Artifact Reduction for Sparse-View CT Using Deep Learning With Band Patch

Sparse-view computed tomography (CT), an imaging technique that reduces the number of projections, can reduce the total scan duration and radiation dose. However, sparse data sampling causes streak artifacts on images reconstructed with analytical algorithms. In this article, we propose an artifact reduction method for sparse-view CT using deep learning. We developed a lightweight fully convolutional network to estimate a fully sampled sinogram from a sparse-view sinogram by enlargement in the vertical direction. Furthermore, we introduced the band patch, a rectangular region cropped in the vertical direction, as an input image for the network based on the sinogram's characteristics. Comparison experiments using a swine rib dataset of micro-CT scans and a chest dataset of clinical CT scans were conducted to compare the proposed method, improved U-net from a previous study, and the U-net with band patches. The experimental results showed that the proposed method achieved the best performance and the U-net with band patches had the second-best result in terms of accuracy and prediction time. In addition, the reconstructed images of the proposed method suppressed streak artifacts while preserving the object's structural information. We confirmed that the proposed method and band patch are useful for artifact reduction for sparse-view CT.