IMD-MTFC: Image-Domain Material Decomposition via Material-Image Tensor Factorization and Clustering for Spectral CT

Spectral computed tomography (CT) provides multispectral X-ray information that can be used for quantitative material-specific imaging compared to the conventional CT. However, the low-count photon rate in a single energy bin may lead to highly noisy measurements with compromised material contrast and accuracy. Moreover, the complicated material decomposition process is an ill-posed inverse problem, which is sensitive to noise. In this work, we develop an image-domain material decomposition method via material-image tensor factorization and clustering (IMD-MTFC) for spectral CT to obtain high-precision material-specific images. Specifically, a set of image patches is extracted from the normalized material-specific image tensors decomposed by the direct inversion (DI). Then, each of them is clustered in a given nonlocal neighboring area to explore the nonlocal self-similarity of material-specific images. Furthermore, the low-rank regularized Kronecker-basis-representation tensor factorization is employed to incorporate the sparsity and redundant correlation across the material-specific images. The split-Bregman is employed to optimize the model by dividing it into several subproblems. The performance of our method is validated with numerically simulated mouse projections, physical phantom, and preclinical experiments. The results confirm that the IMD-MTFC method outperforms other state-of-the-art competing methods.