DDPNet: A Novel Dual-Domain Parallel Network for Low-Dose CT Reconstruction

The low-dose computed tomography (CT) scan is clinically significant to reduce the radiation risk for radiologists and patients, especially in repeative examination. However, it inherently introduce more noise due to the radiation exposure. Nowadays, the existing LDCT reconstruction methods mainly focus on single domain of sinogram and image, or their cascade. But there still has limitations that the insufficient information in single domain, and the accumulation error in cascaded dual-domain. Though dual-domain can provide more information in reconstruction, how to effectively organize dual-domain and make complementary fusion still remain open challenges. Besides, the details inter-pixel in reconstructed LDCT is essential for structure maintenance. We propose a Dual-domain parallel network (DDPNet) for high-quality reconstruction from widely accessible LDCT, which is the first powerful work making parallel optimization between sinogram and image domains to eliminate the accumulation error, and fusing dual-domain reconstructions for complementary. DDPNet is constituted by three special designs: 1) a dual-domain parallel architecture to make joint mutual optimization with interactive information flow; 2) a unified fusion block to complement multi-results and further refine final reconstruction with triple-cross attention; 3) a pair of coupled patch-discriminators to drive the reconstruction towards both realistic anatomic content and accurate inner-details with image-based and inter-pixel gradient-based adversarial constraints, respectively. The extensive experiments validated on public available Mayo dataset show that our DDPNet achieves promising PSNR up to 45.29 dB, SSIM up to 98.24%, and MAE down to 13.54 HU in quantitative evaluations, as well as gains high-quality readable visualizations in qualitative assessments. All of these findings suggest that our method has great clinical potential in CT imaging.