Domain Progressive 3D Residual Convolution Network to Improve Low Dose CT Imaging

The wide applications of X-ray computed tomography (CT) bring low dose CT (LDCT) into a clinical prerequisite. But reducing radiation exposure in CT often leads to significantly increased noise and artifacts, which might lower the judgement accuracy of radiologists. In this paper, we put forward a domain progressive 3D residual convolution network (DP-ResNet) for LDCT imaging procedure which contains sinogram domain network (SD-net), filtered back projection (FBP) and image domain network (ID-net) three stages. Though both based on the residual network structure, the SD-net and ID-net provide complementary effect on improving the final LDCT quality. Experimental results with both simulated and real projection data show that this domain progressive deep learning network achieves significantly improved performance by combing the network processing in the two domains.