Sparse-view tomographic reconstruction using residual u-net with attention gates

Abstract

Reconstructing a tomographic image with sparse-view sampling is a major challenge in low-dose computed tomography. Recently, several studies have reported that deep-learning-based methods can reconstruct images of 512 × 512 pixels from 60-view X-ray projections without large artifacts. In this study, a U-Net variant with residual connections and attention gates is proposed for sparse-view computed tomography. A pair of the proposed U-Nets with a loss function based on the structural similarity index measure can be applied to synthesize sparse-view sampling sinograms and denoise reconstructed images. The experimental results indicate the performance of the proposed method is superior to those of other U-Net-based methods for fewer than 60 projection views. Experiments on a public data set of chest tomographic images validated that the proposed method can be used for COVID-19 identification.