Self-supervised nonlocal spectral similarity-induced material decomposition network for dual-energy CT

Dual-energy computed tomography (DECT) imaging plays an important role in clinical diagnosis applications due to its material decomposition capability. However, in the cases of low-dose DECT imaging and ill-conditioned issue, the direct decomposed material images from DECT images would suffer from severe noise-induced artifacts, leading to low quality and accuracy. In this paper, we propose a self-supervised Nonlocal Spectral Similarity-induced Decomposition Network (NSSD-Net) to produce decomposed material images with high quality and accuracy in the low-dose DECT imaging. Specifically, we first build the model-driven iterative decomposition model and optimize the objective function by the iterative shrinkage-thresholding algorithm (ISTA) with the convolutional neural network. Considering the intrinsic characteristics information (i.e., structural similarity and spectral correlation) underlying DECT images, which can be used as the prior information to improve the accuracy of the decomposed material images, we construct the nonlocal spectral similarity-based cost function by using the prior information and incorporating it into the iterative decomposition network to guarantee stability. The proposed NSSD-Net method was validated and evaluated with real clinical data. Experimental results showed that the presented NSSD-Net method outperforms the other competing methods in terms of noise-induced artifacts reduction and decomposition accuracy.