mathematical Sciences and Institute of Natural Sciences, Shanghai Jiao Tong University, China
Tianye Niu
Sir Run Run Shaw Hospital and Institute of Translational Medicine, Zhejiang University, China
Xiaoqun Zhang
School of Mathematical Sciences and Institute of Natural Sciences, Shanghai Jiao Tong University, China
Yong Long
University of Michigan-Shanghai Jiao Tong University Joint Institute, Shanghai Jiao Tong University, China
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