Projection-to-image transform frame: a lightweight block reconstruction network for computed tomography

Abstract

Several reconstruction networks have been invented to solve the problem of learning-based computed tomography (CT) reconstruction. However, the application of neural networks to tomographic reconstruction remains challenging due to unacceptable memory space requirements. In this study, we present a novel lightweight block reconstruction network (LBRN), which transforms the reconstruction operator into a deep neural network by unrolling the filter back-projection (FBP) method. Specifically, the proposed network contains two main modules, which respectively correspond to the filter and back-projection of the FBP method. The first module of the LBRN decouples the relationship of the Radon transform between the reconstructed image and the projection data. Therefore, the following module, block back-projection, can use the block reconstruction strategy. Because each image block is only connected with part-filtered projection data, the network structure is greatly simplified and the parameters of the whole network are dramatically reduced. Moreover, this approach is trained end-to-end, working directly from raw projection data, and does not depend on any initial images. Five reconstruction experiments are conducted to evaluate the performance of the proposed LBRN: full angle, low-dose CT, region of interest, metal artifact reduction and a real data experiment. The results of the experiments show that the LBRN can be effectively introduced into the reconstruction process and has outstanding advantages in terms of different reconstruction problems.