Image-domain material decomposition for single-energy CT images using cascaded network

Dual-energy computer tomography (DECT) has high application prospects in distinguishing and quantifying materials. However, DECT requires higher hardware cost and higher radiation dose than single-energy CT imaging. In this paper, we have developed a cascaded network method to realize DECT imaging through SECT images, then get the basis material decomposition images. Specifically, we design the mapping convolutional neural network and the material decomposition U-Net to realize the mapping of low- to high-energy images and material decomposition, respectively. To verify the feasibility of the proposed method, we extracted 1442 cranial cavity slice images of 5 patients for experiments. The qualitative and quantitative results show that the proposed method can achieve high-quality DECT imaging of single energy data and high-precision basis material decomposition.