  Computed tomography (CT) uses X-ray measurements taken from sensors around
the body to generate tomographic images of the human body. Conventional
reconstruction algorithms can be used if the X-ray data are adequately sampled
and of high quality; however, concerns such as reducing dose to the patient, or
geometric limitations on data acquisition, may result in low quality or
incomplete data. Images reconstructed from these data using conventional
methods are of poor quality, due to noise and other artifacts. The aim of this
study is to train a single neural network to reconstruct high-quality CT images
from noisy or incomplete CT scan data, including low-dose, sparse-view, and
limited-angle scenarios. To accomplish this task, we train a generative
adversarial network (GAN) as a signal prior, to be used in conjunction with the
iterative simultaneous algebraic reconstruction technique (SART) for CT data.
The network includes a self-attention block to model long-range dependencies in
the data. We compare our Self-Attention GAN for CT image reconstruction with
several state-of-the-art approaches, including denoising cycle GAN, CIRCLE GAN,
and a total variation superiorized algorithm. Our approach is shown to have
comparable overall performance to CIRCLE GAN, while outperforming the other two
approaches.
