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

Abstract

To solve the problem of learning based computed tomography (CT) reconstruction, several reconstruction networks were invented. However, applying neural network to tomographic reconstruction still remains challenging due to unacceptable memory space requirement. In this study, we presents 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 FBP method. The first module of LBRN decouples the relationship of Radon transform between the reconstructed image and the projection data. Therefore, the following module, block back-projection module, can use the block reconstruction strategy. Due to each image block is only connected with part filtered projection data, the network structure is greatly simplified and the parameters of the whole network is 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 (ROI), metal artifacts reduction and 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.

Low-count PET image reconstruction based on truncated inverse radon layer and U-shaped network

“…Currently, there are many successful applications of deep learning in medical image processing, such as lowdose computed tomography (CT) imaging (Chen et al 2017), CT image reconstruction (Ma et al 2022), magnetic resonance imaging (MRI) super resolution (Chen et al 2018), and medical image segmentation (Ronneberger et al 2015). There are also many applications in the field of PET image reconstruction.…”