SPARSE-VIEW CT RECONSTRUCTION BASED ON MOJETTE TRANSFROM USING CONVOLUTIONAL NEURAL NETWORK

摘要

Sparse-view computed tomography (CT) has attracted much attention to reduce the potential radiation risk. However, image reconstruction from insufficient data suffers from distortion. As a discrete form of Radon transform, Mojette transform is exactly invertible even with few samples based on discrete projection and reconstruction lattice. The goal of this paper is to apply Mojette transform to reconstruct images from few views in practice. Due to the fact that its acquisition is incompatible with physical X-ray properties, this paper resorts to deep learning technique to map Radon projection into Mojette domain. Experimental results confirm the effectiveness of the proposed reconstruction scheme based on Mojette transform.