  Recently, a number of approaches to low-dose computed tomography (CT) have
been developed and deployed in commercialized CT scanners. Tube current
reduction is perhaps the most actively explored technology with advanced image
reconstruction algorithms. Sparse data sampling is another viable option to the
low-dose CT, and sparse-view CT has been particularly of interest among the
researchers in CT community. Since analytic image reconstruction algorithms
would lead to severe image artifacts, various iterative algorithms have been
developed for reconstructing images from sparsely view-sampled projection data.
However, iterative algorithms take much longer computation time than the
analytic algorithms, and images are usually prone to different types of image
artifacts that heavily depend on the reconstruction parameters. Interpolation
methods have also been utilized to fill the missing data in the sinogram of
sparse-view CT thus providing synthetically full data for analytic image
reconstruction. In this work, we introduce a deep-neural-network-enabled
sinogram synthesis method for sparse-view CT, and show its outperformance to
the existing interpolation methods and also to the iterative image
reconstruction approach.
