Dual Domain Closed-loop Learning for Sparse-view CT Reconstruction

Sparse view sampling is one of the effective ways to reduce radiation dose in CT imaging. However, artifacts and noise in sparse-view filtered back projection reconstructed CT images are obvious that should be removed effectively to maintain diagnostic accuracy. In this paper, we propose a novel sparse-view CT reconstruction framework, which integrates the projection-to-image and image-to-projection mappings to build a dual domain closed-loop learning network. For simplicity, the proposed framework is termed a closed-loop learning reconstruction network (CLRrcon). Specifically, the primal mapping (i.e., projection-to-image mapping) contains a projection domain network, a backward projection module, and an image domain network. The dual mapping (i.e., image-to-projection mapping) contains an image domain network and a forward projection module. All modules are trained simultaneously during the network training stage, and only the first mapping is used in the network inference stage. It should be noted that both the inference time and hardware requirements do not increase compared with traditional hybrid domain networks. Experiments on low-dose CT data demonstrate the proposed CLRecon model can obtain promising reconstruction results in terms of edge preservation, texture recovery, and reconstruction accuracy in the sparse-view CT reconstruction task.