Research on Material Decomposition of Dual-energy CT Image Based on Iterative Residual Network

Dual energy computed tomography (DECT) plays an important role in the application of advanced imaging due to its material decomposition capability. Image domain decomposition can directly invert CT images through by linear matrix, but the decomposed material images will be seriously affected by noise and artifacts. Although various regularization methods have been proposed to solve this problem, they still face two challenges: tedious parameter adjustment and the loss of image details resulted from over-smoothing. Therefore, in this paper we proposes a dual energy CT image material decomposition algorithm based on iterative residual network. Direct inversion is used as the initial base image, and a stacking two-channel convolutional neural network is used to replace the regularization items in the iterative decomposition model to form a deep iterative decomposition network. This method can realize material decomposition and noise suppression simultaneously. Experimental results show that the iterative residual network proposed in this paper is superior to other comparison methods and can effectively suppress noise and artifacts while maintaining the edge details of the base image.